A supervised polarimetric SAR land cover classification method was proposed based on the Fisher linear discriminant.The feature parameters used in this classification method could be selected flexibly according to lan...A supervised polarimetric SAR land cover classification method was proposed based on the Fisher linear discriminant.The feature parameters used in this classification method could be selected flexibly according to land covers to be classified.Polarimetric and texture feature parameters extracted from co-registered multifrequency and multi-temporal polarimetric SAR data could be combined together for classification use,without consideration of the dimension difference of each feature parameter and the joint probability density function of those parameters.Experimental result with AGRSAR L/C-band full polarimetric SAR data showed that a total classification accuracy of 94.33% was achieved by combining the polarimetric with texture feature parameters extracted from L/C dual band SAR data,demonstrating the effectiveness of this method.展开更多
In this paper,a new decomposition method is proposed to solve the problems that vegetation component is overestimated and is not sensitive to directional scattering features with traditional polarimetric Synthetic Ape...In this paper,a new decomposition method is proposed to solve the problems that vegetation component is overestimated and is not sensitive to directional scattering features with traditional polarimetric Synthetic Aperture Radar(SAR)decomposition.It uses a Polarimetric Interferometric Similarity Parameter(PISP)calculated from Polarimetric SAR Interferometry(PolInSAR)datasets to the scattering decomposition.The PISP is proposed to reveal the geometric sensitivity of SAR interferometry.It is defined by three optimized mechanisms obtained from PolInSAR datasets,therefore,it not only relates to the coherent scattering mechanism closely,but also sufficiently uses the phase and amplitude information.The PISP of building is high,and forest’s PISP is low.The proposed method uses the PISP as a judge condition to select different vegetation model adaptively.The decomposition results show the proposed method can effectively solve the vegetation ingredients overestimation problem.In addition,it is sensitive to the directional scattering.展开更多
Compared to single-polarization synthetic aperture radar(SAR)data,fully polarimetric SAR data can provide more detailed information of the sea surface,which is important for applications such as shallow sea topography...Compared to single-polarization synthetic aperture radar(SAR)data,fully polarimetric SAR data can provide more detailed information of the sea surface,which is important for applications such as shallow sea topography detection.The Gaofen-3 satellite provides abundant polarimetric SAR data for ocean research.In this paper,a shallow sea topography detection method was proposed based on fully polarimetric Gaofen-3 SAR data.This method considers swell patterns and only requires SAR data and little prior knowledge of the water depth to detect shallow sea topography.Wave tracking was performed based on preprocessed fully polarimetric SAR data,and the water depth was then calculated considering the wave parameters and the linear dispersion relationships.In this paper,four study areas were selected for experiments,and the experimental results indicated that the polarimetric scattering parameterαhad higher detection accuracy than quad-polarization images.The mean relative errors were 14.52%,10.30%,12.56%,and 12.90%,respectively,in the four study areas.In addition,this paper also analyzed the detection ability of this model for different topographies,and the experiments revealed that the topography could be well recognized when the topography gradient is small,the topography gradient direction is close to the wave propagation direction,and the isobath line is regular.展开更多
A novel land cover classification procedure is presented utilizing the infor</span><span style="font-family:Verdana;">mation content of fully polarimetric SAR images. The Cameron cohere</span&...A novel land cover classification procedure is presented utilizing the infor</span><span style="font-family:Verdana;">mation content of fully polarimetric SAR images. The Cameron cohere</span><span style="font-family:Verdana;">nt target decomposition (CTD) is employed to characterize land cover pixel by pixel. Cameron’s CTD is employed since it provides a complete set of elem</span><span style="font-family:Verdana;">entary scattering mechanisms to describe the physical properties of t</span><span style="font-family:Verdana;">he scatterer. The novelty of the proposed land classification approach lies on the fact that the features used for classification are not the types of the elementary </span><span style="font-family:Verdana;">scatterers themselves, but the way these types of scatterers alternate from p</span><span style="font-family:Verdana;">ixel </span><span style="font-family:Verdana;">to pixel on the SAR image. Thus, transition matrices that represent loc</span><span style="font-family:Verdana;">al Markov models are used as classification features for land cover classification. The classification rule employs only the most important transitions for decision making. The Frobenius inner product is employed as similarity measure. Ten different types of land cover are used for testing the proposed method. In this aspect, the classification performance is significantly high.展开更多
