Abstract Due to the severe speckle noise of a fully polarimetric synthetic aperture radar image and the complex backscattering mechanism at the junction of different land covers, some of the pixels are easily mislabeled, especially on the edge of the land covers. To address this issue, this study presents a novel scheme that selects polarimetric features step by step to participate in classification through different classification mechanisms. Different from previous classification methods where all land covers are described by the same polarimetric features, we make a fine selection of polarimetric features for each land cover with the help of information entropy and contextual information. Among many polarimetric features, elements of the covariance matrix and the coherence matrix are selected to optimize the original classification results. The experimental results show that the proposed method can achieve good classification results, especially in the edge area of land covers. Compared to traditional classification methods, the proposed method is robust and is able to improve the overall accuracy by more than 5.58% and the kappa coefficient by more than 0.0613.

中文翻译：

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一种基于最优极化特征和上下文信息的新型 PolSAR 图像分类方法

摘要 由于全极化合成孔径雷达图像散斑噪声严重，且不同地被覆物交界处的后向散射机制复杂，导致部分像素点容易误标，尤其是地被覆物边缘。为了解决这个问题，本研究提出了一种新方案，通过不同的分类机制逐步选择极化特征参与分类。与以前所有土地覆盖都由相同的极化特征描述的分类方法不同，我们借助信息熵和上下文信息为每个土地覆盖精细选择极化特征。在众多极化特征中，选取协方差矩阵和相干矩阵的元素对原始分类结果进行优化。实验结果表明，该方法能够取得较好的分类效果，尤其是在土地覆盖的边缘区域。与传统分类方法相比，所提出的方法具有鲁棒性，能够将整体准确率提高5.58%以上，kappa系数提高0.0613以上。