最新刊期
卷 ， 期 4 ， 2008
- 摘要：Scale is a key concept for understanding the complexity of earth system.And it is regarded as one of the main challenges of earth observation.It is crucial to select the optimal spatial resolution of remote sensing image according to its application field and its characteristics.Based on analyzing the scale characteristic of remote sensing images,this paper analyses the scale selection and discusses geo-statistics based method of quantificationally selecting the optimal spatial resolution of remote sensing image.Especially,this paper analyses traditional local variance method and its defects.As for local variance method,it is suggested to measure the relationship between the size of the objects in the scene and spatial resolution,and then calculate the mean value of the standard deviation by passing a n pixel by n pixel moving window for each pixel on successively spatially degraded images,and then takes the mean of all local variances of the successively spatially degraded images as an indication of the spatial elements within the scene of the image,according to which the optimal spatial resolution whose mean local variance is maximum can be estimated.So the traditional local variance method computes the mean of all local variance on the different ground area,which results in that the local variance does not fall substantially with the successively degradation of spatial resolution of the image,consequently the computational results are non-comparable,and the traditional method can not achieve satisfactory result.Breaking through the limitation,this paper proposes the modified local variance method based on variable window sizes and variable resolution to quantitatively select the optimal spatial resolution of remote sensing images,which are high spatial resolution images with large window size and low spatial resolution images with small window size,so that the relevant ground area is kept consistent.The actual process inevitably involves the ideal decimal window size,which is proposed to be computed based on the spatial statistics theory.Consequently,the optimal spatial resolution of remote sensing image can be computed by comparing the modified mean local variance.This paper takes three pieces of IKONOS images which stand for building district,farmland and forest individually as primary experimental image and the modified local variances are computed for the three kinds of landscape individually.The experimental results show that this geo-statistics based method of quantificationally selecting the optimal spatial resolution of remote sensing image has theoretical and instructional meaning: the spatial resolution of 3—5m,3—5m and 1—5m is respectively suitable for landscape of building district,farmland and forest;only the local variance based on variable window size and variable resolution can indicate the actual change of local variance with the degradation of spatial resolution of the image;local variance method adopts proper window size to reflect the change of landscape property,so it can reflect the micro-characters and is suitable for study on the fine scale landscape or the artificial landscape.关键词：remote sensing image;scale;spatial resolution;local variance
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发布时间：2021-06-10 - 摘要：Remote sensing imagery provides vast information about the land surface,and the spatial distribution of land cover types by classification could be obtained.Moreover,the spectrum of land surface objects is useful to improve the accuracy of image classification.However,the spectrum of the same object may be different when they are measured at different measuring scale and with different method.For example,the spectrum of winter-wheat extracted from Landsat TM and measured in the field are different.So it is important to study the scale effect and scaling method on the spectrum at different measuring scale.In this paper,we took the winter wheat as example,and selected Shunyi region in Beijing as our study area.Firstly the definition of three-scale spectrum was explained,then we analyzed the discrimination using the measuring data to highlight the importance of the scale transformation of spectrum.The collected data included: field measured spectrum of leaf and canopy and the hyper-spectrum high-resolution remote sensed imagery OMIS,Landsat TM and MODIS data.We compared the winter-wheat spectrums and calculated the slope of "red-edge" at different measuring scales.We also studied the scaling-up method of the spectrum,and the physical model（SAILH） and statistical model（Linear mixing model） were used to describe the relationship between the spectrum at different measuring scales.SAILH is a typical radiation transfer model,which can be used to simulate the canopy spectrum by taking the leaf spectrum,some structural parameters and environmental variables as inputs.In this experiment,the input parameters were acquired with high accuracy,so the error of simulation result is very small: 8.45%.Linear mixing model was used to describe the relationship between endmember spectrum and pixel spectrum.The resolution of MODIS imagery（visible and infrared bands） is 250m,which was taken as pixel spectrum,and the endmember one can be got by multiple methods,here we adopt two methods: Broadman method from MODIS imagery and aggregation one from TM imagery.The unmixing results were compared and analyzed,and the linear mixing model was validated.Through the spectrum data in the study area,the winter-wheat spectrum of leaf,canopy and OMIS imagery is different and the character reflecting the plant growing status is also varied.As for the scaling method,we found statistical models and physical models are fit on the three research scales respectively.However,the endmember selecting method from the imagery also needs more improvement,and more statistical models or coupling physical models should be explored in the further work.关键词：spectrum;measuring scale;SAILH;linear mixing model
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发布时间：2021-06-10 - 摘要：It’s urgent to consider how to systematically establish the civil aeronautics remote sensing system,assimilate application requirements in the process of remote sensing quantification,and improve the earth observing ability and quantitative level.On October 21,2003,the CBERS-02 was successfully launched.Charge Coupled Device Camera（CCD）,Wide Field Imager（WFI） and the Infrared Multi-Spectral Scanner（IRMSS） are three important payloads on CBERS-02.The quantitative researches and experiments based on CBERS-02 data had been carried out,which provide a reference and demonstration for the quantitative application researches of FY,HY,Resources,HJ etc.satellites data.To conducted the quantitative application of the CBERS-02 satellite images,we have performed a series of experiments and researches.Researches on the quantification application of CBERS-02 include good knowledge of the payload’s parameters,image’s quanlity,radiometric calibration,validation of arithmetic models and the assessment of the application potential.As one of these research,the method of radiometric calibration and coefficients of the CCD,WFI and IRMSS have been published,which has provided basic support to the quantitative application of the CBERS-02 satellite