最新刊期
卷 ， 期 6 ， 2007
- 摘要：Leaf area index（LAI） is the fundamental index to indicate vegetation growth and it can be effectively used in agricultural yield estimation.In this paper,we investigate the LAI inversion uncertainty from mixed pixels.There are two situations for the mixed pixels: one is the pixel mixed with crop of different growth stages;the other is the pixel mixed with different components.The result shows that the accuracy of LAI inversion is almost not affected by different corn growth condition.On the other hand,the impact of mixed pixel on the LAI validation is clear.Field LAI measurements of some points don’t stand for the pixel LAI when the pixel consists of crops of different growth.In this situation,we should be cautious for validation of inverted LAI.关键词：mixed pixel;leaf area index（LAI）;inversion;uncertainty analysis
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发布时间：2021-06-10 - 摘要：Imaging model is the basis for most geometric processing of space-borne SAR imagery,including image rectification,radargrammetry and image geolocation.This paper develops a parametric imaging model for SAR,which explicitly describes the relationship between the 3D cartographic coordinate of a ground target and the 2D image coordinate of the corresponding pixel.The parameters of imaging model consist of near range,image starting time,Doppler parameters and orbit parameters.After quantitatively evaluating the influence of the Earth perturbations on satellite orbit,we present an improved satellite orbit model,which uses less parameters and is able to fit the space orbit at nearly the same accuracy compared with classical model.As an application of the imaging model,an orbit refinement algorithm using ground control points（GCPs） is presented.For this purpose,the imaging model is rewritten to incorporate only 9 orbit parameters,and then linearized through differentiating with respect to the orbit parameters.For each GCP,the image coordinates are expressed as the function of the orbit parameters and two error equations are formed.The orbit parameters can then be iteratively determined through solving a linear equation set consisting of the error equations from all GCPs under least square rule.The orbit refinement algorithm is tested on a RADARSAT SGF（SAR Georeferenced Fine resolution product） scene.5 GCPs are identified from 1∶50000 scale topographic maps to refine the orbit parameters coming with the SGF image.The accuracy of the refined orbit is demonstrated through geometric correction of the SGF image.While the SGF data is poorly corrected using the original orbit data,it is precisely corrected with refined orbit parameters.关键词：space-borne SAR;imaging model;orbit parameters;orbit refinement
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发布时间：2021-06-10 - 摘要：In SOM algorithm it will create a map in output layer in which the cells are labeled class ID,e.g.1,2,3,etc.It’s curial for correctly classifying the data to make map.In this paper,we focus our interests on analyzing the map and the process of creating map to improve the SOM.We take three measures to change the map.We can classify the pure pixels and find the mixed pixels through the changed map.Furthermore,we can process the unclassified pixels from the view of linear spectral mixture analysis（LSMA）.Furthermore,we consider the two constraints: unnegative and the sum one,so the constraint spectral mixture analysis（CSMA） is applied in this paper.After CSMA,we assign the class ID to the endmember which has largest proportion in the mixed pixel.So,the spectral unmixing classification based on category proportion is performed to the unclassified pixels.Thus,we can get the extreme classification combining the former results.The experiment shows that the classification combined SOM with LSMA can get better classification results and well improve the classification accuracy.关键词：hyperspectral image;SOM neural network;linear spectral mixture analysis（LSMA）
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发布时间：2021-06-10 - 摘要：Synthetic aperture radar（SAR） imagery is one of the most advanced technologies in recent two decades in microwave remote sensing.As the resolution reaches a meter order,reconstruction of the 3D stereo objects on the terrain surface from all-weather SAR image becomes more feasible.Due to some technical limitations,conventional radar measurements are mostly restricted to classifying,inverting and reconstructing the stereo objects.It remains for further study via fully polarimetric SAR imagery with high resolution.Since pioneer experiments of SIR-C in 1994,some airborne and satellite-borne polarimetric SAR have been developed.The satellite-borne ALOS phase array polarimetric SAR at L band was launched in January 2006.Canadian Radarsat-2 polarimetric SAR at C band will be in orbit in 2007.How to effectively