最新刊期
卷 ， 期 1 ， 2008
- 摘要：Some bio-physical parameters（e.g.leaf area index,LAI） are often inverted using remote sensing data for its large cover scope,high temporal and spatial resolution.The common way of mapping LAI is through the inversion of physically based canopy-reflectance（CR） models using the optimization methods.The information of remote sensing data is usually not enough for the LAI inversion;furthermore,the inversion problem is ill-posed because of many unknown parameters and the relatively insufficient information in remote sensing data.It is necessary to make suitable inversion strategy（such as which parameter（s） should be inverted） for high accuracy of parameters estimation.We should learn the factors which affect the inversion result in order to design the suitable inversion strategy.Different from the research of parameter sensitivity for suitable inversion strategy,we made progress in the inversion process.For the information of the inversion process,some key points are needed to investigate,such as the factors affecting the parameters estimation,the mutual effect of different parameters in inversion process and so on.In the paper,we investigated the factors which affect the parameter estimation from the inversion process aiming at directing the parameter inversion.One of the accuracy indices for the inversion result is the root mean square error（RMSE）.For the inversion result,the smaller RMSE is,the higher inversion accuracy is.We investigated the formulae of the RMSE based on the physically based canopy-reflectance model.Through mathematical formulae and physical mechanism,we can know that the factors affecting the RMSE consist of canopy reflectance data quality,the sensitivity of parameters and the correlation of the parameter sensitivity.That is to say,as to the sensitivity of parameters,not only the parameter sensitivity but the correlation of the parameter sensitivity the factors affect the parameters inversion accuracy.In other words,the relative sensitivity of the parameter has effect on the parameter inversion.We should make two kinds of progress for high accuracy parameter inversion.One is about the quality of canopy reflectance data.Remote sensing data are often contaminated with noise from various sources,such as radiation calibration,atmosphere correction,geometric registration and some random noises.The other is the sensitivities of the parameters and the correlation of the parameter sensitivity.We can make the suitable inversion strategy based on both the quality of the canopy reflectance data and the parameters sensitivities.The CR model is the SAIL model and the inversion method is the modified least square method in this paper.We validated the factors which affect the LAI inversion accuracy through LAI inversion based on simulated CR data sets.关键词：canopy reflectance model;inversion accuracy;sensitivity analysis
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发布时间：2021-06-10 - 摘要：Semantic inconsistency between geographical classification schemes is believed as the most serious problem for heterogeneous geographical information systems to implement semantic information sharing.Since 1980’s four standard Chinese geographical classification schemes have been constructed,i.e.Classification and Codes for the National Land Information（GB/T13923-1992）,Classification and Codes for the Features 1∶500,1∶1000,1∶2000 Topographic Maps（GB/T14804-1993）,Classification and Codes for the Features 1∶5000,1∶10000,1∶25000,1∶50000,1∶100000 Topographic Maps（GB/T15660-1995）,and Classification and Codes for the Features 1∶500,1∶1000,1∶2000 Topographic Maps for Urban Fundamental Geographical Information System（CJJ100-2004）.Individual special geographical classification schemes are normally constructed for specific geographical information systems.GB/T13923-1992 is a general and basic standard geographical classification scheme.All other geographical classification schemes are suggested to be compatible with GB/T13923-1992.Geographical information systems are usually inconsistent in terms of their content and geographical classification schemes.A single geographical concept might be represented in different ways so that geographical information systems cannot initially implement semantic information sharing.Semantic mapping is considered as a tool to state semantic relations between entities belonging to different classification schemes.It has been proven to most effectively resolve semantic heterogeneity between geographical classification schemes.Although intellectual methods can attain good effects,there are still problems if there are insufficient time,money and experts.This paper firstly describes a method for identification of entities from different geographical classification schemes.Based on semantic relations,parameters for the qualitative and quantitative analysis of their semantic consistency are defined.And then the basic architectures and coding measures of standard geographical classification schemes are compared and analyzed.The results of the quantitative comparison between GB/T 13923-1992 and other standard schemes show that GB/14803-1993 and GB/T 13923-1992 have the best semantic consistency.The results of the quantitative comparison between common-scale and different-scale standard schemes show that GB/14803-1993 and CJJ100-2004 have the worst semantic consistency,but GB/T15660-1995 and GB/14803-1993 can achieve better semantic consistency.Finally,some suggestions are given for construction and modification of Chinese standard geographical classification schemes.For example,standard schemes should define identical main classes and item names,authorized semantic mapping of standard geographical information schemes should be published, and ontologies may be able to solve the problem of semantic inconsistency between photographical geographical information schemes naturally and economically.关键词：geographical classification scheme;semantic analysis;Geographical information system
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发布时间：2021-06-10 - 摘要：Ship wake’s properties（e.g.microwave property,thermal infrared property,acoustic property,underwater optical scattering property of wake bubbles） have been extensively studied to monitor surface ship through the ages. However,there were few researches on above-water spectral features of ship wake,especially features with submerged bubbles.With the increase of the resolution of spaceborne optical sensor,measurement for ship wakes by optical remote sensing is feasible.The physical features and Apparent Optical Properties（AOPs） of ship wakes with bubbles was measured in situ,and how bubbles would affect AOPs of seawater in Case-Ⅱ waters,such as normalized water-leaving radiance and remote sensing reflectance,were analyzed in quantity.By comparing remote sensing reflectance of wake seawater containing bubbles with radiance resolution of ASTER VNIR radiometer,we can find that it is feasible to detect ship wakes containing bubbles by spaceborne optical sensors.In this paper,the main research domestic and abroad was introduced systematically.In accordance with the phenomenon in experiment of optical properties of sea water caused by bubbles,the influence of bubbles in wakes on optical properties caused by varieties of water bodies and environment（e.g.solar altitude angle,wind speed and cloud cover）,and quantificational evaluation of validity of wakes with bubbles based on satellite optical sensor were investigated.These works may provide some theoretical foundations for capturing the information of ship wakes from spaceborne optical sensors.Observations prove that ship wakes with bubbles can change AOPs of seawater in both the magnitude or spectral shape over the visible and infrared wave bands,and the color of water becomes greener because of wakes bubbles.And the bubbles’ concentration and reflectance increased with the distance from the observation position in wake to the target ship geting shorter.During the experiments,we found that the length of target ship wakes with bubbles exceeded 1km,and the width was about 40m.The concentration of bubbles in wakes became higher as the distance between observation point and target ship shortened.Therefore, the reflectance also enhanced.We observed another ship whose speed equaled to the target ship and tonnage was 2000t heavier than the target ship.Although we only took visual observation because of the limited experiment condition,we found that the length and width of its wake with bubbles were greater than that of target ship;therefore,we believe that the screw propeller can generate more bubbles as ship is heavier and faster.And the bubbles in wake are able to last a longer time when the screw propeller is located in deeper water and the time interval of bubbles rising to surface is longer.The changing of AOPs caused by the wake with bubbles will be much more distinct and last a longer time.At the same time,in Case-Ⅱ waters,the variations of seawater AOPs caused by the introduction of bubbles were not very clearly because the high concentration of suspended particles may increase the backscattering.In comparison,the variation of water color affected by ship wakes weakened when water was more turbid.Although the information of wake with bubbles obtained by optical sensor varied with wave bands,the variance of seawater spectrum caused by wake bubbles can be extracted more easily with the improvement of the radiance and spatial resolution of modern optical sensor（the quantification grade of optical sensors carried by QuickBird and IKONOS arrive at 11bits,and the spatial resolution is 0.6m,1m respectively）.Wind speed was less than 10m·s-1 and the sea condition was good when we took experiments.The effect of the roughness of sea surface on experiment did not completely emerge.In order to fully analyze how marine environment affects AOPs of wake seawater with bubbles,more experiments are needed in other wind speed and sea condition.Meanwhile,we need to take more experiments to measure vessels of different size and speed.We wish to summarize the rules of AOPs of wake seawater comparing with background seawater by means of combining satellite remote sensing information with experiment data.Finally,we may achieve our goals of monitoring and identifing ships in large-scale sea area by using spaceborne optical sensor.关键词：ship wake;bubbles;Case-Ⅱ waters;AOPs