images.In this study,the remote sensed data processing frame and products system are designed based on the characteristic of the CBERS-02 images.The study demonstrates that the functions of quantifying processing for CBERS-02 satellite include the stage from the remte sensed signals to quantitative information and to the inversion of the surface parameters for CBERS-02.To provide a compositive product system for CBERS-02 satellite,five level products have been designed based on the consideration of geometric and radiometric characteristic of CBERS-02 satellite,which will be a reference of the product system for the coming high spatial resolution satellite image in our country.The five level products are: Level 1（1B data）,Level 2（relatively radiometric calibration product,MTF compensated product,coarse geometric registration product,accurate geometric registration product）,Level 3（appearance radiance,appearance reflectance,appearance brightness,cloud identified product）,Level 4（surface reflectance,surface brightness,surface albedo,LST） and Level 5（vegetation index,LAI,land cover,FPAR）.In this study,techniques of quantifying processing for every level product are illustrated completely for CBERS-02 satellite.For the stage from the remote sensed signals to quantitative information,the vicarious calibration and cross-calibration of CCD,WFI and IRMSS images,cloud detection arithmetic of CCD images and atmospheric correction method of CCD images.For the absolute radiometric calibration for the CBERS-02 satellite,vicarious and cross-calibration are performed and the calibration coefficients are achieved for CCD camera and IRMSS camera.And the cross-calibration is also carried out for WFI imager and the calibration coefficients are calculated as the first time in China.For the stage from quantitative information to the inversion of the surface parameters,the following products will be processed and achieved: surface reflectance,surface albedo,vegetation index,LAI,FPAR,land cover and LST,ect.Based on these quantitative researches,the professional quantifying processing software for CBERS-02 satellite,Remote Sensing Quantitative application software:CBERS（RSQA-CBERS） is developed,which will facilitate the quantitative application of the CBERS-02 satellite.The development environment of RSQA-CBERS is MS-Visual C++.However,the current version of RSQA-CBERS is running based on the Level 2 product which is defined and provided by the center of Chinese Resource Satellite data and Application（CRESDA）.Thus,due to the limitation of the images,the surface parameters inversed by the software are limited.What’s more,the inversion arithmetic need to be improved.关键词：CBERS-02 satellite;quantifying processing
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发布时间：2021-06-10 - 摘要：Synthetic Aperture Radar（SAR） sensors have important properties on the operational level,since they are capable of acquiring data in all weather conditions,and are not affected by cloud cover or different sunlight conditions.Their advantages have aided the development of SAR techniques.In recent years,SAR systems have acquired data that are used for various tasks,such as imagery fusion,temporal change detection and SAR interferometry（InSAR）.The basis of all these tasks is accurate imagery registration,though the registration accuracy may vary from one task to the other.In addition,the use of an automatic registration procedure is important due to the increasing volumes of SAR data and their use in a wide range of applications.Referring to the problem of automatic SAR imagery registration,researchers of SAR community have developed and applied various imagery-intensity-based and feature-based algorithms.However,their methods have deficiencies.This paper addresses the problem of automatic registration of spaceborne SAR images.The spectral phase of an imagery given by the Fourier transform is an important source of information since it preserves the location of objects;then,registration techniques based on spectral phase information are a suitable solution to overcome the limitations of the general area-based or feature-based automatic registration methods.The application of the Symmetric Phase Only Matching Filtering to Fourier-Mellin Invariant（SPOMF-FMI） descriptors,one of the spectral information based registration methodologies,allows a registration of translated,rotated and scaled imagery.However,this approach has some limitations,such as imagery size restrictions and weak capability of processing non-linear geometric distortions.In this paper,an enhanced automatic registration method and its utility on the registration of spaceborne SAR imagery are investigated.The approach consists of two main steps: coarse registration using a Range-Doppler equation whose parameters come from ephemeris;and fine registration using the enhanced SPOMF-FMI algorithm.The significant advantage of the enhanced SPOMF-FMI is its capability to process non-linear geometric distortions and its relaxed restrictions on imagery size due to imagery sectioning and Kriging interpolation.As a rule,the general SPOMF-FMI algorithm is based on a rigid body transformation used as the global mapping function for the geometric transformation of the sensed imagery.A simple rigid body transformation can not achieve accurate registration of images with non-linear geometric distortions which are introduced by sensor non-linearity and other random factors.With the help of imagery sectioning and Kriging interpolation,this problem can be resolved.Imagery size restriction is another defection of the general SPOMF-FMI procedure;however,it can also be solved by imagery sectioning and Kriging interpolation.The implementations of our method for Radarsat-1 images,ENVISAT-1 ASAR images are validated by the experiments of automatic imagery registration.The results demonstrate that the new approach can achieve sub-pixel registration when dealing with the same sensor SAR images that have the same orbit number within a repeating cycle or the same orbit pass which guarantees similar imagery textures.关键词：automatic registration;synthetic aperture radar（SAR）;Kriging interpolation
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发布时间：2021-06-10 - 摘要：Spaceborne differential interferometric Synthetic Aperture Radar technology（D-InSAR） in long temporal series is an important technology in the land subsidence measurement,extraction of stable pointwise target is a critical procedure in long temporal D-InSAR processing chain.As we know,now the main two kinds of method have been proposed.One is amplitude method that can be performed to model the similar behaviors with phase dispersion with no resolution loss by means of a time series analysis of amplitude data.The other is based on coherence stability that can be computed in the stack of interferograms.The two methods have their own defects,the former requires a minimum of 30 images to detect the stable pointwise target precisely,the latter will directly lead to lower resolution than the original images.In this paper we introduce an optimized extraction method of stable pointwise target combining amplitude and coherence analysis.In this paper,amplitude detection method is first implemented and the number of stable pointwise targets is extracted.In the step,the data need to