implement broad applications of polarimetric SAR images has become an extensive topic.The PI-SAR is a Japanese airborne polarimetric and interferometric SAR with two independent radar systems（L band-1.27 GHz,3m resolution,and X band-9.55GHz,1.5m resolution）.Interferometric data at X band and only polarimetric data at L band can be obtained.Fully polarimetric data from the square loop flights of PI-SAR over Sendai city,Japan in 2004 becomes available.Multi-directional flights can see the 3D objects in different angles and lead to reconstruction of those stereo objects.In this paper,the images of coherency matrix components derived from polarimetric PI-SAR images of two converse flights at X band,are employed which are mostly characterized by double bounce scattering between the building sides and ground surface,a feasible approach to reconstruct the 3D stereo objects,such as a TV tower and campus buildings is developed.Reconstructions of the building targets are validated by the real ground truth.关键词：multi-directional flights;polarimetric SAR;height and location;stereo reconstruction
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发布时间：2021-06-10 - 摘要：The time and frequency synchronization error models are given according to the theory of spaceborne parasitic interferometric synthetic aperture radar（InSAR） and the work characteristic of ultra stable oscillator（USO）.The effect of time and frequency synchronization error on interferometric phase is analyzed based on the error models.The decorrelation caused by the time and frequency synchronization errors is presented for the first time.Based on the formation configuration and image signal model of the spaceborne parasitic InSAR system,the thermal noise decorrelation,ambiguity noise decorrelation,block adaptive quantization（BAQ） noise decorrelation,baseline decorrelation,Doppler decorrelation,mis-coregistration decorrelation and volume scatter decorrelation are analyzed.The relative height accuracy of the system is deduced when all the signal decorrelations are taken into account.The relative height accuracy of the seven sub-swaths of ASAR system on Envisat is simulated.The theory analysis and simulation results indicate the spaceborne parasitic InSAR system can achieve perfect performance if the orbit and radar parameters are correct designed.关键词：spaceborne SAR;parasitic InSAR;synchronization error;decorrelation;performance analysis
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发布时间：2021-06-10 - 摘要：The ERS-1/2（European Remote-Sensing Satellites-1/2） wind scatterometer（WSC） has a resolution cell of 50 Km,but it covers more than 80% of the Earth surface in four days and can make measurements at multiple incidence angles.In this paper,the method to estimate soil moisture is studied using WSC data.Firstly,based on classical GOM model,the high correlation between WSC data and soil moisture is proved,by comparing in-situ-based precipitation and soil moisture with derived Fresnel reflectivity at normal incidence（which is very closely related to soil moisture） at two points（Amdo and Naqu）.This part of work is performed using Database of Global C-Band Radarbackscatter,which is derived from original WSC measurements.Secondly,a simplified algorithm is proposed to estimate soil moisture based on "Water-Cloud" model and AIEM model,the validation is performed using both in-situ-based soil moisture and Basist Wetness Index（BWI）,showing that the algorithm can estimate Spatio-temporal soil moisture distribution effectively.关键词：Backscattering coefficient;soil moisture;ERS wind scatterometer;AIEM model;water-cloud model
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发布时间：2021-06-10 - 摘要：Applying distance weighted factor to the convolution method,the authors put forward an algorithm of image mosaic.Firstly,the algorithm considers the grey scale difference in the edges of image in overlapping area and the relation of neighboring points,it is necessary to use some ways to eliminate or decrease the impact of adjacent pixels,so we must calculate the weight factor,in this paper we make use of spatial in mathematics to compute pixel weighted factor.Secondly,we adopt the convolution method and distance weighted adopetd to realize the seamless mosaic of remote sensing images.Finally,we make some assessments to this algorithm in time spending and visual effect.Many experiments demonstrate that the proposed method is simple and easy,and it has less time consumption and higher efficiency.Adopting the algorithm to deal with the overlapping area can get favorite visual effect.关键词：image mosaic;convolution method;distance weighted;spatial analysis