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发布时间：2021-06-10 - 摘要：Interferometric Synthetic aperture radar（InSAR） is based on the concept of observing the same scene with two slightly different radar trajectories.Each point of the scene is seen from two different positions along the two radar trajectories,and the differences between those points constitute the interferometric baseline.For quantitative SAR interferometry and differential interferometry applications,e.g.building digital elevation models（DEMs） or monitoring terrain displacement,the baseline must be estimated with a higher accuracy than generally achieved from satellite or airplane trajectories or attitude measurements.Traditional techniques for accurate baseline estimation need a priori information,like orbit data（to be known with a high precision）,fringe frequencies in flat areas,ground control points（GCP） and existing digital elevation models（DEM）.In many cases it is not possible to meet these requirements,i.e.neither orbit data or a digital elevation models（DEM） are available nor Ground Control Points（GCP） or flat terrain are within the scene.In this paper,a method of the InSAR baseline estimation is proposed based on Kalman filter and co-registration parameters of the interferometric SAR images and is not limited to the restrictions mentioned above.The proposed method are free from the following restrictions: 1.ground control points（GCPs）;2.existing digital elevation model（DEM）;3.flat terrain in the scenes;4.independence of orbit data.Moreover,the novel method allows the determination of time varying baseline parameters and is independent of the accurate knowledge of the height of the tie point.So the method can be applied when precise orbit data,ground control points（GCPs） or existing digital elevation models（DEM） are unavailable,or in mountainous regions where flat areas are difficult to be observed in the scene.The proposed method is made up of two steps: obtaining co-registration parameters of the interferometric SAR images and estimating the InSAR baseline based on Kalman filter.In the first step,the co-registration accuracy of the interferometric SAR images is required in 0.1pixels.In the second step,a geometrical relation between the pixel offset in range and the baseline components is derived,and the initial values of the baseline parameters are calculated using the forecasted satellite orbit data.Additional use of the knowledge about the stochastic characteristics of the involved parameters allows the determination of the baseline components by using Kalman filter.In the method a flat Earth approximation was considered,namely considering the terrain height as a constant equalling to the average value of the terrain height in the scene.The proposed method is examined using the ERS-1/2 Tandem data from the European Space Agency（ESA）.The data are imaged in Nanjing test site,which is located in the east of China.The results show that the proposed method can obtain the accuracy of baseline parameters at dm level and make up the limitations of baseline estimation when the accurate orbit data and ground control points can not be obtained.And the errors caused by orbit shift have been reduced.Thus,the method proposed can improve the accuracy of the DEM produced.关键词：Kalman filter;InSAR;baseline estimation;co-registration
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发布时间：2021-06-10 - 摘要：Data update is an important task for GIS.However,complex and inefficient process has been a principal problem that gets in the way of updating GIS data in time during vector data update in GIS,it is necessary to substitute an automatic method for manual method currently.In order to make data update less dependent on arbitrary judgement,auxiliary information should be cited.During the change detection of remote sensing images, GIS data have played an important role as prior knowledge in increasing accuracy and simplifying process.Approaches of change detection and data update has been proposed in recent years.However,those approaches are based on neural network,knowledge base instead on linear vector data.Linear vectors that describe the object region edge in GIS,is usually irregular polygon which is represented as connection of the discrete points on the edge.Hence,it makes sense that the method depending on the attributes of linear vectors above is figured out to update the coordinate values of points on the boundary.A simple and convenient method,which utilizes data in GIS as prior knowledge,is proposed here for vector edge update to solve the problem of complexity and inefficiency.This method involves three-step processes of searching edge: generally locating points,recognizing the convexity and concavity,and detailedly describing the polygonal shape.The points moves within the limited regions that threshold values determines.This method integrates remote sensing images and prior GIS data,as well as replace searching edge curve with locating inflexions to simplify change detection and update process.In addition,the method points out an idea of integration of remote sensing and geographical information system data with the carrier of linear vectors.With the experimental data of Landsat TM remote sensing images and the prior knowledge of vector data saved as shapefile of ArcGIS software,the method is applied to detect and update the edges of Keqin Lake,Dongsheng Reservoir and an area of marsh in Zhalong Wetland in Heilongjiang Province,China.And then update accuracies are tested by the simplified buffer detection algorithm for computing polygonal error based on original buffer detection algorithm,and the polygonal comparability between this presented method and the manual method is calculated and analyzed.In terms of the characteristic of polygon,five factors are adopted in evaluating the polygonal comparability: major direction angle and minor direction angle of minimum exterior rectangle,length and width of minimum exterior rectangle,and polygonal perimeter.Relative error of these five factors are computed,based on which mean value is considered as evaluation of polygonal comparable percentage.Besides,different from original buffer detection algorithm,a simplified buffer detection algorithm is pointed out.Based on the improvement,buffer detection algorithm can be calculated through infinite numbers of points on the polygon that stand for object region edge.Estimations are obtained according to those two criteria above.The results show that the update accuracy of proposed method reach above 80%,a little lower than the result derived from manual method,though good polygonal comparability very close to the result of manual method.In spite of that,the approach presented performs an automatic procedure of vector edge update in GIS.Apparently,this method well simplifies change detection and update process,as well as achieves an update accuracy close to the manual method,and a relatively satisfactory comparability with the manual update method.Besides,the results implies that different experimental objects lead to distinct evaluated results.The simpler the shape of edge is,the better the method is evaluated.Thus,the result from experiment on Keqin Lake is better than Dongsheng Reservoir and marsh,as the contour of Keqin Lake is simpler than that of the other two.Nevertheless,there are still several disadvantages to be improved.This method loses control of contour shape of object so that the movement can not be restricted to be harmonious between adjacent points.Also,the moving range threshold of points are supposed to be settled by researcher,which means that the automatic process is affected by human idea.关键词：remote sensing image;vector edge;prior knowledge;change detection;update