be radiometrically calibrated in order to ensure the images to be comparable.And the experiential threshold of amplitude dispersion is selected to extract stable pointwise targets.Aiming at solving problems of low density and ununiformity distribution of pointwise targets extracted by amplitude detection,a multi-step detection method is proposed.The multi-step detection method first uses amplitude detection,the proper threshold is selected to extract candidates of stable pointwise targets,which can ensure most of stable pointwise targets are included and greatly improve computation efficiency by the threshold.Further temporal coherence is utilized to extract the precise pointwise targets.In the step,the model of temporal coherence is established,and the partial atmosphere disturb and orbit error are removed by lowpass filters,sequentially the phase caused by the DEM error is estimated by a rough search of parameter space,the average effective phase are separated in interferogram stack.As the temporal coherence coefficient is proportional to the number of interferograms,the proper threshold is chosen in terms of the proportional relationship.Lastly,pointwise targets of their pixel value larger than the referred threshold are extracted.With the multi-step detection method,more reliable stable pointwise targets are extracted.The evaluation of the method is finally carried out with 26 ERS-1/2 C-band SAR images acquired over Shanghai urban covering 94 km2 from 1992 to 2000.Amplitude detection method and multi-step detection method are implemented at the same region respectively.The effectiveness of two methods is evaluated by the number and density of extracted pointwise targets.By comparison,pointwise targets of extracted by amplitude detection have the lower density than multi-step detection method.And reliability is evaluated by testing the target corresponding to the land type.It is known that stable pointwise targets points corresponding to the stable man-made objects in the urban.After the ground investigation in shanghai urban,we find the most of extracted points are usually corresponding to stable man-made objects,e.g.building and bridge.It fully validates the reliability of multi-step detection method.In summary,the testing results show that the presented method can extract stable pointwise targets effectively and reliably.关键词：long temporal series;pointwise target extraction;amplitude detection;multi-step detection;temporal coherence
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发布时间：2021-06-10 - 摘要：The bidirectional reflective characteristic of objects plays an important role not only in models of remote sensing but also in inversion.Consequently,it is the basis of quantitative analysis in remote sensing with the appropriate method of acquiring the bidirectional reflective characteristic of objects in natural environment.For a long period,BRF（Bidirectional Reflectance Factor） acquired in natural environment and related to radiant environment,is regarded as the expression of bidirectional reflective characteristic of objects.There are differences which must be considered between BRF and real value except in a strict clear day.In order to eliminate the influence of the environment radiance,some method acquiring BRDF（Bidirectional Reflectance Distribution Function） data of objects in natural environment have been designed.But the error occurred in these method has not been strictly analyzed.In this paper these methods are compared by using radiosity-graphic combined model based on three dimensional structures which is one of computer simulation model.And their fixed error affected by conditions acquiring BRDF data is analyzed in the simulation.We also provide principles to choose the suitable method of acquiring BRDF data in natural environment.By these principles we can take the most suitable method under different conditions in order to reduce the error and get acceptable value from measurement.关键词：Bidirectional Reflection;BRDF;BRF;RGM（Radiosity-Graphic combined Model）
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发布时间：2021-06-10 - 摘要：The hyperspectral remote sensing technology,which appeared early in 1980s,combines the radiation information which relates to the targets’ attribute,and the space information which relates to the targets’ position and shape,completing the information continuum of optics RS image from panchromatic image to hyperspectral via multi-spectral image.The spectrum information,which is rich in the hyperspectral image,compared with panchromatic remote sensing image and multispectral remote sensing image,can be used to classify the ground target better.It has become an important technique of map cartography,vegetation investigation,ocean remote sensing,agriculture remote sensing,atmosphere research,environment monitoring and military information acquiring.As Support Vector Machine（SVM） was applied to machine learning fields successfully in recent years,the classic linear pattern analysis algorithms which was called the 3rd revolution of pattern analysis algorithms,can cope with the nonlinear problem.Some references applied the kernel methods to linear Fisher Discriminant Analysis（FDA）,and put forward Kernel Fisher Discriminant Analysis（KFDA）.Firstly,this paper introduced the classification method based on the kernel fisher discriminant analysis.For the binary problem,the aim of FDA is to find out the linear projection（projection axes） on which the intra-class scatter matrices of the training samples are maximized and scatter matrices of inter-class are minimized.For KFDA,the inputted data is mapped into a high dimensional feature space by a nonlinear mapping,while linear FDA in the feature space will be performed.Secondly,we researched on the selection methods of the kernel function and its parameter,and studied on the multi-classes classification methods,and then applied them to hyperspectral remote sensing classification.We use decomposition methods of multi-class classifier and method of parameter selection using cross-validating grid search to build an effective and robust KFDA classifier.Finally,we carried out the hyperspectral image classification experiments based on KFDA and some other comparative experiments.Some conclusions can be drew as follows.Using the kernel mapping,the KFDA experiment on PHI and AVIRIS image demonstrates that the KFDA is less affected by the dimension of input sample,and can avoid the Hughes phenomena effectively.The results show that it has more comparable classification accuracy than support vector machine classifier.There is no need to compute the complicated quadratic optimizing problem in training KFDA classifier as SVM classifier does,so this algorithm is not very complicated and costs less time.Especially in the one-against-rest decomposition,comparing with the SVM,KFDA is much faster.The capability of KFDA classifier is affected a lot by kernel function and its parameters,and a fine recognition precision can only be obtained when the kernel function’s parameters are appropriate.The stability of classification can be effectively improved by parameter selection via cross-validate grid search method.关键词：hyperspectral remote sensing;classification;Kernel Fisher Discriminant Analysis;kernel function
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发布时间：2021-06-10 - 摘要：Target identification fusion based on multi-source remote sensing images can make full use of the redundancy and complementary information from all sensors,acquiring more accurate result of target recognition.One of the pre-condition of identification fusion is target association,which is to determine if the information from two or more images are related to the same target and should be fused together.Due to different performance of sensor and diverse target distribution,the extracted information of targets generally has some uncertainty,which results in the difficulty in judging whether the information from two images is originated from the same target.Therefore,how to utilize the information of remote sensing images to distinguish multi-target association has become an urgen problem.There are two kinds of methods concerning target association when using image data: one is Kalman filtering based data association and tracking,which utilizes accumulated kinematic information of multi-frame images to estimate and track.Typical methods are Nearest Neighbor（NN）,Joint Probabilistic Data Association（JPDA）,Multiple-Hypothesis Tracking（MHT） and so on.These methods need dense sampling of observed data,and the target motion model should be simple.The other one uses image match in computer vision for reference.Typical methods are cross correlation matching,feature matching and so on.These methods usually work on condition that only single target is concerned.For remote sensing images,there are two problems when associating multiple targets in them.Firstly,it is incapable to acquire a series of multi-temporal remote sensing images on the same region at present,so the kinematic state of a target cannot be estimated accurately with low temporal resolution data and the classical Kalman filtering association algorithms are no more applicable.We must seek for other time-independent information as the associating measurement,which can be image invariant feature.Secondly,there are two uncertainties lying in image feature extraction of a target.One uncertainty lies in determining invariant features due to various image distortions such as rotation,scaling and so on.The other lies in establishing feature correspondences between any two consecutive images.So,it is difficult to discriminate the ambiguity of multiple targets’ correspondences when using image matching-based association method.In order to solve above problems,a novel multiple targets association method based on image invariant feature matching and Association Cost Matrix（ACM） global optimization is proposed.At first,the Multi-scale Autoconvolution（MSA） transform of a target is computed based on affine invariant theory and is used as association measurement,which can overcome the negative influence of changes in target’s pose,imaging viewpoint and so on.Secondly,the association cost matrix is constructed based on the dissimilarities of MSA feature matching of any two target pairs from two images respectively,representing the correspondence illegibility of two targets.Finally,the minimal energy of ACM is found using simulated annealing（SA） algorithm,and the global optimal association result is achieved.From the simulation experiments,some conclusions can be drawn as follows:（1） Using image invariant features to perform target association is a validate way,overcoming the bottleneck that the time-dependent kinematic feature cannot be estimated from sparse remote sensing image series.（2） Compared with the NN local algorithm,the optimization of association cost matrix is a global optimal algorithm and has excellent performance in dense targets circumstance.（3）The approximate algorithms such as SA can greatly improve the search of optimal association cost matrix,and then make complex association method practicable.关键词：remote sensing image;target association;multi-scale autoconvolution;association cost matrix
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发布时间：2021-06-10 - 摘要：Beijing 2008 Olympic Games is a grand event of China,also an historic opportunity for Beijing.Giving a high-level Olympic Games with distinguishing features is a solemn promise China and Beijing have made to the world.High Technology always makes significant contributions to fulfill the great success of Olympic games,while remote sensing,Geographic Information System,and Global Positioning System are new technologies that gradually been used for the planning and management.Dynamic monitoring of Beijing Olympic venue using remote sensing technology directly served the "Green Olympics" in this project.This project is supported by Ministry of Science and Technology under the "Hi-tech Olympic Program" and the Chinese Academy of Sciences under the "CAS Hi-tech Olympic program".Institute of Remote Sensing Applications undertook the project and carried out the continuous and stereo monitoring with focuses on the status and changes made during Beijing Olympic venue construction,delivered seasonal and annual reports,and developed virtual simulating system for Beijing Olympic Venue Construction,which serving as an important spatial information source for "Digital Olympics".Multi-temporal high-spatial resolution data have been collected nearly for 7 years,with observation cycle 1 year for airborne remote sensing,and 3 months for satellite remote sensing.Using these satellites and airborne remote sensing data,we extracted thematic information for venue construction project（venue distribution,project schedule,old house breaking and building,etc.）,traffic project（highway,railway,subway,etc.）,as well as environment project（greenbelt,water body,landuse,etc.）,and carried out thematic analysis and dynamic evaluation.All these monitoring results have been released in the form of seasonal and annual reports.Meanwhile,we developed a virtual reality system for Beijing Olympic venue construction and environment monitoring,and a virtual Olympic scenarios releasing and browsing system based on Internet,with our own copyright.Remote sensing data and field data of Beijing Olympic venue,Wukesong Gymnasium,and district from Huixin East Bridge to XueYuan Road were first collected,and then virtual environments were built for them.OpenFlight format was adopted to manage simulated data for these districts,and simulation system was developed through model simplification,real-time rendering optimization and CPU performance optimization.And in the end a network virtual simulation system for Beijing Olympic venue construction and environment monitoring was developed using ActiveX/COM+/DCOM component technology and OSG（OpenSceneGraph） three-dimensional graphics engine.Those three-dimensional,continuous,and accumulated observation data and standard products were directly delivered to the Beijing Organizing Committee for the Games of the XXIX Olympiad（BOCOG） and the Beijing authorities,which effectively served for Olympic planning and construction,providing scientific data and information for "Green Olympics".Airborne remote sensing images acquired in this project were used by the Construction & Project Planning Department of Olympic Committee and Beijing Municipal Commission of Urban Planning for Olympic Park planning and designing,traffic planning and designing for the park and its surrounding areas.