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发布时间：2021-06-10 - 摘要：Ocean wave spectrum and surface currents can be determined from a series of spatial wave images recorded by X-band marine radar.In the absence of surface current,the three-dimensional spectral energy resulted from the series of images will be confined to a trajectory defined by the still water dispersion relationship.The presence of a surface current will make the three-dimensional spectral energy show a corresponding Doppler shift from which the current velocity can be inferred using the least squares method and then the directional wave spectrum can be also obtained.On the basis of conventional wave spectrum and directional function,the paper emulates a series of X-band radar images considering shadow modulation and simulates numerically the three-dimensional spectrum both with and without ocean surface current,calculates the current velocity by virtue of the Doppler shift,and obtains the two-dimensional image spectrum.Finally the paper analyzes the ocean data measured in a fixed staring direction by an X-band radar in McMaster University and obtains the one-dimensional image spectrum.关键词：X-Band radar;fourier transform;ocean wave spectrum;ocean surface currents
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发布时间：2021-06-10 - 摘要：Exact agricultural crops identification and planting area measure depend on not only classifiers but also training samples imported into classifiers.The effect of training samples,including sample quality and quantity,is greater than that of classifiers on measure accuracy.In order to probe into the effect of training sample on agricultural planting area measure,a representative wheat sowing section was taken as an example.A standard sample set was constructed based the popular TM image,SPOT-5 image with high resolution and field data from GPS.Then training samples with same features and different numbers were respectively input into six different classifiers to classify image and identify wheat,which are spectral angle mapper,parallelepiped,mahalanobis distance,minimum distance,maximum likelihood and support vector machine.Moreover,the experimentation was repeated ten times under different sample numbers.The results indicate: ①Ten measure results fluctuate from six methods under different training sample numbers.The fluctuation is reducing with the increasing of training sample numbers,which is greater in more fragmentized plot.There are some random errors in the current common used method of wheat planting area measure,which always uses one time classification result as the final value.In the task of agriculture planting area mensure,samples should be used as many as possible in classification to advance the result stability and the average of lots of classification results is advised to counteract the random errors.② The wheat pixel numbers（region whole area） differ each other among six methods under the same training numbers.And the diversity is larger in area with complex plant structure and fragmentized plots.Different classifier has different response to the same area,while same classifier has different response to different area.But it can be judge and verdict,which result is nearer to the true value,through analyzing the product accuracy and user accuracy of wheat.关键词：wheat;Planting area;training sample;sample amount;TM scale
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发布时间：2021-06-10 - 摘要：Hierarchical clustering based on the finite mixture model（FM） has shown very good performance in a number of fields.However,it generally requires storage and computing at least proportional to the square of the dimension of observations,so that its application to large datasets has been hindered by time and memory complexity.Another,multispectral images provide detailed data with information in both the spatial and spectral domains.But many clustering methods for multispectral images are based on a per-pixel classification,while uses only spectral information and ignores spatial information.In this work,a new hierarchical clustering based on GFM model,suitable for large datasets,e.g.,multispectral remote sensing images,is proposed.This algorithm integrates with GFM model with Markov random field.The number of clusters is automatically identified by using the pseudolikelihood information criterion（PLIC）.An oversegmented image is obtained by a simple K-means clustering method.Instead of starting with singleton clusters,hierarchical clustering is applied on the oversegmented image.Initial parameters of component densities of GFM model can be easily extracted.At last,the accuracy of the algorithm is quantitatively evaluated through simulated test image generated by using Gibbs sampler.The experiment show a superior performance compared to several other methods,such as K-means and classical hierarchical clustering based on the classical FM model.Its validity is also illustrated by using a polarimetric SAR image of Flevoland in the Netherlands.关键词：finite mixture model;generalized gaussian mixture model;Markov random field;expectation maximization algorithm;agglomerative hierarchical clustering
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发布时间：2021-06-10 - 摘要：It is usually assumed that the prior distributions of parameters and error are Gaussian distribution in remote sensing inversion.This assumption seems to be impractical in many cases.Prior distribution of parameters and error are very important in remote sensing inversion since many remote sensing inversion strategies take advantage of prior knowledge.We present a bootstrap method for estimating the prior distributions of parameters and error in this paper.This method relaxes the distribution assumption of parameters and error,and obtains those approximately exact distributions by means of prior data.Moreover,we classify prior data since they are collected from different classes,and implement statistical test for classified prior data.Results show that proper classification of prior data is reasonable.Finally,we take RossThick-LiTransit linear kernel-driven model as an example,and make a comparison of our method with usual Tikhonov regularizing inversion and Bayes inversion under normal hypothesis with NOAA-AVHRR observations.The result shows that classifying prior data and using the prior distribution obtained by bootstrap method can significantly decrease uncertainty of parameters.关键词：remote sensing inversion;bootstrap method;prior distribution;posterior distribution;hypothesis test
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发布时间：2021-06-10 - 摘要：Landslide prediction is very important in disaster prevention and reduction procedures,including spatial and temporal landslide prediction,and it is one of practical research fields to evaluate and predict landslide hazards using statistic analysis model,but the prediction result depends mostly on sample numbers and spatial distribution.The aim of this study is to analyze and compare the landslide prediction using different models in Cameron highland,Malaysia,and nine evaluation factors are selected,i.e.elevation,topographic slope,topographic aspect,topographic curvature,distance from lineament,land use and land cover,geomorphic characteristics,distance from road and drainage.Support vector machine（SVM） and logistic regression model are applied to landslide spatial prediction and mapping,and the results show that average prediction accuracy using logistic regression model is about 86.2%,but 95.9% using SVM model,at the same time,the prediction result based on SVM model is more changeless,less influenced by sample numbers.So the SVM model is commended for actual applications,and it is more efficient and accurate for landslide hazard evaluation and spatial prediction.关键词：Cameron highland;landslide spatial prediction;SVM;logistic regression model
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发布时间：2021-06-10 - 摘要：The HY-1 satellite is the first satellite for remote sensing of ocean environment in China and COCTS is one of the main sensor.Striping noise is unavoidable for most data of the COCTS because each detector’s response to radiant signal is different.The striping noise can seriously affects the quality of image and quantitative application of COCTS data.In order to decrease this effect we try to remove these stripes in COCTS images using moment matching correction and histogram matching correction in this paper.The final results indicate these two methods are both effective,but considering the overall effect,the histogram matching correction is better.The result of this study is also applicable in striping removal of other multisensor’s remote sensing data.关键词：the response difference of detectors;striping removal;moment matching;histogram matching
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发布时间：2021-06-10 - 摘要：As one of the computer simulation models,the Radiosity-Graphics combined Model（RGM） has many advantages in calculating the bidirectional reflectance factor（BRF）.Because it takes advantage of radiosity theory and computer graphics technique,the model can contain much more detailed and complex structures of vegetation canopy and take reflection,transmission and multiple scattering into account,which is useful to understand the interaction between the light and the canopy.A hypothesis of Lambertian is made in the general radiosity theory,namely,all surfaces of components（i.e.,leaves,stem and background） in the scene are Lambert reflection/transmission.In fact,studies of the properties of leaves have shown that the bidirectional reflectance distribution functions（BRDF） of most leaves’ surfaces are not isotropic.In order to apply RGM to calculate the radiance distribution caused by the non-Lambert（specular） component,a semi-experimental Phong model is used to evaluate the specular radiosity from leaves’ surfaces.This method is applied to the maize canopy,and the results are analyzed.As an interesting experiment,this extended RGM,which includes diffuse and specular component at the same time,not only keeps the advantages of the general radiosity theory,but also eliminates the hypothesis of Lambertian in vegetation scene.关键词：Radiosity-Graphics combined Model（RGM）;BRF;specular reflection;Phong model;maize
