综合多特征的极化SAR图像随机森林分类算法
|
摘要
为抑制相干斑噪声对极化SAR图像分类结果的干扰,本文提出一种综合多特征的极化SAR图像随机森林分类方法。该方法首先利用简单线性迭代聚类(SLIC)算法生成超像素作为分类单元;然后,基于高维极化特征图像,利用训练好的随机森林模型,统计决策树的分类投票数,计算各超像素的类别概率;最后,利用超像素间的空间邻域特征,采用概率松弛算法(PLR)迭代修正超像素的类别后验概率,并依据最大后验概率(MAP)准则得到分类结果;实现综合利用超像素和空间邻域特征,降低相干斑噪声干扰的极化SAR图像分类方法。实验对比结果表明:本文方法能得有效抑制极化SAR图像中相干斑噪声的干扰,得到高精度且光滑连续的分类结果。
The classification technique plays an important role in the analysis of polarimetric synthetic aperture radar(PolSAR) images.PolSAR image classification is widely used in information extraction and scene interpretation or is performed as a preprocessing step for further applications. However, speckle noise appears in PolSAR images because of the coherent interference of waves reflected from elementary scatters. Such inherent speckle noise degrades the classification performance and brings difficulty for PolSAR image classification.Therefore, a novel supervised multi-feature-based classification method was proposed in this study.This method combined polarimetric signature information and spatial context information based on the random forest model. First, a modified simple linear iterative clustering algorithm was utilized to generate superpixels as classification elements by using the Pauli RGB image, which helped reduce speckle noise interference. Second, a high-dimensional polarimetric SAR feature image was constructed by collecting various polarimetric signatures generated by polarimetric decomposition and algebra operations. Then, the random forest model was trained on the basis of the PolSAR feature image by using training samples, and the number of classification votes of each decision tree in the random forest for each pixel was counted to compute the class probabilities of the superpixels. Finally, a neighborhood function was defined to express the spatial relationship among adjacent superpixels quantitatively, and the class probabilities of the superpixels were recalculated by the predefined neighborhood function in a Probabilistic Label Relaxation(PLR) procedure to reduce the interference of speckle noise. The final classification result was obtained by the maximum a posteriori decision rule when the iteration of PLR was terminated.Comparative experiments using different RADARSAT-2 images were conducted to evaluate the validity and applicability of the proposed method. The proposed approach achieved the highest accuracy(94.39% on the Flevoland RADARSAT-2 image and 85.09% on the Wuhan RADARSAT-2 image) and generated accurate and consistent classification results for the experimental images, which was considerably improved compared with those of other methods.Therefore, the proposed method can effectively suppress the interference of speckle noise by using superpixels and spatial context information and obtain accurate and consistent classification results for PolSAR images.
The classification technique plays an important role in the analysis of polarimetric synthetic aperture radar(PolSAR) images.PolSAR image classification is widely used in information extraction and scene interpretation or is performed as a preprocessing step for further applications. However, speckle noise appears in PolSAR images because of the coherent interference of waves reflected from elementary scatters. Such inherent speckle noise degrades the classification performance and brings difficulty for PolSAR image classification.Therefore, a novel supervised multi-feature-based classification method was proposed in this study.This method combined polarimetric signature information and spatial context information based on the random forest model. First, a modified simple linear iterative clustering algorithm was utilized to generate superpixels as classification elements by using the Pauli RGB image, which helped reduce speckle noise interference. Second, a high-dimensional polarimetric SAR feature image was constructed by collecting various polarimetric signatures generated by polarimetric decomposition and algebra operations. Then, the random forest model was trained on the basis of the PolSAR feature image by using training samples, and the number of classification votes of each decision tree in the random forest for each pixel was counted to compute the class probabilities of the superpixels. Finally, a neighborhood function was defined to express the spatial relationship among adjacent superpixels quantitatively, and the class probabilities of the superpixels were recalculated by the predefined neighborhood function in a Probabilistic Label Relaxation(PLR) procedure to reduce the interference of speckle noise. The final classification result was obtained by the maximum a posteriori decision rule when the iteration of PLR was terminated.Comparative experiments using different RADARSAT-2 images were conducted to evaluate the validity and applicability of the proposed method. The proposed approach achieved the highest accuracy(94.39% on the Flevoland RADARSAT-2 image and 85.09% on the Wuhan RADARSAT-2 image) and generated accurate and consistent classification results for the experimental images, which was considerably improved compared with those of other methods.Therefore, the proposed method can effectively suppress the interference of speckle noise by using superpixels and spatial context information and obtain accurate and consistent classification results for PolSAR images.