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发布时间：2021-06-10 - 摘要：The amount of various impervious land surfaces increases in the process of urban development.Accompanying with the fast urbanization,it has been well known that the drastically increasing impervious land surface has serious impacts not only on urban environment but also on regional and global environment,such as changing rainfall runoff process,causing urban heat islands,changing local microclimate and so on.However,due to the complex components of impervious surface,it is difficult to derive the accurate estimates of impervious cover.Thus,the objective of this study was to directly estimate impervious cover based on multi-spectral features from remote sensing image in city center of Beijing.According to the spectral response of different land cover,a new methodology was explored to directly estimate urban land imperviousness.The object oriented method was applied to classify land cover/use into basic land units within similar spectral features and texture.Then,the multiple principal regression model was explored to estimate the relation of surface imperviousness and TM image based spectral response.The results showed that the combination of multi-spectral features could efficiently predict land imperviousness.Totally,twenty-two spectral indicators were identified to indicate the characteristics of surface imperviousness.Among the spectral indicators,it showed that the four indicators among others,Band 1,Band 5,Band 6 and the Standard Deviation of Band 6,have the closest relation with surface imperviousness.The significant relations of land imperviousness and TM based spectral features could reach 0.851（P<0.001）.The model validation showed that the estimated imperviousness based on TM image was accurate（R=0.91）.It proved that the developed method could efficiently estimate land surface imperviousness.In addition,based on the developed impervious model,the distributed pattern of surface imperviousness within Beijing center was extracted.The results showed that the urbanization degree is very high.More than 70% lands of the city center were estimated as high or middle imperviousness,the index of which was between 50%70% or larger than 70%.The average size of these impervious patches was large and the distribution pattern was heterogeneous and fragmented.Moreover,from the core center（within the 2nd ring road） to the urban-rural edge（the 5th ring road） the surface imperviousness patterns were quite different.For example,the 3rd and 4th rings were fast developed in recent decades,containing diverse land use/cover types such as large commercial center,shopping center and residential district.In contrast,more high impervious patches,mainly old buildings,such as old flat residential built-ups and historic sites,filled up the 2nd ring where the development history is thousands of years and new development was strictly limited.The 5th ring was the urban-rural transitional zone,which is the new developmentregion for the city sprawl in recent years.Large industry district,technology district and residential district with high or middle impervious patches occupied around 68.8%.关键词：remote sensing;impervious surface;landscape pattern;object segmentation
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发布时间：2021-06-10 - 摘要：Since the launch of ENVISAT satellite in 2002,a considerable amount of programs has been set up in application of the new generation of Advanced Synthetic Aperture Radar（ASAR） instrument for monitoring agricultural crops,which extends the mission of SAR instruments onto ERS-1/2 and provides the continuity on agricultural monitoring.Under the framework of Dragon Project,which is a three-year（2004—2007） cooperation program between NRSCC and ESA focused on science and applications development in China mainly using data from the ERS and ENVISAT missions,a project theme on rice monitoring was set and endeavored to validate the efficiency of ASAR data for rice monitoring in China.Under these circumstances,this paper intends to propose a practical method for rice mapping,taking advantage of ASAR Alternative Polarization Mode（APMode） product which allows the acquisition of radar images with two simultaneous polarizations selected from the four polarizations HH,HV,VH and VV.Therefore,during the early rice season of Gaoan district of Jiangxi Province in 2006,two scenes of ASAR APMode products were acquired on the date of May 8 and June 12,in a specific radar configuration of HH and VV polarization and at approximately 40° incidence angle.Meanwhile,six differential GPS samples containing different crops and other land surface objects,with size of 1 km×1 km for each,were collected during the ground campaign.Several dominant crop calendars were also recorded as ancillary information for image interpretation.The preprocessing of ASAR images includes the calibration,speckle noise filtering,co-registration,and geo-reference.Backscattering coefficients of different crops and other land surface objects were obtained from ASAR images overlaid with the GPS samples and all were averaged for further analysis.Before the rice mapping,the backscattering difference of land surface objects was analyzed by the maps of multi-polarization backscattering difference for each date and multi-temporal backscattering difference of the same polarization which were calculated from the received ASAR data.Accordingly,paddy rice showed significant disparity of backscattering coefficient between VV polarization and HH polarization,while the multi-polarization backscattering difference was not obvious for other land surface objects.Moreover,high sensitivity of HH polarization radar wave to the temporal change of paddy rice was observed,which indicated multi-temporal backscattering difference of HH polarization contained more paddy rice information.However,it should be that the ASAR images employed in the above analysis were acquired at different rice growth stage one of which is at rice transplanting stage and the other at booting stage.During the rice transplanting,the dominant scattering mechanism is the surface-volume scattering,while single-volume scattering becomes the dominant scattering mechanism during the booting stage since paddy canopy has become much denser.Therefore,an optimal combination of backscattering difference images used for paddy rice mapping was obtained.Then,two classification methods are compared: threshold classification and supervised classification were applied separately to retrieve paddy rice from the combined image.As a result,a high rice identification accuracy of 84.92% was achieved using the supervised classification.The relative inferior performance of threshold classification could be caused by the remained speckle noise,because the threshold classification was performed at pixel scale.Finally,based on the multi-remporal and multi-polarization backscattering difference,the practical approach presented in this study not only simplifies traditional rice mapping methods,but also keeps a high mapping accuracy of rice crop.However,limited by the available ASAR data,further study should be extended for investigation of ASAR data on rice mapping of different rice calendar and for rice yield estimation using multi-temporal and multi-polarization radar images.