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发布时间：2021-06-10 - 摘要：Image understanding is generally defined as the construction of explicit,meaningful descriptions of the structure and the properties of the 3-dimensional world from 2-dimensional images.A conceptual framework for image understanding is based on Marr’s concept of visual perception as computational process.Marr postulated a hierarchical architecture for vision systems with different intermediate representations and processing levels（low,middle and higher level vision）.According to the description of Marr’s machine vision theory and the characteristics of low resolution remote sensing images,this paper proposes an automatic main-road extraction method,which is based on line segment perceptual grouping and dynamic programming.The method exploits the road model in low resolution remote sensing images at low level and locates the road seeds automatically by line segment perceptual grouping at middle level and then tracks the road seeds to extract the road networks by dynamic programming at high level.Firstly we illustrate the road model in low resolution remote sensing images based on analyzing road characteristics,such as photometric,geometric,topological and contextual characteristics and so on.To increase the precision of road object recognition and to reduce the effects of noise,source images are preprocessed ahead,which include contrast stretching,edge information detection with canny operator and redundant line segments elimination.Edge detection is crucial to line segment perceptual grouping,thus canny operator is applied because of its characteristics of high position precision,single pixel width and low error rate.In the process of line segments elimination,the length and curvature of line segments are considered as the decisive factors to the elimination of redundant line segments.But the directions of the beginning and end line segments are recorded to assist the decision.At middle level,edge line segments are grouped by perceptual grouping technology based on contextual line segments to generate latent road edge line segments.After that,several road seeds are located by computing the latent road edge line segment groups.The relationship of gray value between the regions shaped by the latent road edge line segment and their background is exploited to locate the road seeds thereinto.Then a new road tracking approach using dynamic programming is adopted at high level.The approach introduces the concept of minimized cost route and extends the primitive segment which is formed by direct connection of road seeds to the whole road network in light of minimized cost route.Finally false alarms are eliminated by knowledge inference method,in which inference rules are attained based on the geographic characteristics of road networks in low resolution remote sensing images.We conducted experiments on three datasets of low resolution remote sensing images,which include the Landsat7（B80 band） image with 15m-resolution,the SPOT image of San Diego district with 10m-resolution and the SAR（Synthetic Aperture Radar） image with 12.5m-resolution.Correctness and completeness are introduced to make objective evaluation on the effectiveness of the method.In this way,reference data,which means the road network plotted by observer,should be defined ahead.Experimental results show that our proposed method has high correctness,especially in Landsat7 remote sensing image（98.7%）.Meanwhile the completeness criteria gained from all the source datasets is comparatively high.The lowest value（88.1%） appears in SAR image probably due to the speckle noises.Moreover the followings are proved by the experimental results:（1） the proposed method is effective in lowresolution remote sensing images（high resolution remote sensing images can be sampled to generate its low resolution counterpart）.Especially the images contain some sparse rural roads and intricate city road network;（2） the method is completely automatic and shows better computation efficiency than others,especially compared to semi-automatic road detection methods which need human and computer interaction;（3） the method shows robustness and good performance in remote sensing images such as Landsat7,SPOT and SAR.关键词：road model;perceptual grouping;dynamic programming;road extraction;knowledge inference;image understanding
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发布时间：2021-06-10 -
Comparison of Classification Performance of Full-,Dual- and Single-Polarization SAR Images Using SVM
摘要：Classification is an important process in interpretation of SAR images.In classification,the information,such as amplitude,phase and texture,is used to arrange all pixels in an image into different classes.A classification map shows directly classes of terrains,which is helpful to understand image.Classification methods of SAR images can be divided into supervised and unsupervised.Support vector machine（SVM） based on statistical learning theory,proposed by Vapnik et al.,is an effective supervised classifier.It is used widely in face recognition,hand-writing identification,and automatic target recognition for good classification performance with small training data sets.It has been a new focus in the field of machine learning.Several researchers have tried to use SVM for classifying polarimetric SAR images,and obtained promising results.As an advanced instrument for remote sensing,polarimetric synthetic aperture radar（SAR） has been applied widely in many fields,such as ecology,environmental monitoring,geological exploration,vegetation investigation,and so on.Compared with single-polarization SAR,to what extent dual-polarization and fully polarimetric SARs can improve in classifieation is important.Classification performance of full polarization versus dual and single polarization is compared qualitatively and quantitatively with SVM taken as the classifier in this paper.For fully polarimetric SAR data,six power values,extracted from the covariance matrix,and three eigenvalues,obtained by eigenvalue analysis technique using the coherency matrix,are contained in an input feature vector.For dual-polarization data,there are only three power values and two eigenvalues.And only one power value is used as an input feature for single-polarization data.In order to equilibrate effect of each element in an input feature vector on classification results,all features are normalized.According to the ground truth or a span image,training samples are selected to train SVM to obtain classifier parameters.Lastly,full-,dual-,and single-polarization SAR images are classified by the trained SVM,and the classification accuracy is calculated if the ground truth is available.In the first experiment,an L-band fully polarimetric image of Flevoland,Netherlands,acquired by the NASA/JPL AIRSAR sensor on August 16,1989,is used to analyze quantitatively the classification accuracy of full-,dual-,and singlepolarization SAR data.The results show that the classification accuracy of fully polarimetric SAR is highest,followed by dual-polarization SAR,and it is lowest for single-polarization SAR.For crop application,the accuracy of HH-VV SAR is greater than other two dual-polarization SARs,comparable with fully polarimetric SAR.If fully polarimetric SAR is unavailable,HH-VV SAR is a proper substitute with acceptable performance.Because of stronger depolarization capability,separability of each terrain in HV data is better than that in the other two cases.Consequently,classification accuracy of HV SAR is better than other two single-polarization SARs.For the two co-polarization SARs,performance of HH SAR is better than another.If the co-polarization transmitter and receiver are used,HH is more proper.In the second experiment,an HH-HV dual-polarization image,obtained in China, is used to analyze qualitatively classification performance of dual-and single-polarization SARs.The experimental results show that scattering power of building is badly confused with that of bank and bare soil due to weak depolarization of building.Thus the classification result of HV SAR is worse than HH SAR.Lastly,using detailed results of the above two experiments,classification performance difference of full-,dual-,and single-polarization SARs is explained from the point of view of scattering characteristics of terrains and operational mechanism of the classifier,SVM.关键词：radar polarimetry;synthetic aperture radar（SAR）;classification- 222
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发布时间：2021-06-10 - 摘要：Distribution and characteristics of global total column ozone（60°N—60°S）data retrieved from the Atmospheric Infrared Sounder（AIRS）,the TIROS Operational Vertical Sounder（TOVS） and Total Ozone Mapping Spectrometer（TOMS） during 2003—2005 are investigated and compared in this study,showing that the total column ozone in North hemisphere has an evident seasonal variation with a maximum of 322.25DU in spring and a minimum of 277.83DU in autumn,decreasing by 45DU.Whereas it is unapparent in South hemisphere.The global total column ozone is latitude-dependent with about 250—270DU at lower latitude and 294/279DU at North hemisphere/South Hemisphere higher latitude.Comparison among three datasets indicates the global mean total column ozone retrieved from the AIRS is larger by 3—5DU than those from TOVS and TOMS,and significant abnormality appears in Antarctic continent and deserts.A comparison of satellite ozone data with ground-based data shows that TOMS retrieved value is lower than ground-based measurements.Further evidence depicts that AIRS/TOMS retrieved values have the moderately good agreement and relationship with the ground-based ozone data.关键词：total column ozone;remote sensing;AIRS;TOMS;TOVS
