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Robust Classification of PolSAR Images Based on Pinball loss Support Vector Machine
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摘要: 考虑到极化合成孔径雷达(PolSAR)图像标注信息量低以及相干斑噪声难以消除的问题，该文从鲁棒统计学习的角度提出了一种基于Pin-SVM的极化SAR图像鲁棒分类方法，根据极化SAR图像的散射特性和地物的纹理特性，通过求解两类样本之间的最大分位数距离来确定分类超平面，在无需迭代的前提下得到更加鲁棒的分类结果。相比传统的基于最大间隔的极化SAR图像分类算法，该文所提算法一方面在对极化SAR图像提取到的特征中包含的噪声具有更好的鲁棒性，另一方面对于训练样本的抽样范围不敏感，即重采样具有更好的鲁棒性。利用EMISAR的Foulum地区极化SAR数据进行了算法验证，多种情况的对比实验的结果验证了该算法的有效性。Abstract: Given the problems that the amount of supervised information in the Polarimetric Synthetic Aperture Radar (PolSAR) image is low and the speckle noise is difficult to eliminate, in this study, a robust classification algorithm for PolSAR image based on Pinball loss Support Vector Machine (Pin-SVM) is proposed from the perspective of robust statistical learning. On the basis of the scattering characteristics of PolSAR images and the texture characteristics of surface features, the proposed algorithm determines the optimal decision hyperplane by solving the maximum quantile distance between the samples of two classes, which can provide more robust results without iteration. Compared with the traditional PolSAR image classification algorithms that solve the maximum margin, on one hand, the proposed algorithm is robust to the noise contained in the features extracted from PolSAR images. On the other hand, the proposed algorithm is insensitive to the sampling range of training samples, which means that it has better robustness to resampling. The experimental results of EMISAR-Foulum PolSAR data prove the validity of the proposed algorithm through comparative tests in a variety of situations.
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图 1 0-1损失、Hinge损失与Pinball损失的对比
Figure 1. The form of 0-1 loss, Hinge loss and Pinball loss
图 2 不同超参数
$\tau $ 取值下Pin-SVM分类示意图Figure 2. Schematic diagram of Pin-SVM classification with different hyperparameter
$\tau $ 
图 3 基于Pin-SVM的极化SAR图像分类流程
Figure 3. Flowchart of PolSAR classification based on Pin-SVM
图 6 不同分类器对各地物分类精度的对比图
Figure 6. The comparison of the classification accuracy of different terrains
图 7 分类器预测的概率密度函数对比
Figure 7. Comparison of probability density functions for the prediction of classifiers
图 9 Pin-SVM的参数对分类精度的影响
Figure 9. The effect hyperparameters of Pin-SVM on classification accuracy
表 1 不同分类器对测试样本的分类精度(%)
Table 1. Classification accuracy comparison of different classifiers
分类器 地物类型 整体精度 建筑物 森林 裸地 细径作物 阔叶作物 Pin-SVM 94.3 90.7 95.1 82.3 94.1 91.3 C-SVM 91.0 80.6 92.3 80.1 89.7 86.7 Wishart 85.4 71.2 93.7 84.2 85.6 84.0 LSSVM 86.2 80.2 89.3 78.8 88.2 84.5 OPTELM 86.5 82.3 92.1 75.1 83.8 84.0 
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表 2 重采样实验结果
Table 2. Experimental results of the resampling
分类器 重采样 30次 50次 100次 150次 Pin-SVM w $8.96 \pm 1.11$ $8.69 \pm 0.99$ $8.57 \pm 0.96$ $8.63 \pm 0.95$ $\tau {\rm{ = }}1.0$ b $ - 5.91 \pm 0.62$ $ - 5.95 \pm 0.61$ $ - 5.93 \pm 0.59$ $ - 5.93 \pm 0.44$ Pin-SVM w $9.43 \pm 1.23$ $9.54 \pm 1.20$ $9.42 \pm 1.15$ $9.68 \pm 1.13$ $\tau {\rm{ = 0}}{\rm{.5}}$ b $ - 6.93 \pm 0.75$ $ - 7.02 \pm 0.71$ $ - 6.91 \pm 0.67$ $ - 6.85 \pm 0.59$ Pin-SVM w $11.43 \pm 1.63$ $11.22 \pm 1.47$ $11.09 \pm 1.46$ $11.31 \pm 1.44$ $\tau {\rm{ = 0}}{\rm{.2}}$ b $ - 8.53 \pm 0.84$ $ - 8.53 \pm 0.78$ $ - 8.41 \pm 0.75$ $ - 8.39 \pm 0.73$ Pin-SVM w $12.30 \pm 1.84$ $12.12 \pm 1.74$ $12.23 \pm 1.61$ $12.09 \pm 1.59$ $\tau {\rm{ = 0}}{\rm{.1}}$ b $ - 9.57 \pm 1.02$ $ - 9.88 \pm 0.99$ $ - 9.50 \pm 0.97$ $ - 9.62 \pm 0.92$ C-SVM w $13.29 \pm 1.96$ $12.98 \pm 1.87$ $13.42 \pm 1.73$ $13.12 \pm 1.69$ b $ - 16.15 \pm 3.04$ $ - 15.81 \pm 2.74$ $ - 15.92 \pm 2.45$ $ - 15.48 \pm 2.28$
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