引文
Achanta R,Shaji A,Smith K,Lucchi A,Fua P and Süsstrunk S.2012.SLIC superpixels compared to state-of-the-art superpixel methods.IEEE Transactions on Pattern Analysis and Machine Intelligence,34(11):2274-2282[DOI:10.1109/TPAMI.2012.120]
Akbari V,Doulgeris A P,Moser G,Eltoft T,Anfinsen S N and Serpico S B.2013.A textural-contextual model for unsupervised segmentation of multipolarization synthetic aperture radar images.IEEETransactions on Geoscience and Remote Sensing,51(4):2442-2453[DOI:10.1109/TGRS.2012.2211367]
Benz U and Pottier E.2001.Object based analysis of polarimetric sar data in alpha-entropy-anisotropy decomposition using fuzzy classification by ecognition//Proceedings of International Geoscience and Remote Sensing Symposium.Sydney,NSW,Australia:IEEE:1427-1429[DOI:10.1109/IGARSS.2001.976867]
Blaschke T.2010.Object based image analysis for remote sensing.IS-PRS Journal of Photogrammetry and Remote Sensing,65(1):2-16[DOI:10.1016/j.isprsjprs.2009.06.004]
Breiman L.2001.Random forests.Machine Learning,45(1):5-32[DOI:10.1023/A:1010933404324]
Chen L J,Yang W,Liu Y and Sun H.2010.Feature evaluation and selection for polarimetric SAR image classification//Proceedings of the IEEE 10th International Conference on Signal.Beijing,China:IEEE:2202-2205[DOI:10.1109/ICOSP.2010.5656765]
Feng J L,Cao Z J and Pi Y M.2014.Polarimetric contextual classification of PolSAR images using sparse representation and superpixels.Remote Sensing,6(8):7158-7181[DOI:10.3390/rs6087158]
Haddadi G A,Sahebi M R and Mansourian A.2011.Polarimetric SARfeature selection using a genetic algorithm.Canadian Journal of Remote Sensing,37(1):27-36[DOI:10.5589/m11-013]
Lee J S,Grunes M R and Pottier E.2001.Quantitative comparison of classification capability:fully polarimetric versus dual and singlepolarization SAR.IEEE Transactions on Geoscience and Remote Sensing,39(11):2343-2351[DOI:10.1109/36.964970]
Liu B,Hu H,Wang H Y,Wang K Z,Liu X Z and Yu W X.2013.Superpixel-based classification with an adaptive number of classes for polarimetric SAR images.IEEE Transactions on Geoscience and Remote Sensing,51(2):907-924[DOI:10.1109/TGRS.2012.2203358]
Liu H Y,Zhu D X,Yang S Y,Hou B,Gou S P,Xiong T and Jiao L C.2016.Semisupervised feature extraction with neighborhood constraints for polarimetric SAR classification.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,9(7):3001-3015[DOI:10.1109/JSTARS.2016.2532922]
Maghsoudi Y,Collins M J and Leckie D G.2013.RADARSAT-2 polarimetric SAR data for boreal forest classification using SVM and a wrapper feature selector.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,6(3):1531-1538[DOI:10.1109/JSTARS.2013.2259219]
Niu X and Ban Y F.2012.An adaptive contextual sem algorithm for urban land cover mapping using multitemporal high-resolution polarimetric SAR data.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,5(4):1129-1139[DOI:10.1109/JSTARS.2012.2201448]
Qin F C,Guo J M and Lang F K.2015.Superpixel segmentation for polarimetric SAR imagery using local iterative clustering.IEEEGeoscience and Remote Sensing Letters,12(1):13-17[DOI:10.1109/LGRS.2014.2322960]
Reigber A,J?ger M,Neumann M and Ferro-Famil L.2010.Classifying polarimetric SAR data by combining expectation methods with spatial context.International Journal of Remote Sensing,31(3):727-744[DOI:10.1080/01431160902897809]
Richards J A and Jia X P.2007.A dempster-shafer relaxation approach to context classification.IEEE Transactions on Geoscience and Remote Sensing,45(5):1422-1431[DOI:10.1109/TGRS.2007.893821]
Song H,Yang W,Bai Y and Xu X.2015.Unsupervised classification of polarimetric SAR imagery using large-scale spectral clustering with spatial constraints.International Journal of Remote Sensing,36(11):2816-2830[DOI:10.1080/01431161.2015.1043759]
Sun X,Huang P P,Tu S T and Yang X L.2016.Polarimetric SAR image classification using multiple-feature fusion and ensemble learning.Journal of Radars,5(6):692-700(孙勋,黄平平,涂尚坦,杨祥立.2016.利用多特征融合和集成学习的极化SAR图像分类.雷达学报,5(6):692-700)[DOI:10.12000/JR15132]
Wang W S,Yang X F,Li X F,Chen K S,Liu G H,Li Z W and Gade M.2017.A fully polarimetric sar imagery classification scheme for mud and sand flats in intertidal zones.IEEE Transactions on Geoscience and Remote Sensing,55(3):1734-1742[DOI:10.1109/TGRS.2016.2631632]
Wu Y H,Ji K F,Yu W X and Su Y.2008.Region-based classification of polarimetric SAR images using Wishart MRF.IEEEGeoscience and Remote Sensing Letters,5(4):668-672[DOI:10.1109/LGRS.2008.2002263]
