The composite kernel feature fusion proposed in this paper attempts to solve the problem of classifying polarimetric synthetic aperture radar (PolSAR) images. Here, PolSAR images take into account both polarimetric and spatial information. Various polarimetric signatures are collected to form the polarimetric feature space, and the morphological profile (MP) is used for capturing spatial information and constructing the spatial feature space. The main idea is that the composite kernel method encodes diverse information within a new kernel matrix and tunes the contribution of different types of features. A support vector machine (SVM) is used as the classifier for PolSAR images. The proposed approach is tested on a Flevoland PolSAR data set and a San Francisco Bay data set, which are in fine quad-pol mode. For the Flevoland PolSAR data set, the overall accuracy and kappa coefficient of the proposed method, compared with the traditional method, increased from 95.7% to 96.1% and from 0.920 to 0.942, respectively. For the San Francisco Bay data set, the overall accuracy and kappa coefficient of the proposed method increased from 92.6% to 94.4% and from 0.879 to 0.909, respectively. Experimental results verify the benefits of using both polarimetric and spatial information via composite kernel feature fusion for the classification of PolSAR images.
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机译：本文提出的复合核特征融合试图解决极化合成孔径雷达（PolSAR）图像分类问题。这里，PolSAR图像同时考虑了极化信息和空间信息。收集各种极化特征以形成极化特征空间，形态学轮廓（MP）用于捕获空间信息并构建空间特征空间。主要思想是，复合核方法在新的核矩阵中编码各种信息，并调整不同类型特征的作用。支持向量机（SVM）用作PolSAR图像的分类器。在精细的四极点模式下，对Flevoland PolSAR数据集和San Francisco Bay数据集进行了测试。对于Flevoland PolSAR数据集，与传统方法相比，该方法的总体准确性和kappa系数分别从95.7％增加到96.1％，从0.920增加到0.942。对于旧金山湾数据集，所提方法的整体准确性和卡伯系数分别从92.6％增加到94.4％，从0.879增加到0.909。实验结果验证了通过复合核特征融合将极化信息和空间信息同时用于PolSAR图像分类的好处。
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