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发布时间：2021-06-10 - 摘要：High spatial resolution remote sensing images represent the surface of the earth in detail.As spatial resolution increases,spectral variability within the land cover units becomes complex in high spatial resolution remote sensing images,which makes traditional remote sensing image-processing methods on pixel basis such as ISODATA not suitable.Image segmentation that takes spatial information of image into account provides an alternative solution to this problem,and becomes a hot spot in the processing of high spatial resolution remote sensing image nowadays.Temporal Independent Pulse-Coupled Neural Network（TI-PCNN for short） is an improved PCNN,which is a useful biologically inspired image-processing algorithm. It has two properties including a neuron which has the ability to capture neighboring neurons in similar states and regions of neurons which are not connecting with each other,no matter in which states they are,have different pulsing time.These properties of the TI-PCNN ease difficulties of optimal parameters selection process commonly encountered in the usage of traditional PCNN,and make it a useful new tool in non-remote sensing image segmentation.However,due to its heavy computational cost and over-segmentation of objects within the range of low intensity,the original TI-PCNN method is ineffective at segmenting high spatial resolution remote sensing image.By taking account of spatial and spectral characteristics of high spatial resolution remote sensing image,this paper studies the function of parameters in the TI-PCNN and proposes a segmentation method based on the TI-PCNN.A subset of aerial images with spatial resolution of 0.3m is used for experiment and analysis.Segmented result is compared with that of current TI-PCNN method and ISODATA.Result shows that our method can reduce variability within the land cover units to a large extent while maintaining geometric structure in the image.It provides a great potential in high spatial resolution remote sensing image segmentation.关键词：high spatial resolution remote sensing image;segmentation;temporal independent pulse coupled neuron network
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发布时间：2021-06-10 - 摘要：In order to use multicast to transfer file crossovers network segments in WebGIS system,every gateway has to open IGMP protocol,which unavoidably leads to hidden security trouble such as network storm,With using TCP to transfer file,server has to send data to clients in turn,which decreases efficiency and engrosses more band width.To solve this problem,MAP（multicast agent protocol） was established to transfer data.The MAP dynamically selected one multicast agent in every subnet.When an agent received data from server via TCP protocol,it multicast files into local subnet without opening IGMP protocol.By this way multicast could play such advantages,without opening IGMP router protocol.The key steps are as follows:Firstly,every client joins the p2p network,and gets the newest resources from their friends to make resource simultaneously.In the next place,client looks up the multicast agent and takes part in the agent election.If wins,the agent connects the server to get the newest resource.And then,the server of WebGIS transfers the resource to multicast agent in turn by TCP protocol.The agent multicasts the resource in local segment.At last,if there is losing package,it will be compensated by the p2p network.Then the update task of all the clients is finished quickly.The load of the multicast was greater than common nodes.When agent finded that there was one client whose performance was better or quited the system normally, it demised the privilege of agent to more suitable node.The parameters of performance included the value of CPU,the size of memory and the available space of hard disk.When an agent quited abnormally,its connected nodes would receive the FDCLOSE message,then these nodes would launch the campaign of multicast election in local segment.However,there was one special situation that agent and its connected nodes quited almost at the same time abnormally,if that happened,the campaign couldn’t be executed.This problem can be solved in this way that every node who sent node to quit sended a message to search agent in p2p network.If failed,the node launches the campaign of multicast election activity.Due to multicast unreliability,some clients would lose the packet or quit abnormally or when it logined in the system the agent began to translate data.All of these situation would lead some clients to lack packets.Consequently in every subnet segment p2p nework was constructed to compensate packet,which lightened the burden of multicast proxy greatly.After logined in system,each client sent message to local segment by multicast to look up friend.When the clients in the same local segment received this message,they checked the number of their friend and then they would connect them if the number was less than three,otherwise reject this message.And this client only received three relative request.For this client,if the waiting time was out,it meaned that it was the first node in local segment,then it was the multicast agent.It should connect the WebGIS server immediately.In this way,the local p2p network was constructed.The shape of the network was hexagon.Every peer had three friends and there only was one supernode who managed the p2p network.Its task was to calculate information and update data and so on.Generally that was multicast.In order to increase the server’s load and in time accept client’s connection,completion port was used to manage socket connection between server and client.To improve the transfer efficiency,thread pool was adopted to response toclients’ on-line request and memory pool was used to manage resource which was allocated to restore clients’ information.We compare MAP with TCP by abundant and exact experiments.The results shows we find that there is a linear relation between the number of segment and the time of transfer in MAP protocol.It has nothing to do with the number of clients in subnet,which demonstrates that MAP exceeds TCP greatly.Additionally,metadata was used to converse among server、 multicast proxy andcommon clients,which can support to transfer files from broken dot.关键词：WebGIS;network topology;multicast agent;P2P;completion port;metadata
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发布时间：2021-06-10 - 摘要：An effective feature fusion strategy applied in barracks detection is proposed,which takes advantage of the complementary target features in Synthetic Aperture Radar（SAR） and optical satellite images.With more details,it is easier to detect barracks in optical satellite images.However,extracted features in optical satellite images may be unreliable because of the complex background.Thus feature fusion between SAR and optical satellite images is needed. Traditional fusion detection methods are commonly using image registration to set up mapping of the same target in multi-source satellite images.However,due to side-looking character of SAR imaging,the hypsography may cause distortion and precise rectification of the distortion will be very complex.What is more, reference control points are different to select because of the different imaging methods,and co-registration between SAR and optical satellite images may be not perfect.Thus,in this paper we tried to avoid precise rectification and co-registration.The proposed method firstly locates barracks in optical satellite images and cuts the SAR sub images of the same location by adopting the geodetic coordinates information of images.It then independently extracts the feature information of detection results in both optical satellite images and SAR sub images and makes the extracted areas associated according to the redundant feature information.Also a verification method is present to make sure the association is correct.Finally,all the features are fused to get a more accurate and integral description of targets.Results obtained prove that this method can fully use the information of multi-source satellite images,reduce detection mistakes and improve the accuracy of targets features.关键词：multi-source satellite images;fusion detection;feature association;association verification