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发布时间：2021-06-10 - 摘要：Winter wheat（Triticum aestivum L.） is one of the most important crops in China.Severe epidemic of yellow rust is the main reason of winter wheat yield loss and quality degradation.Hyperspectral remote sensing,acquiring images in narrow and continuous spectral bands,has been recognized as a reliable method for crop health monitoring.In researches related to hyperspectral data analysis,the region of the red-near infrared（NIR） transition has been shown to have high information content for vegetation status.This region is generally referred to as the "red edge".It represents the region of abrupt change in leaf reflectance between 680 and 780nm caused by the combined effects of strong chlorophyll absorption in the red wavelengths and high reflectance in the NIR wavelengths due to leaf internal scattering.In this article,the first derivative of red edge spectra（Dred） was analyzed for winter wheat with different stripe rust disease severity.Results indicated Dred had characteristic of multi-peak;for diseased wheat,the front peak（700nm） was more dominant and the back peak（725—735nm） was more flat;red edge position（REP） shifted towards shorter wavelength;both maximum value of Dred（Dr） and sum of Dred（Sr） decreased.Two new red-edge indices（DSr,Ar） were proposed based on the difference in the shape of Dred between healthy and diseased wheat.DSr,an index describing thinness of Dred,was defined by the ratio of Dr and Sr.When winter wheat is healthy（diseased）,Dred is very steep（flat） and hence DSr is very large（small）.Ar,an index describing asymmetry of Dred,was defined as（S2-S1）/（S2+S1）,where S1 and S2 was the sum of Dred in 680—700 nm and 701—775 nm respectively,which corresponded to the spectral region adjacent to front peak and back peak.When winter wheat is healthy（diseased）,the difference between S2 and S1 is very significant（non-significant） and hence Ar is very large（small）.Compared to other conventional red edge indices,DSr and Ar can predict stripe rust disease severity more accurately.关键词：hyperspectral;red-edge;stripe rust;disease index
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发布时间：2021-06-10 - 摘要：The mechanism of remote sensing detecting the hydrocarbon microseepage is its geochemical abnormality.Referencing the geochemical models of hydrocarbon microseepage suggested by Schumacher（1996） and Saunders et al（1999）,this study implements ferrics index（TM3/TM1）,ferrous index（TM5/TM4） and clay index（TM5/TM7） to detect the abnormal areas of ferric-oxides,ferrous-oxides/organics,and clayzation/carbonation,respectively,in the east Yimeng Uplift of Ordos Basin.Integrated analysis the ferrous index,mineral composition,aeroradiometric data（U,Th and K）,and thorium-normalized uranium index（Ud）,two hydrocarbon microseepage belts in this area have been extracted.One is located in the northern Dongsheng which is along a nearly east-westward fault zone and Kubuqi desert.Its distribution is consistent with the ranger reported by the oil and gas investigation.The other one,named Pohaizi-Zhunzhao-Xinmiao zone,is located in the southern Dongsheng uranium mineralization zone and its northern boundary is along the northwestward Pohaizi-Zhunzhao fault.On the cross profiles of Pohaizi-Zhunzhao-Xinmiao zone,it is clear that the ferrous index（Fe2+）,aeroradiometric uranium（U）,and thorium-normalized uranium（Ud） are positive abnormality,and the ferric index（Fe3+）,aeroradiometric total channel（Tc） and potassium（K）,thorium-normalized potassium（Kd） are negative abnormality.The study suggests that the subaerial reduced areas are related to hydrocarbon microseepage and the hydrocarbon migration along the fault and fracture zone or penetrable strata played an important role for uranium mineralization in Zhiluo Formation near the northwestward fault zone.关键词：Ordos basin;Yimeng uplift;hydrocarbon microseepage;remote sensing detecting;uraniun mineralization
