Image classification is a critical and important application in PolSAR image interpretation. Finding a feature extraction method, which can effectively describe the characteristics of the target, is an important basis for image classification. In addition to unique polarimetric features of PolSAR system, spatial adjacent features of image also need to be considered. So in this article, a joint polarimetric-adjacent features extraction method based on local convolution sparse representation is proposed for PolSAR image classification. Firstly, this article uses convolutional sparse representation to achieve the convolution of the image filters and the feature responses so as to achieve the effective combination of the polarimetric and adjacent information of the image. Meanwhile, construct and train the dictionary using local strategy in the original domain to avoid the high computational complexity and the confusion of different grounds caused by global dictionary. Finally, support vector machine (SVM) is used to combine the extracted features to achieve the classification. Three sets of full polarimetric data are used and the experiment results prove that the proposed method can effectively combine the polarimetric and adjacent information of data and have a good performance in PolSAR image classification.

中文翻译：

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基于LCSR的PolSAR图像分类联合极化相邻特征

影像分类是 PolSAR 影像解译的关键和重要应用。寻找一种能够有效描述目标特征的特征提取方法，是图像分类的重要依据。除了PolSAR系统独特的极化特征外，还需要考虑图像的空间相邻特征。因此本文提出了一种基于局部卷积稀疏表示的极化-相邻联合特征提取方法用于PolSAR图像分类。本文首先利用卷积稀疏表示来实现图像滤波器和特征响应的卷积，从而实现图像的极化信息和相邻信息的有效结合。同时，在原始域中使用局部策略构建和训练字典，以避免全局字典造成的高计算复杂度和不同理由的混淆。最后，使用支持向量机（SVM）将提取的特征组合起来实现分类。使用三组全极化数据，实验结果证明该方法能够有效地结合数据的极化信息和相邻信息，在极化SAR图像分类中具有良好的性能。