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发布时间：2021-06-10 - 摘要：One of the key issues of land use/cover change detection using remote sensing images is the threshold determination.This paper introduces the histogram approximation method based on Expectation Maximization（EM） algorithm and Bayes Information Criterion（BIC） into unsupervised change detection.EM algorithm is an iterative algorithm that has many advantages in estimating the statistical values.BIC is always used for evaluating a statistical model on the aspects of accuracy and complexity."Difference image" is acquired by applying the change vector analysis（CVA） technique,which can magnify the difference between the two-temporal images.The probability distribution function（PDF） of its histogram can be modeled as a mixture of M Gaussian distributions.Different values of M will get different models.The best one will make the value of BIC minimum.According to this criterion,the statistical values of the mixture Gaussian distributions can be estimated using the EM algorithm and BIC.Then the threshold of the change detection will be obtained by finding the intersection point of two neighbor Gaussian distributions.M Gaussian distributions will gain M points that are the M thresholds.M is regarded as the number of the changed types.The estimated values including means and variations and the prior distributions of every Gaussian distribution have definite physical meanings.The means indicate the values of images of those changed types based on which difference image can be classified quickly.The variations illustrate the difference in one changed types,and the percentage of every change types can be given by the prior distributions of every Gaussian distribution.The traditional methods of change detection by remote sensing based on EM algorithm is often assuming the difference image containing two types of pixels.Which are changed pixels and unchanged pixels.But when there are more than one changed types and the difference images’ histogram becomes complex,this method is proved not accurate.To compare these two methods and validate this method,this paper chose the area around the Miyun Reservoir as the experiment area.There are more than one types changed including water,bare land and vegetation and so on,so this area is representative for change detection study.The experiment data is 2001 TM image and 2004 ETM+ image.The difference image’s histogram is modeled by 4 Gaussian distribution,according to the models the difference image is classified 4 types.Then the difference image is processed by traditional method of change detection based on EM algorithm.The entropy is introduced to evaluate the two experimental results,which is usually used to evaluate the uncertainty of one pixel belonging to one classification.Its advantage is that it can make the pixels’ uncertainty visible in the image.Results show that the histogram approximation based on EM and BIC method is credible and effective in change detection from the remote sensing images,especially when the changed types are more complex.关键词：threshold determination;mixed Gaussian distribution;EM;BIC;histogram approximation
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发布时间：2021-06-10 - 摘要：Vegetation phenology,the study of recurring vegetation cycles and their connection to climate,is an important variable in a wide variety of earth and atmospheric science applications.Vegetation phenology is also an integraph of global changes and a comprehensive indicator of landscape and environment changes,and the studies on its response to global environment changes have become a focus of global changes field.Vegetation phenology detection methods based on remote sensing overcome conventional ground observation’s shortcomings,such as limited observation sites and missing data,and realize the spatial scale transition of observation methods from points to coverage.Remote sensing technology greatly promotes the study on vegetation ecosystem response to climate changes at regional,continental,even global scales.In order to keep consistent with the character of remote sensing-based vegetation phenology detection,the paper uses "vegetation greenness period" to replace "vegetation growing season",and chose leaf unfolding and leaf coloration of local plant communities as indicator events to show the start and end of vegetation greenness period.Then,based on NOAA/AVHRR dataset,meteorological data,ground phenology observation data,and so on,the paper builds a remote sensingbased vegetation greenness period detection model,namely,Logistic fitting model on cumulative frequency of NDVI to determine the beginning date of greenness period（BGP） in spring and the end date of greenness period（EGP） in autumn of China since 1982.BGP and EGP are utilized to reflect the leaf-unfolding stage and leaf-coloring stage of the terrestrial vegetation,respectively.The computed results indicate that BGP appeared to delay and EGP have an advance trend from south to north.Finally,through comparing the results of the model with the 9 ground observation sites and other remote sensing-based detection models,it is found that BGP and EGP computed by the model have differences of 9—21 days and 0—13 days,respectively,with the ground observation in Mudanjiang,Huhehot,Beijing,Luoyang and Xi’an observation sites.The model is more precise than the other remote sensing-based detection models and the annual BGP and EGP fluctuations are comparatively small.It is obvious that BGP and EGP estimated by the model are reliable in the north temperate regions.In Tunxi,Guiyang,Renshou and Guangzhou observation sites,the differences of BGP and EGP with the ground observation are more obvious than those in Mudanjiang,Huhehot,Beijing,Luoyang and Xi’an observation sites in spite of any remote sensing-based detection model.Tunxi,Guiyang, Renshou and Guangzhou observation sites locate in the southern subtropical evergreen region.The vegetation has no obvious and consistent leaf-unfolding stage and leaf-coloring stage.However,obvious BGP and EGP can be computed by remote sensing-based detection models,which are mainly related to continuously overcast,rainy and foggy days during the rainy season in the sites.Therefore,BGP and EGP estimated by the model are not the real start and end of the vegetation growing season,but reflect vegetation’s response to regional climate changes.In a word,compared with other remote sensing-based detection model,the logistic fitting model on cumulative frequency of NDVI in China can be characterized in three ways.（1） NDVI data needn’t be exceedingly smoothed,which can remain more temporal details;（2） Logistic model only includes three fitting parameter.So,computing process is relatively simple;（3） Multi-model of NDVI arisen from multiple growth cycles（e.g.,double or triple-crop agriculture,semiarid systems with multiple rainy seasons,etc.） is considered.BGP and EGP can be straightly determined by fitting the cumulative frequency of NDVI.The logistic fitting model on cumulative frequency of NDVI is more suitable for China than the other remote sensing-based detection models and can be applied to different spatial scales.关键词：vegetation phenology;remote sensing-based detection;vegetation greenness period;NDVI
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发布时间：2021-06-10 - 摘要：Airborne laser scanning for altimetry is a new technology,which has been developed during last two decades.To derive topography from laser scanned data,off-ground elements like forest and buildings,which are treated as noise,need to be filtered out.This paper,in addition to the review of recent studies on derivation of topography from laser scanned data,is intended to develop a new algorithm by using average filter for forest areas.By iterative calculation,the surface approached the ground progressively by cutting off convexes above the reference surface.In particular,by introducing topographic factor to transform the surface model to a presumed flat terrain,over filtering or less filtering was significantly reduced.The accuracy（RMS） of the derived DTM was 0.4—0.5m.Data was acquired by Laser Bird,which is a laser radar system produced by Optech Ltd.A digital surface model（DSM） was created with all original data,from which the terrain model was constructed by extracting signals reflected from the ground.The basic procedure is as follows:（1） Creating surface model.With all random foot print data,Delaunay triangle was adopted to create a three-dimensional surface model DSM0.（2） Displaying shaded relief.The interim-results were manually monitored with a shaded relief image to determine whether to continue or stop filtering.（3） Filtering.A smoothed surface model was created by average filter,which is called temporal surface model（DSMt）.Regardless the feature of topography,the size of filter window was set stable with 5m×5m.（4） Approaching the ground.By comparing DSMt with a threshold,smaller values were selected for DSMt modification,and the surface gradually came to the ground.（5） Reiterative computing.Above computations were reiteratively operated,which were ended up when either of following conditions was met,i) the difference between DSMt and threshold reaches the specified value,or ii) reiteration reaches 150 times.（6） Computing the reference surface.Complexity of topography was taken into account.A reference surface model（DSMr）,which is similar to the real one in form,was introduced for computing.DSMr was created by using the filter with a larger window.Considering the relationship between DSM0 and DSMr,the rough surface was transformed to a plain one（DSMp）,which was then filtered,and the topography was restored after calculation.（7） Classificating the data.With a threshold ΔH,signals from canopy or ground were separated by comparing with the original LP data.Points with LP-DSMt≤ΔH were considered as ground,which were used for DTM construction.（8） Constructing terrain model.Foot prints on ground were extracted from the original LP data to create digital terrain model.（9） Testing the accuracy.The elevations of 16 points in the study area were manually measured,which were used for accuracy evaluation.关键词：digital elevation model;filtering;forest;laser scanner;signal classification;topographic survey