Akbari V,Doulgeris A P,Moser G,Eltoft T,Anfinsen S N and Serpico S B.2013.A textural-contextual model for unsupervised segmentation of multipolarization synthetic aperture radar images.IEEETransactions on Geoscience and Remote Sensing,51(4):2442-2453[DOI:10.1109/TGRS.2012.2211367]
Benz U and Pottier E.2001.Object based analysis of polarimetric sar data in alpha-entropy-anisotropy decomposition using fuzzy classification by ecognition//Proceedings of International Geoscience and Remote Sensing Symposium.Sydney,NSW,Australia:IEEE:1427-1429[DOI:10.1109/IGARSS.2001.976867]
Blaschke T.2010.Object based image analysis for remote sensing.IS-PRS Journal of Photogrammetry and Remote Sensing,65(1):2-16[DOI:10.1016/j.isprsjprs.2009.06.004]
Breiman L.2001.Random forests.Machine Learning,45(1):5-32[DOI:10.1023/A:1010933404324]
Chen L J,Yang W,Liu Y and Sun H.2010.Feature evaluation and selection for polarimetric SAR image classification//Proceedings of the IEEE 10th International Conference on Signal.Beijing,China:IEEE:2202-2205[DOI:10.1109/ICOSP.2010.5656765]
Feng J L,Cao Z J and Pi Y M.2014.Polarimetric contextual classification of PolSAR images using sparse representation and superpixels.Remote Sensing,6(8):7158-7181[DOI:10.3390/rs6087158]
Haddadi G A,Sahebi M R and Mansourian A.2011.Polarimetric SARfeature selection using a genetic algorithm.Canadian Journal of Remote Sensing,37(1):27-36[DOI:10.5589/m11-013]
Lee J S,Grunes M R and Pottier E.2001.Quantitative comparison of classification capability:fully polarimetric versus dual and singlepolarization SAR.IEEE Transactions on Geoscience and Remote Sensing,39(11):2343-2351[DOI:10.1109/36.964970]
Liu B,Hu H,Wang H Y,Wang K Z,Liu X Z and Yu W X.2013.Superpixel-based classification with an adaptive number of classes for polarimetric SAR images.IEEE Transactions on Geoscience and Remote Sensing,51(2):907-924[DOI:10.1109/TGRS.2012.2203358]
Liu H Y,Zhu D X,Yang S Y,Hou B,Gou S P,Xiong T and Jiao L C.2016.Semisupervised feature extraction with neighborhood constraints for polarimetric SAR classification.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,9(7):3001-3015[DOI:10.1109/JSTARS.2016.2532922]
Maghsoudi Y,Collins M J and Leckie D G.2013.RADARSAT-2 polarimetric SAR data for boreal forest classification using SVM and a wrapper feature selector.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,6(3):1531-1538[DOI:10.1109/JSTARS.2013.2259219]
Niu X and Ban Y F.2012.An adaptive contextual sem algorithm for urban land cover mapping using multitemporal high-resolution polarimetric SAR data.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,5(4):1129-1139[DOI:10.1109/JSTARS.2012.2201448]
Qin F C,Guo J M and Lang F K.2015.Superpixel segmentation for polarimetric SAR imagery using local iterative clustering.IEEEGeoscience and Remote Sensing Letters,12(1):13-17[DOI:10.1109/LGRS.2014.2322960]
Reigber A,J?ger M,Neumann M and Ferro-Famil L.2010.Classifying polarimetric SAR data by combining expectation methods with spatial context.International Journal of Remote Sensing,31(3):727-744[DOI:10.1080/01431160902897809]
Richards J A and Jia X P.2007.A dempster-shafer relaxation approach to context classification.IEEE Transactions on Geoscience and Remote Sensing,45(5):1422-1431[DOI:10.1109/TGRS.2007.893821]
Song H,Yang W,Bai Y and Xu X.2015.Unsupervised classification of polarimetric SAR imagery using large-scale spectral clustering with spatial constraints.International Journal of Remote Sensing,36(11):2816-2830[DOI:10.1080/01431161.2015.1043759]
Sun X,Huang P P,Tu S T and Yang X L.2016.Polarimetric SAR image classification using multiple-feature fusion and ensemble learning.Journal of Radars,5(6):692-700(孙勋,黄平平,涂尚坦,杨祥立.2016.利用多特征融合和集成学习的极化SAR图像分类.雷达学报,5(6):692-700)[DOI:10.12000/JR15132]
Wang W S,Yang X F,Li X F,Chen K S,Liu G H,Li Z W and Gade M.2017.A fully polarimetric sar imagery classification scheme for mud and sand flats in intertidal zones.IEEE Transactions on Geoscience and Remote Sensing,55(3):1734-1742[DOI:10.1109/TGRS.2016.2631632]
Wu Y H,Ji K F,Yu W X and Su Y.2008.Region-based classification of polarimetric SAR images using Wishart MRF.IEEEGeoscience and Remote Sensing Letters,5(4):668-672[DOI:10.1109/LGRS.2008.2002263]
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |