关键词: 压缩感知, 极化合成孔径雷达图像分类, 判别式测量矩阵, 威沙特空间核

Abstract: To deal with the large scale problem, a classifier based on the spatial sensing matrix and Least Squares Support Vector Machine (LS-SVM) is proposed for the polarimetric synthetic aperture radar (PolSAR) image. Inspired by the compressive sensing theory, a spatial sensing matrix is designed, which is equal to the product of the measurement matrix and the kernel matrix. The discriminative sensing matrix is proposed to largely reduce the scale of the optimization problem. Then, by taking the special properties of the polarimetric data and spatial information into account, we propose a spatial-Wishart dictionary to reduce the noise of speckle. Finally, the compressive sensing inspired classifier is constructed and the sparse support vector coefficients are achieved. Classification accuracy and spatial consistency of the proposed classifier is superior to those of other classifiers.

Key words: compressive sensing, polarimetric synthetic aperture radar image classification, discriminative sensing matrix, spatial-Wishart kernel