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发布时间：2021-06-10 - 摘要：The land subsidence of Tianjin area,mainly induced by withdrawal of ground water,has been measured by leveling measurements for many years.Although the monitoring results of leveling technique are reliable,the sparse leveling data grid in the area and the long time span on a single bench mark prevents us from understanding the process of deformation.Spaceborne differential radar interferometry（DInSAR） has been proven a remarkable potential for mapping ground deformation phenomena over tens-of-kilometers-wide areas with centimeter-scale accuracy on a more dense space grid and time series than leveling and GPS technique.As well known,geometrical and temporal decorrelation is an important factor that prevent DInSAR from being an operational tool for displacement monitoring.Moreover,atmospheric inhomogeneities produce an atmospheric phase screen（APS） on every SAR image,which can contaminate the results of the deformation monitoring.Interferogram stacking is a technique to improve the relative accuracy of SAR interferometric surface displacement mapping based on a combination of multiple interferograms.Under the assumption of statistical independence of the atmospheric distortions,the displacement terms add up linearly whereas the error term increases only with the square root of the number of pairs considered.Using ASAR images and the approach of interferogram stacking,the subsidence phenomena of Tianjin area has been mapped.9 ENVISAT ASAR images covering the period from October 2003 to August 2004 have been selected to retrieve the process of subsidence in Tianjin area.To reduce the geometric decorrelation and topographic errors,13 pairs with perpendicular baselines minor than 300 meters are chosen from the possible combinations.In the process of removing topographic component from the inerferometric images,SRTM DEM data are used.Since the orbit data of ENVISAT have been referenced to the WGS84 ellipsoid,the SRTM DEM height values the EGM96 geoid as the reference choose.The geoid height in the area has been compensated.Among the 13 differential interferograms,only one of them is believed to be severely affected by the atmospheric artifacts,the others show almost the same deformation phase model over the work area.Since it is difficult to discriminate displacement phase contributions from the atmospheric signature only by using individual interferogram,the approach of interferogram stacking is used.5 unwrapped phase images are summed and the time span is 350 days.Before phase unwrapped,the coherence values of pixels in all the interferograms are taken into consideration while coherent targets are selected.For the problem at hand,where in a large area high coherence urban patches are surrounded by vegetation-covered field with low coherence,the method of phase unwrapping firstly triangulates the unmasked pixels and then unwraps the phases based on the minimum cost flow algorithm with the averaged coherence map as a weight file.The geocoded subsidence map of test area during 90 days shows the distribution and the relative deformation value of the displacement field.Compared with previous measurements from DInSAR and in-situ GPS,the subsidence cones characterized by the method are reliable.But the temporal decorrelation and the atmospheric distortion are not completely overcome,and the deformation estimations derived from this method still need validation and correction by in-situ measurements mainly observed in Tianjin urban area.In the future work,with more ENVISAT ASAR data acquired,we plan to apply the PS InSAR and derived methods to exploit the data with long spatial and temporal baseline as much as possible.关键词：DInSAR;subsidence;coherent targets;interferogram stacking;Tianjin
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发布时间：2021-06-10 - 摘要：In order to estimate vegetation index precisely,the soil lines of different soil types must be identified.However,it is difficult to obtain the soil line parameters,because there are many influencing factors.With the laboratory soil hyperspectral reflectance studied,the main factors influencing the soil line were determined.The laboratory soil hyperspectral reflectance was used to calculate soil line parameters,which were introduced to compute the vegetation indices related to soil line.The correlation between the indices and soybean chlorophyll a or leaf area index（LAI） was compared with that between normalized difference vegetation index（NDVI） and chlorophyll a or LAI,then the feasibility of the soil line computing method was analyzed.The results were as follows: the main factors affecting soil line parameters were not the band width but the soil class,the latitude,organic matter,mineral composition,and straw residues.Soil classes,with different physical and chemical properties,show significant differences in spectral characteristics,especially the reflectance shape at red and infrared bands.The slope of soil line with low soil albedo was not always smaller than that with higher soil albedo.What’s more,latitude also influenced the soil line parameters because of the regular distribution of soil parent material and other soil properties.The effect of organic matter on soil reflectance in visible and infrared bands（620—810nm） is much stronger than other spectral regions,the reflectance curve at visible and infrared domain is concave,so the slope of soil line increases with organic matter content.What’s more the effect of organic matter on soil line parameters may be stronger than soil class.Based on soil line,the soil vegetation indices partly eliminate the soil background noises,and the correlation between the indices and soybean chlorophyll a or LAI is more significant than that between NDVI and chlorophyll a or LAI,which indicates that the soil line parameter deriving method is feasible and appropriate for the vegetation indices based on soil line.In order to enhance the monitoring precision of vegetation indices,the soil line parameters should be calculated based on the spatial difference of the main factors influencing the parameters.However,the results of the study is based on ground crop spectral reflectance data and obtained in the laboratory,as a result,the influence of atmosphere is not completely considered.Therefore,when the result is used in remote sensing image data,the influence of atmosphere on soil and crop reflectance must be analyzed.关键词：soil line;hyperspectral;reflectance;vegetation index;quantitative remote sensing
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发布时间：2021-06-10 - 摘要：The downwelling diffuse attenuation coefficient（Kd） indicates the information of water quality.It is also key parameter of bio-optical model and attracting more and more attentions.Based on the data of light field under water in Meiliang bay of Taihu lake,the changes of Kd with water depth is analyzed.The results show that,Kd takes on significant fluctuation with the depth above 60cm,which is caused by the surface waves of water.But the fluctuation with depth weakens,and approximately levels off,even showing the characteristic of asymptotic light fields below 60cm,because the effect of scattering is gradually significant.In the region of 0—60cm,there is significant linear correlation between the degree of fluctuation of downwelling diffuse attenuation coefficient and the absorption of water,namely,the absorption of water strengthens the effect of surface wave on the light fields in water.The Kd of shortwave takes on evident fluctuation with the depth compared with that of longwave,because The detritus and CDOM in water significantly absorbs the radiation of shortwave,lowers diffuse light,and intensifies the effect of focusing or defocusing of surface waves.关键词：downwelling diffuse attenuation coefficient;depth;scatter;absorb
