Multi-feature-based classification method using random forest and superpixels for polarimetric SAR images

Qiao XU; Xiao ZHANG; Shaohuai YU; Qihao CHEN; Xiuguo LIU

doi:10.11834/jrs.20197475

Views : 0 下载量: 998 CSCD: 10 更多指标

PDF
Export
Share
Collection
Album

Multi-feature-based classification method using random forest and superpixels for polarimetric SAR images
Vol. 23, Issue 4, Pages: 685-694(2019)
Published： 2019-7 ，

Accepted： 19 March 2018
DOI： 10.11834/jrs.20197475

扫描看全文

徐乔, 张霄, 余绍淮, 陈启浩, 刘修国. 2019. 综合多特征的极化SAR图像随机森林分类算法. 遥感学报, 23(4): 685–694

Xu Q, Zhang X, Yu S H, Chen Q H and Liu X G. 2019. Multi-feature-based classification method using random forest and superpixels for polarimetric SAR images. Journal of Remote Sensing, 23(4): 685–694
徐乔, 张霄, 余绍淮, 陈启浩, 刘修国. 2019. 综合多特征的极化SAR图像随机森林分类算法. 遥感学报, 23(4): 685–694 DOI： 10.11834/jrs.20197475.

Xu Q, Zhang X, Yu S H, Chen Q H and Liu X G. 2019. Multi-feature-based classification method using random forest and superpixels for polarimetric SAR images. Journal of Remote Sensing, 23(4): 685–694 DOI： 10.11834/jrs.20197475.

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。

Abstract

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.

关键词

遥感极化SAR分类多特征随机森林超像素概率松弛算法

Keywords

remote sensingpolarimetric SARclassificationmultifeaturerandom forestsuperpixelsprobabilistic label relaxation

references

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

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. IEEE Transactions on Geoscience and Remote Sensing, 51(4): 2442–2453

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. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1): 2–16

Breiman L. 2001. Random forests. Machine Learning, 45(1): 5–32

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

Haddadi G A, Sahebi M R and Mansourian A. 2011. Polarimetric SAR feature selection using a genetic algorithm. Canadian Journal of Remote Sensing, 37(1): 27–36

Lee J S, Grunes M R and Pottier E. 2001. Quantitative comparison of classification capability: fully polarimetric versus dual and single-polarization SAR. IEEE Transactions on Geoscience and Remote Sensing, 39(11): 2343–2351

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

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

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

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

Qin F C, Guo J M and Lang F K. 2015. Superpixel segmentation for polarimetric SAR imagery using local iterative clustering. IEEE Geoscience and Remote Sensing Letters, 12(1): 13–17

Reigber A, Jäger 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

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

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

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

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

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

Wu Y H, Ji K F, Yu W X and Su Y. 2008. Region-based classification of polarimetric SAR images using Wishart MRF. IEEE Geoscience and Remote Sensing Letters, 5(4): 668–672

Alert me when the article has been cited

提交

Class-independent domain adaptation for hyperspectral image classification

Improving the quality of remotely sensed precipitation product from GPM satellites by using a spatial random forest

Remote sensing extraction of paddy rice in Northeast China from GF-6 images by combining feature optimization and random forest

Estimation of fractional woody and herbaceous vegetation cover in Temperate Sparse Forest Grassland using fusion of UAV and Satellite imagery

Net primary productivity estimation of Dongting Lake wetland