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发布时间：2021-06-10 - 摘要：Now there are three methods for land surface temperature retrieval from single channel thermal infrared remote sensing data,that is,atmospheric correction,mono-window algorithm and single-channel algorithm.The atmospheric parameters are required while using these methods for retrieving land surface temperature.Generally,the atmospheric parameters are estimated by using the atmospheric water vapor or humidity and average air temperature above near surface（2 meters’ height）.This method can only obtain data from several points.In this paper,the distribution of atmospheric water vapor content was calculated by two channel ratio method based on MODIS near infrared data.Then,land surface temperature was retrieved by using the thermal infrared band of Landsat ETM+ and a generalized single-channel method developed by Jiménez-Muoz and Sobrino.The exploited approach was applied to Changsha and Wuhan city.The results show that the combination of multi-source remote sensing data is effective for retrieving urban land surface temperature and can yield a reasonable estimation of land surface temperature.关键词：multi-source remote sensing data;land surface temperature;atmospheric water vapor content;MODIS;Landsat ETM+
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发布时间：2021-06-10 - 摘要：Successful forest resources monitoring requires an effective method to collect information related to forest area and tree crown.Current field-based assessment methodology provides the needed information,but is costly,and therefore assessment frequency is low.High-resolution remote sensing techniques provide a potentially low-cost alternative to field-based assessment.This paper proposed a method of monitoring survival rate and growth condition of economic forests in the farmland being returned to forests by using high resolution Quickbird imagery in Zhangjiakou,Hebei province.Multi-resolution image segmentation and object-oriented image analysis approaches were used for extracting farmland being returned to forests and tree crown information from Quickbird data.Firstly,tree crown classifictation was conducted using object-oriented image analysis,then,a program was developed to acquire tree crown factors and survival rate the classified from tree crown map.Finally,accuracy assessment was done according to field survey data,the results showed that the average error of tree crown size was as follows: 0.337m in east-west,0.433m in south-north,the accuracy of survival rate was 89.837%.The study provides a scientific basis for planning,management and decision support of the project of returning farmland to forests.关键词：high resolution remote sensing;Quickbird imagery;returning farmland to forests;object-oriented image analysis;Tree crown;survival rate
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发布时间：2021-06-10 - 摘要：This paper analyzed the possibility of reflectance spectra obtained under laboratory conditions for the assessment of Pb,Cd and Hg content in soil quickly.Besides original spectra（R）,several spectral indices were also calculated: first derivative reflectance spectra（FDR）,inverse-log spectra（lg（1/R）） and band depth（BD）.Partial least square regression（PLSR） was used to develop calibrations between spectral indices data and content of soil elements.Coefficient of determination（R） and root-mean-square error（RMSE） were used as criteria for best model.The results show that: 1) lg（1/R） is the best index to estimate soil heavy metal content,especially for Cd（R=0.8221） and Pb（R=0.8612）;2) The mechanism for estimating soil heavy metal element content by VIS-NIR-SWIR spectra is the absorption function of organic matter,iron-manganese oxide,and clay minerals;3) Simulated multi-spectral data have the good ability to estimate soil heavy metal elements content.While satisfactory results are obtained by laboratory spectra,more factors should be considered when using field data even satellite data.关键词：Reflectance spectra;spectral indices;soil;heavy metal
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发布时间：2021-06-10 - 摘要：In urbanization process,greater consideration of the manner in which rural lands are developed to urban lands will become progressively more important.Removal of rural land cover types such as soil,water,and vegetation and their replacement with common urban impervious surface materials such as asphalt,concrete,and metal have significant environmental implications.Impervious surfaces are anthropogenic features through which water cannot infiltrate into the soil,existed in roads,driveways,sidewalks,parking lots,rooftops,and so on.To estimate urban impervious surface distribution,a major component of the vegetation-impervious surface-soil（V-I-S） model,is important in monitoring urban eco-environment,such as reduction in evapotranspiration,promotion of more rapid surface run-off,increased storage and transfer of sensible heat,and reduction of air and water quality.The conceptual V-I-S model may be implemented by using the technique of linear spectral mixture analysis（LSMA）,which decomposes the spectral reflectance of a pixel into different proportions.LSMA is regarded as a physically-based image processing tool that supports repeatable and accurate extraction of quantitative subpixel information.In this paper,impervious surface distribution,together