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发布时间：2021-06-10 - 摘要：The dry-hot valley is a special landscape in the mountainous area of south-west China.It is found that the typical dry-hot valleys were mainly occupied by slope non-irrigated field,grass land,bare and semi-bare land below 1650m,topographic shadows and seasonal leave unused land with the value of slope less than 0.5° when the altitude is below 1350m.So it is not enough to identify the dry-hot areas just by elevation of 1100—1350m（the traditional method）.Because it is influenced not only by the topographic characteristics related to elevation,aspect and slope,but also by human activities.In this paper,land use/cover mapping method,brightness temperature threshold method and dry-hot index method were used to identify the typical dry-hot valleys in Yuanmou,Yunnan province,China.Landsat ETM+ data acquired on 23rd November,1999 were selected for the mapping of typical dry-hot valleys.For the land use/cover mapping method,more works were involved including mapping different land use/cover types and identifying topographic shadows.And then the brightness temperature threshold method was proposed and employed,for which the determination of brightness temperature threshold was difficult and the identification of topographic shadows was also involved to eliminate the influence of topographic shadows on brightness temperature.To solue this problem,a multi-spectral operation method was proposed.It is not enough for brightness temperature threshold method to represent the characteristics of dry-hot landscapes,so a dry-hot index method（RTVI,ratio of temperature and NDVI）was proposed.In the mapping of typical dry-hot valleys by RTVI,bare land between 1350m and 1650m was eliminated from the typical dry-hot valleys,which should be included by dry-hot landscapes.So normalized difference bareness index（NDBaI） was used to rectify the mapping results.The identification results were compared with landscape ecological method.The agreements between the results of land use/cover mapping and RTVI suggested that the proposed dry-hot index method in this paper was feasible and easily performed.And then the dry-hot landscape characteristics were analyzed in different vertical zones.The analysis of dry-hot landscape in different vertical zones show that the dry-hot landscape obviously exceeds the upper limit of traditional elevation（i.e.,1350m）,and the values of NP and PD in the elevation of 1350—1650m（6732 and 0.5117） were much greater than those below the elevation of 1350m（1873 and 0.1424）,and the value of LPI in the elevation of 1350—1650m（0.3757） was much smaller than that below the elevation of 1350m（2.57）.Those values indicated that the exceeded patches were fragmentized.The mapping results showed that the exceeded patches were mosaic distributed with water and vegetation patches,which suggested that the recovery of original landscape in the elevation of 1350—1650m was easier than the area in the elevation of 1100—1350m.关键词：Yuanmou;dry-hot valley;multi-spectral operation;dry-hot index（Ratio of temperature and NDVI）;thermal infrared
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发布时间：2021-06-10 - 摘要：In this study,vegetation indices（VI） were firstly calculated from the simulated band reflectance spectra of NOAA11-AVHRR,AQUA-MODIS and LANDSAT5-TM using rice canopy hyperspectral reflectance spectra,which were acquired in 2002 and 2004.Regression models for estimating LAI were fitted by that of 2002 and validated by the VIs data sets of 2004.Estimating accuracies between models were compared and analysis of VIs’ sensitivity to changes in LAI was performed.Due to differences in spectral response function,the simulated red band reflectance of AVHRR was higher,whereas near infrared reflectance was lower than that of TM or MODIS for identical rice canopy.Compared with the models of VIs calculated from the red band reflectances,LAI showed closer linear relationship in models of ratio VIs from the red-edge or green band reflectances and closer exponential relationship in models of normalized VIs from the green band reflectances.Among others,the model using Red-edge Ratio Vegetation Index（Red-edge RVI） showed the highest estimating accuracy not only for calibration but also the highest one for external validation,furthermore the decrease in estimating accuracy between calibration and external validation was lowest.The models of Green Normalized Difference Vegetation Index（GNDVI） and Green Ratio Vegetation Index（GRVI） held the second highest position of estimating accuracies for external validation,the models of normalized difference vegetation index（NDVI） and enhanced vegetation index（EVI） were the third highest and those of RVI were the worst.Although AVHRR bands have broader ranges of spectral response than TM and MODIS,their NDVI and RVI models exhibited higher estimating accuracies for rice LAI in this study.The sensitivities of all indices decreased with the increase of LAI values,but difference of sensitivities between normalized VIs and ratio VIs was significant.The normalized VIs exhibited much higher sensitivities with rapidly decreased rate than the ratio VIs at LA1 values of less than 1.2,while the ratio VIs did higher sensitivities than the normalized VIs at LA1 values of more than 1.2.Red-edge RVI and GRVI exhibited not only higher linearity relationship with LAI but also higher sensitivities at LAI values of more than 2.8.When comparison of model fitness between linear and nonlinear regression models is made,it should be done not by directly comparing the coefficients of determination of regression models but the coefficients of determination of linearity relationship between the predicted and the observed.关键词：vegetation indices;leaf area index;accuracy of regression model;sensitivity;rice
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发布时间：2021-06-10 - 摘要：During the past decade,there have been dramatic improvements in LiDAR technology and it has been used sucessfully in forestry.To evaluate LiDAR height estimation performance in China,the Culaishan Forest Farm was selected as test site,which is located in Taian,Shandong province.The airborne discrete return LiDAR data were collected on May 13,2005,using Riegl LMS-Q280i laser scanner together with IMU and DGPS.The relative flight height is 800m and the scan angle is 30 degrees.The Laser beam divergence is 0.5mrad.Only first returns were recorded.The LiDAR point density is about 0.35 point per square meter.The tin filter was used to classify LiDAR point cloud data.The ground point dataset,vegetation point dataset and elevation normalized vegetation point dataset were generated for further analysis.The airphotos acquired simultaneously were used as reference during filter parameters selection.Then grid local maximum,upper-quartile and average height were calculated from elevation normalized vegetation point and compared with ground measurements collected in April,2006. The results demonstrated that it is feasible to use airborne LiDAR technology to estimate forest height.As only low density data was available at the test site,quartiles allowed for good tree height estimation in the low LiDAR point density case.The accuracies from all plots were higher than 87% and the total average accuracy was 90.59%.The accuracy of deciduous forest stand was higher than that of coniferous forest stand as deciduous had more flat shape canopy and higher reflectance.This accuracy met the tree height accuracy requirement of general cash forest and ecological forest in the Forest Management Inventory of China. The accuracy was a little bit lower than the requirement of national cash forest（5%） and more validation should be done for this kind of forests.As for this experiment site,the accuracy showed good fulfillment with the surveying requirement of ecological forest in forest parks.关键词：LiDAR;point cloud data;upper-quartile;tree height
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发布时间：2021-06-10 - 摘要：Previous reports demonstrated that data from air-and spaceborne sensors are appropriate for delineation of soil patterns.It is expected to obtain soil information conveniently in this way when conventional survey is restricted.This study was conducted to assess the application of integrated terrain and ASTER and SPOT databases for soil pattern delineation.The main objective was to test the effect of the remotely sensed data and terrain descriptor on the classification results in arid area,with a study area of Ebnur Lake,in Xinjiang,China.At first,the basic data were collected,including sensing multi-spectral images of nine basic ASTER channels,four SPOT channels and DEM data.The basic dataset was used to extract the classifying characteristics,including principal components bands,soil brightness index and the green vegetation index of tassled cap transformation,NDVI,NDMI,NDWI,texture characteristics,terrain derivatives,and so on.These characteristics constituted the classifying database for this paper.Then,with the field-derived data and supplementary soil map of genesis taxonomy achieved by the 2nd soil survey in China,we analyzed the relations between soil and landscape characteristics of remote sensing information.According to J-M distances among soil subclasses based on the classifying database,some subclasses were adjusted to adapt to classification.8 in 17 subclasses had been merged or abandoned,and 9 new classes were remained at last.The merged subclasses mainly included the same landscapes of farmland,covered by the crop.Then,a soil classification system suitable for remote sensing was established in the study area,including salinized and desertified soil mainly.Lastly,a training dataset was collected based on the soil classification system,and maximum likelihood classifier（MLC） method was performed to the classifying database.The classification error was evaluated by the confusion matrixes.It indicated that the method were helpful to the soil classification in arid area,and the overall accuracy of about 90% was satisfying.关键词：remote sensing;soil classification;arid area