Simulated Annealing (SA) is used in this work as a global optimization technique applied in discrete search spaces in order to change the characterization of pixels in a Polarimetric Synthetic Aperture Radar (PolSAR) ...Simulated Annealing (SA) is used in this work as a global optimization technique applied in discrete search spaces in order to change the characterization of pixels in a Polarimetric Synthetic Aperture Radar (PolSAR) image which have been classified with different label than the surrounding land cover type. Accordingly, Land Cover type classification is achieved with high reliability. For this purpose, an energy function is employed which is minimized by means of SA when the false classified pixels are correctly labeled. All PolSAR pixels are initially classified using 9 specifically selected types of land cover by means of Google Earth maps. Each Land Cover Type is represented by a histogram of the 8 Cameron’s elemental scatterers by means of coherent target decomposition (CTD). Each PolSAR pixel is categorized according to the local histogram of the elemental scatterers. SA is applied in the discreet space of nine land cover types. Classification results prove that the Simulated Annealing approach used is very successful for correctly separating regions with different Land Cover Types.展开更多
A land cover classification procedure is presented utilizing the information content of fully polarimetric SAR images. The Cameron coherent target decomposition (CTD) is employed to characterize each pixel, using a se...A land cover classification procedure is presented utilizing the information content of fully polarimetric SAR images. The Cameron coherent target decomposition (CTD) is employed to characterize each pixel, using a set of canonical scattering mechanisms in order to describe the physical properties of the scatterer. The novelty of the proposed classification approach lies on the use of Hidden Markov Models (HMM) to uniquely characterize each type of land cover. The motivation to this approach is the investigation of the alternation between scattering mechanisms from SAR pixel to pixel. Depending </span><span style="font-family:Verdana;">on the observations-scattering mechanisms and exploiting the transitions </span><span style="font-family:Verdana;">between the scattering mechanisms we decide upon the HMM-land cover type. The classification process is based on the likelihood of observation sequences </span><span style="font-family:Verdana;">been evaluated by each model. The performance of the classification ap</span><span style="font-family:Verdana;">proach is assessed my means of fully polarimetric SLC SAR data from the broader </span><span style="font-family:Verdana;">area of Vancouver, Canada and was found satisfactory, reaching a success</span><span style="font-family:Verdana;"> from 87% to over 99%.展开更多
Since its first flight in 2007,the UAVSAR instrument of NASA has acquired a large number of fully Polarimetric SAR(PolSAR)data in very high spatial resolution.It is possible to observe small spatial features in this t...Since its first flight in 2007,the UAVSAR instrument of NASA has acquired a large number of fully Polarimetric SAR(PolSAR)data in very high spatial resolution.It is possible to observe small spatial features in this type of data,offering the opportunity to explore structures in the images.In general,the structured scenes would present multimodal or spiky histograms.The finite mixture model has great advantages in modeling data with irregular histograms.In this paper,a type of important statistics called log-cumulants,which could be used to design parameter estimator or goodness-of-fit tests,are derived for the finite mixture model.They are compared with logcumulants of the texture models.The results are adopted to UAVSAR data analysis to determine which model is better for different land types.展开更多
基金Supported by ESA-MOST Dragon 2 Cooperation Programme (5344)the National High-Tech R&D Program("863"Program)(2011AA120401)the National Natural Science Foundation of China(60890071,60890072)
文摘A supervised polarimetric SAR land cover classification method was proposed based on the Fisher linear discriminant.The feature parameters used in this classification method could be selected flexibly according to land covers to be classified.Polarimetric and texture feature parameters extracted from co-registered multifrequency and multi-temporal polarimetric SAR data could be combined together for classification use,without consideration of the dimension difference of each feature parameter and the joint probability density function of those parameters.Experimental result with AGRSAR L/C-band full polarimetric SAR data showed that a total classification accuracy of 94.33% was achieved by combining the polarimetric with texture feature parameters extracted from L/C dual band SAR data,demonstrating the effectiveness of this method.
文摘In this paper,a new decomposition method is proposed to solve the problems that vegetation component is overestimated and is not sensitive to directional scattering features with traditional polarimetric Synthetic Aperture Radar(SAR)decomposition.It uses a Polarimetric Interferometric Similarity Parameter(PISP)calculated from Polarimetric SAR Interferometry(PolInSAR)datasets to the scattering decomposition.The PISP is proposed to reveal the geometric sensitivity of SAR interferometry.It is defined by three optimized mechanisms obtained from PolInSAR datasets,therefore,it not only relates to the coherent scattering mechanism closely,but also sufficiently uses the phase and amplitude information.The PISP of building is high,and forest’s PISP is low.The proposed method uses the PISP as a judge condition to select different vegetation model adaptively.The decomposition results show the proposed method can effectively solve the vegetation ingredients overestimation problem.In addition,it is sensitive to the directional scattering.
基金The National Natural Science Foundation of China under contract Nos 51839002 and U2006207.