关键词：rice mapping;image classification;ASAR
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发布时间：2021-06-10 - 摘要：Currently,synthetic aperture radar（SAR） is a hot research topic in the field of Microwave Remote Sensing.It possesses many incomparable advantages such as the capability to work at all time and under all weather,high spatial resolution and strong penetrability through the ground surface and so on.Therefore,it is significant to extract information from SAR imagery.It can not only compensate the deficiency in optical imagery,but also help constructing the spatial database.Road information is a kind of most important spatial basic information,which has a lot of significant applications,including military defense,map matching,database updating etc.Ratio of average（RoA） algorithm is a classical method for road extraction from SAR imagery developed in recent 20 years,however,because of SAR speckle noise and other factors,it still has some disadvantages.For example :low locality accuracy,thickened edges,higher false alarming rate and non-continuous road segments.This paper is mainly divided into two sections to resolve these problems.In the first section,it introduces a local ratio detector for road extraction.But it has defects on the accuracy of position and widening road width.In order to tacle these problems,a pruning window is added into the process,which is used to remove effects of rivers and trees.Therefore,a thinned and more accurate road map can be gained.Based on the above method,in the second section,we firstly describe the road linking method using chain codes,define the chain code energy network and import the prior knowledge into processing course.Then,we analyze linking probability of line element in simulated road map and propose an approach to optimize the chain codes.Because the process is programmed with classed computation,and need no search for all line segments so that it improves efficiency highly.At last,this method is applied to extracting the road network from real SAR image and its validity has been proved.关键词：SAR image;road extraction;road network;chain code optimization
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发布时间：2021-06-10 - 摘要：The normalized difference vegetation index（NDVI） is the most widely used vegetation index,which is not only applied into many operational applications,but also is an important parameter for some models.The objective of the study is to evaluate the effect of the red band and near-infrared（NIR） bands on NDVI,which is the function of red and NIR spectral bands,and to explore the relationship between band position and bandwidth of NDVI with the aim of monitoring the biophysical and biochemical parameters of rice.Canopy hyperspectral reflectance data of rice at seven stages were collected by portable spectroradiometer with a spectral range from 350 nm to 2500 nm.Extreme values of rice reflectance spectra in the visional and NIR regions at different stages were identified in order to determine the suitable band center positions for red and NIR bands of NDVI.Through the analysis of hyperspectral reflectance,the spectral bands at 674 nm and 860 nm were selected as red and NIR bands center positions,and for the computation NDVI.Then,the NDVI values were calculated under variable band position and bandwidth from hypersectral data of rice at different stages.Three scenarios were adopted to study the effects of band position and bandwidth on NDVI as follows.The NDVI values were calculated（1） from a constant red band,centered at 674nm with a 10-nm bandwidth,and variable NIR band positions and bandwidths for rice,（2） from a constant NIR band,centered at 860 nm with 10-nm bandwidth,and variable red band positions and bandwidths,（3） from a constant NIR band,centered at 860 nm with a 10-nm bandwidth,and variable red band bandwidths with 674nm,645nm as band center position,respectively.Moreover,the interactions between bandwidth and the central position of red band under the a precision requirement of 99% were analyzed too.The result indicated that band position and bandwidth of NIR channel have no significant influence on the NDVI of rice canopy at different development stages while those of red channel affect NDVI significantly,especially when band position approaches the red minimum（near 670nm）.That means NDVI is more sensitive to band position and bandwidth of red band than those of NIR band.The sensitivity varied with rice development stages.Relative to the middle and later development stages of rice,the NDVI values derived from reflectance data at early development stages are more easily affected by band position.Although NDVI of rice was sensitive to band position,the difference between NDVI values was less affected by band position,therefore,the data sources should be consistent when NDVI was used to study rice.In addition,the NDVI of rice at different developments was essentially affected by proximity of the red and NIR bands to red edge region（690—740 nm）.Under the 1% error of NDVI,the bandwidth of red band becomes narrower when the central position of the band moves towards longer wavelengths at bloom stages,and it reaches its minimum at round 690 nm.Beyond that position,the bandwidth becomes a little wider.However,for early and later development stages,the variation of red bandwidth with central position was more complicated due to the fluctuation of red bandwidth around 648 nm.The research on the effects of band position and bandwidth on NDVI will provide useful information for remote sensing of rice.关键词：NDVI;band position;bandwidth;rice