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发布时间：2021-06-10 - 摘要：The measurement of crop planting area is essential to yield estimation by remote sensing.The area measuring accuracy directly affects the reliability and applicability of the results.The application of multi-scale remotely sensed data is the inevitable trend.The selection of different scale remotely sensed data sources directly affects the accuracy of measurement of crop planting area.It is necessary and valuable to study the effect of scale on crop planting area measurement,as well as the relationship of qualitative and quantitative research.Therefore,this paper used SPOT5 data and analyzed the scale change effect on the accuracy of measurement by remote sensing.Spatial statistic method and manifold accuracy evaluation indices were respectively used to analyze them with various spatial resolution,spatial extent and crop percentage.And the study results indicate:（1） with the decrease of the spatial resolution,the average regional accuracy will decrease and the errors of crop planting area measurement will increase due to the augmentation of mixed pixels;（2） with the enlargement of the spatial extent of the measuring region,the average regional accuracy increases because the proportions error can be counteracted reciprocally at a certain extent;（3） the percentage of crop can exert a great influence on the choice of data with different spatial resolution used in the measurement of crop planting area.When the percentage of the crop is above 30%,the mean region accuracy at different resolution is basically above 90%.When the percentage reaches 40%—50%,the differences of the mean regional accuracies at different resolution reach the least value and is almost above 93%.With the percentage unceasingly increasing,the mean region accuracies at lower resolution（30m,50m,70m,and 90m） almost keep unchanged.When the percentage of 30m resolution is above 10%,the mean region accuracy can stabilize over 95% even if the measuring extent is small.The results of this paper provide academic and experimental reference to resolve the problem of data selection and accuracy pledge in operational crop planting area measurement by remote sensing.关键词：scale;crop;regional accuracy;pixel accuracy;resolution;spatial extent;percent
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发布时间：2021-06-10 - 摘要：Impervious surfaces are usually defined as anthropogenic features through which water cannot infiltrate the soil,typically including buildings,roads,parking lots,sidewalks,and other built surfaces.Due to the close correlation with the spatial extent and intensity of urban development,impervious surface has been recently recognized as a key environmental indicator in assessing urban ecological condition and utilized to investigate urban hydrology,urban climate,land use planning and resource management.Over the past decades,extensive researches have been carried out to map impervious surfaces cost-effectively with satellite remote sensing data,especially multi-spectral optical images（e.g.Landsat TM/ETM+ and SPOT imagery）.However,an accurate representation of impervious surface is still a challenge using these middle-resolution optical remote sensing data,because of the complexity of urban/suburban landscapes and the spectral confusions among different land-use/cover types（such as between barren land and parking lots）.The spectral confusion as well as the presence of mixed pixel may result in an overestimation of impervious surface distribution in the less-developed areas,but underestimation in the well-developed areas.Unlike optical images that represent the spectral reflectivity of the targets illuminated by sun light,synthetic aperture radar（SAR） images are very sensitive to the surface roughness,shape,structure,dielectric properties of the illuminated ground objects and can provide information complementary to optical data.And recent advantages have shown that the use of SAR interferometric coherence products（e.g.coherence,ratio intensity and average intensity） can improve the capability to distinguish natural land covers and built-up objects in urban area.These SAR feature images generally are derived from SAR interferometry（InSAR） pairs acquired with relatively small perpendicular baseline and long interval.The main aim of this paper is to explore potentials of the use of InSAR data in mapping impervious surface cover.In this study,a CART-based approach is developed to quantify urban impervious surfaces as a continuous variable（impervious surface percentage,ISP） by using multi-source remote sensing datasets in which aerial photograph with high spatial resolution was used as training/test data of ISP estimation and medium-resolution imagery（e.g.InSAR feature images） to extrapolate imperviousness over large spatial areas.This approach produces a rule-based model for prediction of ISP based on training data,and can allow impervious surface cover to be mapped at sub-pixel level of medium remote sensing data.It involves the following steps:（1） development of training/test data using 33cm-resolution digital aerial CIR photography;（2） design of predictive variables,establishment and assessment of final regression tree modeling;（3） spatial extendibility of ISP prediction modeling with InSAR feature images,and（4） accuracy assessment of ISP mapping.A case study was conducted for impervious surface mapping in Hong Kong by using an ERS-2 InSAR pair acquired in year 1998,which was separated by a 35-day interval and had 13m perpendicular baseline component.Experiment results indicate that ERS-2 InSAR data is capable of mapping urban impervious surface cover with a reasonable accuracy.Average error of ISP estimation derived from ERS-2 InSAR data versus actual percent impervious surface is 18.2% with correlation coefficient 0.69.This estimation performance is comparable with the results obtained by using SPOT5 HRG multi-spectral imagery,which average error and correlation coefficient is 13.70% and 0.77,respectively.And the combination of InSAR data and SPOT5 HRG imagery can improve the performance of impervious surface percent estimation with average error 12.45% and correlation coefficient 0.80.关键词：urban impervious surface;synthetic aperture radar（SAR） interferometery;ERS-1/2 InSAR archives;classification and regression tree（CART）
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发布时间：2021-06-10 - 摘要：The new discovered copper mineralized zone in Halasu region, Xinjiang kalaxiange mineralization belt,was selected as the case study in order to fast extract ETM+ mineralization alteration remote sensing anomaly.First of all,the paper discussed the geological background,geophysical and geochemical anomaly characteristics of this area,aggregated metallogenic regularities,then,compared the features of remote sensing data.Though choosing ETM data sources,we achieved geological,geophysical,geochemical data processing and remote sensing image enhancement processing.Spectrum characteristics of known alteration distribution area and geologic setting,six（bands band 1—5 and 7） of alteration rock of ETM+ image and the enhanced image（ratio image,the principal component analysis image）,iron-stained anomaly and hydroxyl anomaly were analyzed.Spectrum knowledge to differentiate alteration rock from mining region strata was discovered.By introducing the standard error based on TM4<120,we known that the brightness value of phyllic rock was 75.6<TM5/7<87.3,the propvlitization altered rock 64.2<PC4-H<73.42.By the use of the mineralization geological feature of mining region and typical ore deposit mineralization model,we could establish the remote sensing geology mineral prospecting model,and then,the remote sensing geology ore-control characteristic was investigated.The middle-bottom basalt and augitophyre,in the middle Devonian Beita mountain group,were the main rock of the porphyry mineralization within the copper（or molybdenum） belt.As a result,the abnormal information model of extracting alteration remote sensing based on ETM+ image has been developed.Additionaly,the alteration remote sensing abnormal information could be extracted by the expert classifier model of ERDAS IMAGING V8.7.Based on the result we can combine geology with remote sensing image processing.Although the establishment of the regulation was manual,whereas at present,the geological expert system theory was not yet ripe,such model in this field has opened up a new development path.Through actual verification,it proved that the results had good correspondence with the actual condition,and the copper mineralization belt had been discovered.This model provides reference to geological investigation and prospecting for copper mines in those area with the similar geologic background to the southern margin of Kalaxiange region,in Xinjiang.关键词：Halasu;alteration anomaly;knowledge discovery;decision tree
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发布时间：2021-06-10
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