文摘Compared to single-polarization synthetic aperture radar(SAR)data,fully polarimetric SAR data can provide more detailed information of the sea surface,which is important for applications such as shallow sea topography detection.The Gaofen-3 satellite provides abundant polarimetric SAR data for ocean research.In this paper,a shallow sea topography detection method was proposed based on fully polarimetric Gaofen-3 SAR data.This method considers swell patterns and only requires SAR data and little prior knowledge of the water depth to detect shallow sea topography.Wave tracking was performed based on preprocessed fully polarimetric SAR data,and the water depth was then calculated considering the wave parameters and the linear dispersion relationships.In this paper,four study areas were selected for experiments,and the experimental results indicated that the polarimetric scattering parameterαhad higher detection accuracy than quad-polarization images.The mean relative errors were 14.52%,10.30%,12.56%,and 12.90%,respectively,in the four study areas.In addition,this paper also analyzed the detection ability of this model for different topographies,and the experiments revealed that the topography could be well recognized when the topography gradient is small,the topography gradient direction is close to the wave propagation direction,and the isobath line is regular.
文摘A novel land cover classification procedure is presented utilizing the infor</span><span style="font-family:Verdana;">mation content of fully polarimetric SAR images. The Cameron cohere</span><span style="font-family:Verdana;">nt target decomposition (CTD) is employed to characterize land cover pixel by pixel. Cameron’s CTD is employed since it provides a complete set of elem</span><span style="font-family:Verdana;">entary scattering mechanisms to describe the physical properties of t</span><span style="font-family:Verdana;">he scatterer. The novelty of the proposed land classification approach lies on the fact that the features used for classification are not the types of the elementary </span><span style="font-family:Verdana;">scatterers themselves, but the way these types of scatterers alternate from p</span><span style="font-family:Verdana;">ixel </span><span style="font-family:Verdana;">to pixel on the SAR image. Thus, transition matrices that represent loc</span><span style="font-family:Verdana;">al Markov models are used as classification features for land cover classification. The classification rule employs only the most important transitions for decision making. The Frobenius inner product is employed as similarity measure. Ten different types of land cover are used for testing the proposed method. In this aspect, the classification performance is significantly high.
文摘Simulated Annealing (SA) is used in this work as a global optimization technique applied in discrete search spaces in order to change the characterization of pixels in a Polarimetric Synthetic Aperture Radar (PolSAR) image which have been classified with different label than the surrounding land cover type. Accordingly, Land Cover type classification is achieved with high reliability. For this purpose, an energy function is employed which is minimized by means of SA when the false classified pixels are correctly labeled. All PolSAR pixels are initially classified using 9 specifically selected types of land cover by means of Google Earth maps. Each Land Cover Type is represented by a histogram of the 8 Cameron’s elemental scatterers by means of coherent target decomposition (CTD). Each PolSAR pixel is categorized according to the local histogram of the elemental scatterers. SA is applied in the discreet space of nine land cover types. Classification results prove that the Simulated Annealing approach used is very successful for correctly separating regions with different Land Cover Types.
文摘A land cover classification procedure is presented utilizing the information content of fully polarimetric SAR images. The Cameron coherent target decomposition (CTD) is employed to characterize each pixel, using a set of canonical scattering mechanisms in order to describe the physical properties of the scatterer. The novelty of the proposed classification approach lies on the use of Hidden Markov Models (HMM) to uniquely characterize each type of land cover. The motivation to this approach is the investigation of the alternation between scattering mechanisms from SAR pixel to pixel. Depending </span><span style="font-family:Verdana;">on the observations-scattering mechanisms and exploiting the transitions </span><span style="font-family:Verdana;">between the scattering mechanisms we decide upon the HMM-land cover type. The classification process is based on the likelihood of observation sequences </span><span style="font-family:Verdana;">been evaluated by each model. The performance of the classification ap</span><span style="font-family:Verdana;">proach is assessed my means of fully polarimetric SLC SAR data from the broader </span><span style="font-family:Verdana;">area of Vancouver, Canada and was found satisfactory, reaching a success</span><span style="font-family:Verdana;"> from 87% to over 99%.
基金This work has been supported in part by the Shenzhen Science&Technology Program[grant number JSGG20150512145714247]the State Key Program of National Natural Science of China[grant number 61331016]National Key Research Plan of China[grant number 2016YFC0500201-07].
文摘Since its first flight in 2007,the UAVSAR instrument of NASA has acquired a large number of fully Polarimetric SAR(PolSAR)data in very high spatial resolution.It is possible to observe small spatial features in this type of data,offering the opportunity to explore structures in the images.In general,the structured scenes would present multimodal or spiky histograms.The finite mixture model has great advantages in modeling data with irregular histograms.In this paper,a type of important statistics called log-cumulants,which could be used to design parameter estimator or goodness-of-fit tests,are derived for the finite mixture model.They are compared with logcumulants of the texture models.The results are adopted to UAVSAR data analysis to determine which model is better for different land types.