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发布时间：2021-06-10 - 摘要：The technology of airborne Light Detection And Ranging（LIDAR） receives wider attention and broader application for the ability of rapid acquiring three-dimensional topographic measurements of large-scale areas.These measurements are three-dimensional point clouds with irregular spacing.The points include bare ground,buildings,vehicles,vegetation and so on.It is important to identify and classify ground and non-ground points for generating DEM and extracting objects.Removing non-ground points from LIDAR datasets is called as filtering.In the last few years,a number of filtering algorithms have been explored.But most algorithms have more or less drawbacks and limitation in adaptability and correctness.Filtering is still a challenging task that is difficult to resolve for scene complexity.The topographic theory that filtering lies on are generally two aspects: one is that the natural terrain has continuity;the other is that the size of objects often has a range.Filtering based on mathematical morphology is considered as a promising strategy because it combines the two above aspects.But most of researchers carry on erosion or opening operation using every point,which is time-consuming and often cause errors.In order to overcome the weakness mentioned above,a new method of filtering based on morphological gradient is proposed in this paper.The method mainly analyzes the distribution characteristic of LIDAR points according to morphological gradients,so as to choose the specific points to carry on the morphological operation,which mainly include following steps.Firstly,point clouds are divided by an index mesh,which can organize points effectively and maintain the high resolution potential of raw data.Then,the morphological gradient of each point is calculated using the method suitable for filtering,and the low outliers are removed.Finally,some points are chosen based on gradients to carry on an improved opening operation iteratively.The iterative times are controlled through analyzing the gradient histogram.During each time of iteration,a point is classified as an object point if its difference of the height after opening operation and the original height is more than a threshold.15 sample data sets are released by ISPRS especially for testing of filtering algorithms,mainly including situations when difficulties are encountered in different geographical environments,such as outliers,object complexity,attached objects,vegetation and discontinuities in the bare ground.The semi-automatic filtering and manual editing of sample data have been done by ISPRS,whose results are used to evaluate result of automatic algorithms.ISPRS also publish the test results and analysis of eight typical filtering algorithms.The method proposed in this paper is tested with the sample data and compared with other filtering methods qualitatively and quantitatively.Qualitative assessment is done by visual representation of filtering results.Quantitative assessment is done by evaluating Type Ⅰ error（rejection of bare ground points）,Type Ⅱ error（acceptance of object points as bare ground） and Total error.The experimental results show that the method has high robustness in all kinds of complex scenes.The filter based on morphological gradient can reduce the nonessential computation as well as the possibility that errors happen.All types of error are controlled simultaneously in a relatively small range.The topographic features are well preserved while object points are removed effectively.So the method has good reliability and practicability.关键词：LIDAR;filtering;airborne laser scanning;morphological gradient
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发布时间：2021-06-10 - 摘要：There are many problems in eco-geological environment evaluation,such as being short of macro,difficulty of data acquisition,trouble of indicator quantitative analysis and operation.Consequently,eco-geological environment evaluation and its dynamic monitoring is influenced by them.In order to solve this problem,this paper discusses the feasibility of eco-geological environmental evaluation with NDVI and lakes changes and open out practice in QingZang Plateau.Firstly,analyzing lake changes by considering the influence of eco-geological environment in lake source area,the relationship among eco-geological environment,lake change and NDVI change in lake source area are established.Secondly,a method of eco-geological environment assessment which is named as "Assessment of eco-geological environment base on Remote Sensing Dynamic Changes Information of Lakes and NDVI",for short "L-N",is given.Finally,based on the survey data of lake by Remote Sensing technique with Landsat TM in 1990’s and Landsat ETM in 2000’s,with contemporaneous NDVI data from NOAA/AVHRR sensor,the eco-geological environment assessment of QingZang Plateau is accomplished by "L-N".The results show that: The eco-giologic environment of QingZang Plateau has been worsening,it is obvious in the fringe area of north Plateau,Rivers Sources of Yangtze River,specially Qaidam Basin.On the contrast,environment situation is preferable in southeast of the Plateau and Yarlung Tsangpo river basin.From the evaluation result of QingZang Plateau,"L-N" way obtains closely consilient outcome and its needful index is consumedly decreasing,at the same time,with nicer and easy operation.Ulteriorly,in virtue of powerful monitoring ability of Remote Sensing technology,it is beneficial to develop dynamic eco-geological environment evaluation investigating in QingZang Plateau.In conclusion,it is helpful to promote efficiency of environment monitoring by "L-N" with Remote Sensing technology,having the characteristic of reducing many evaluating index,expediently being investigated by Remote Sensing technology and feasibility.In addition,it is a significant for eco-geological environment to monitor lake and NDVI data in QingZang Plateau.If correlative theory can be advanced,ploting out fixed area and surveying lakes will change by Remote Sensing image processing technique.Therefore,it is possible that we can improve,eco-geological environment status of a region in macroscopical scale.关键词：QingZang Plateau;lakes;NDVI;remote sensing;ecological-geological environment.