with vegetation and soil cover,is estimated through a constrained linear spectral mixture model using Landsat ETM+ data within the metropolitan area of Shanghai city in China.Four endmembers,low albedo,high albedo,vegetion,and soil are selected to model complicated urban land cover.Impervious surface fraction is obtained by adding low and high albedo endmembers fraction.Estimation accuracy is assessed using root mean square（RMS） error and color aerial photography.The overall root mean square error is 0.71%.Results indicate that impervious surface distribution can be derived from remotely sensed imagery with promising accuracy.Then the spatial pattern of impervious surface fraction in central area of Shanghai is analyzed.The spatial pattern of impervious surface discloses urban framework and the characters of urban sprawling.关键词：V-I-S;spectral mixture analysis;impervious surface;Shanghai
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发布时间：2021-06-10 - 摘要：Artemisia ordosica community coverage is a direct index to estimate the desertification severity in Mu Us Sandland.Acquiring its information is beneficial to carry out desertification monitoring and evaluation better.In this paper,we utilize spectral mixture analysis to retrieve Artemisia ordosica coverage information,based on Landsat ETM+ image.Some key issues in SMA process,including image pre-processing,endmember selection,spectral mixture model selection and SMA results analysis,are discussed in depth,and the suitable solutions are provided.Then the coverage of Artemisia ordosica community is retrieved,and the accuracy of the result is validated based on field survey data.The results show A significant linear relationship is found between Artemisia ordosica community fraction and measured Artemisia ordosica community coverage（the correlation coefficient is 0.88）.So,Artemisia ordosica community coverage of the research region can be acquired though linear transformation of Artemisia ordosica community fraction.Therefore,SMA is an effective technology for retrieving Artemisia ordosica community coverage accurately in Mu Us Sandland.关键词：Artemisia ordosica;spectral mixture analysis;endmember;Mu Us Sandland
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发布时间：2021-06-10 - 摘要：Regional dynamic monitoring is gaining rising interests in landuse/land cover study.In this article,a short-term land use/land cover change detection method was proposed,which takes periodic change of land cove into account and performs change detection between simulated image and actual image.Eight scenes of Radarsat image of Pearl River Delta was used for experiment.First,periodogram analysis was carried out on the time-series data to get the temporal pattern of the study area.Some land cover like paddy,cultivated land,orchard and forest reveal periodic variation during the research span.Thus various temporal dynamics of these land covers should be taken into account to acquire accurate short-term change detection.Then,a time-based neural networkprediction model（TNN） was built for time-series forecasting.Ten types of land cover with different temporal pattern were classified and four scenes of Radarsat images in vegetation growing seasons（April,June,August,October） in 2000 were used for network training.Land-cover type was classified based on their temporal variation.The first three scenes were used as the input and the last scene was used as the output（to be predicted）.The training result showed stable and precise simulation of TNN.In the third step,the first three scenes of Radarsat images in 2001 was taken as the input to the network and the forth scene was simulated.Finally,a distance function was defined and change threshold was set for change detection.The simulated result was used to detect the change between simulated image and actual image.The detection assessment shows that neural network simulation could well represent the short-time non-linear change of land use/land cover.The detection precision ranged from 66.67%（rural residential area） to 91.67%（water）.Other land cover type like paddy field（83.33%） and orchard（71.43%） also got relatively high precision,corresponding to their notable variation in time-series images.The average detection precision reached 81.66%,which is a satisfying result for our primary experiment on short time change detection.To sum up,this article testified the possibility of short-term change detection under dynamic cally changing environment.So far there is still few method applicable for short-term change detection.TNN network proposed in this article is a meaningful attempt for research in this field.关键词：Radarsat;land use/land cover change;neural network;change detection;time series analysis
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发布时间：2021-06-10
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