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发布时间：2021-06-10 - 摘要：Songnen Plain which lies in northeast China,is the important grain production base for China,precision agriculture,serious soil erosion and quantitatively remote sensing thirst for soil spatiotemporal variation,but traditional chemical analysis method can not satisfy the need,because of little points,slow test rate and limited measuring area.Soil spectral reflectance is the compositive reflection of soil physical and chemical properties,and many studies have predicted soil properties with soil hyperspectral reflectance.Soil reflectance and its models are different for places,while the physical models,such as soil BRDF models,have been used to describe the spectral changes caused by soil surface physical variation,can not depict the spectral characteristics with different soil chemical compositions quantitatively.To quickly acquire the physical and chemical properties of typical soils in Songnen Plain,and provide the spatiotemporal soil information for quantitative remote sensing,precision agriculture and other related studies,Nongan county,which is the typical area in Songnen Plain,was selected to study the spectral characteristics of different soils.It is black soil Zone in Heilongjiang province to be selected to study the effect of physical and chemical properties on single soil spectral characteristics.Soil organic matter,including total N,total Fe and water content were measured with traditional chemical methods.Laboratory spectral reflectance between 400—2500 nm was measured with ASD FieldSpec 3 pectroradiometers.Soil hyperspectral reflectance was continuum removed,and its derivate was calculated.Spectral indices relating to soil parameters were extracted with spectral analysis methods.Then the spectral characteristics of typical soils in Songnen Plain,and their relationship with soil physical and chemical properties were analyzed.Soil properties predicting models based on spectral indices were built,and the Black soil reflectance simulating models were built with the extracted spectral controlling points.The results show that: the spectral differences among soils in Songnen Plain are mainly at the two absorption vales,the wavelength domains are 450—600 nm and 600—800 nm.Organic matter is the determining factor of Black soil reflecting spectral characteristics.Fe is not important to the spectral characteristics of soils in Songnen Plain,which is different from the case in south China.With growing soil moisture,soil reflectance decreases and increases,because of water specular effect,and the process of moisture effect on soil reflectance,the inflection point can be described quantitatively with cubic functions.The soil parameter predicting models basing on soil reflectance and its spectral characteristics can be used for soil parameter quickly measuring.As the correlation between organic matter and total N in Songnen Plain is significant,total N content of soils can be characterized with the result of organic matter spectral prediction.The Black soil reflectance simulating model（linear,quadratic）,which is based on the extracted spectral controlling points at 450,500,590,660,930 nm,describes the spectral characteristics of Black soil precisely,and can be used for hyperspectral data compression,hyperspectral reflectance reconstruction with multispectral data.关键词：soil;hyperspectral;reflectance;organic matter;moisture;Fe
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发布时间：2021-06-10 - 摘要：Snowmelt runoff is the main composition of water resources in middle and high latitude region.Research on the snowmelt runoff process are important in monitoring and foresting flood and drought disaster as well as managing water resources.In recent years the studies on distributed snowmelt runoff model are based on the application and local improvement of foreign mature models such as the SRM,have more limitations in their applicabilities.A Distributed Snowmelt Runoff Model based on RS and GIS has been designed and explored in this paper.The model consists of three parts,grid snowmelt model,grid snowmelt flow generation model and grid snowmelt flow concentration model.It was based on energy balance and water quantity balance.The grid snowmelt model was based on the energy balance.And the grid flow model was based on the water volume balance as well as the distributed grid runoff model,thus has formed a completely,distributed snowmelt runoff model which could apply to the actual service.Snowmelt and runoff process were all based on grid scale,all of the input/output data were distributed processed,the primary format were TIFF,so the model was provided with distributing character and had strict physical mechanism.An important concept of "Unit Period of Time" was put forward in the paper,and thereby developed a "Degree and Cent Snowmelt Runoff Model"from the traditional "Degree-Day Method" which has widespread application nowadays,for it was not fit for the practical applications because of its glancing time-resolution,in the snowmelt period,the processes were usually very unabiding,so the time-step in day is not appropriate,but with the "Degree and Cent Snowmelt Runoff Model",the computing time-stride of the snowmelt flood precaution decision support system could adjust itself,and to make use in the real-time computation and the forecast.It also brought forward another important concept of "Freezing and Melting Coefficient",which was aimed to explain the snow freezing and melting repetitive physical mechanism and this concept is significant to exactly hold the snowmelt process,because the water melting of snow/ice usually refreeze in the afternoon.Besides,a Distributed Snowmelt Runoff Stimulating System by GIS has been developed,which is the platform and support of the running of the Distributed Snowmelt Runoff Model,has all of the basic functions of GIS,such as spatial analysis,hydrological analysis,dynamic simulation,and so on.Both the model and the system are the kernel modules of Snowmelt Flood Prewarning Decision Support System.At last,based on a great deal of remote sensing data,obtained distributed input data for the model,for example,distilled snow cover information with Unmixing Pixel Method,and land surface temperature（LST） with Split-window Arithmetic designed by Zhihao Qin,Kebiao Mao and so on,with our proper correction.Basic geo information data like DEM and its relevant data such as shope and asect,field simultaneous observation data mainly including weather data and hydrology data,were disposed with uniform spatial resolution（30m×30m in this paper） and the same data format（TIFF）,stimulated the runoff process in representative snow melt period in 2006 of the typical study area,Juntanghu river basin,a close watershed,which located in the middle of the northern Tianshan Mountain,Xinjiang,China,the area of it is 833.57km2,with the Distributed Snowmelt Runoff Stimulating System,the time-step was 15 minutes.An average relative error of this model with the synchronous true measuring data was under 0.18,indicating that the model was able to meet the need of snowmelt flood alarming and foresting.关键词：RS;GIS;distributed snowmelt runoff model
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发布时间：2021-06-10 - 摘要：Daily regional evapotranspiration in Hebei Plain has been estimated based on the remote sensing data as well as the Surface Energy Balance System（SEBS） in this study.The Surface Energy Balance System（SEBS） model was developed to estimate land surface fluxes using remotely sensed data and available meteorological observations.It has the most important advantage of its inclusion of a physical model for the estimation of the roughness height for heat transfer which is the most critical parameter in the parameterization of the heat fluxes of land surface.In this paper,SEBS has been utilized to estimate the surface fluxes over Hebei Plain in Northeastern China by using MODIS/TERRA products,in combination with meteorological data collected in meteorological stations distributed over the study area.The estimated daily evapotranspiration by SEBS in cloud free days are first compared with measurements by the large weighing lysimeter in Luancheng Agro-Ecosystem Station（LAES）.The comparisons show that the estimated evapotranspiration from SEBS have a good agreement with the ground truth data.Based on the validation of the model,a modified version of SEBS is utilized to analysis the soil moisture status over the study area and the spatial-temporal distributions of actual evapotranspiration were analyzed by combination of the up-to-date land cover map in Hebei Plain.关键词：evapotranspiration;remote sensing;surface energy balance system（SEBS）;soil moisture
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发布时间：2021-06-10
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