Convolutional Kernel-based covariance descriptor for classification of polarimetric synthetic aperture radar images

There are two types of important information in a polarimetric synthetic aperture radar (PolSAR) image: spatial features in two dimensions and polarimetric characteristics in the scattering dimension. Considering both polarimetric and spatial information is important for PolSAR image classification. Convolutional kernels show superior performance for extraction of spatial information from two dimensions of an image in convolutional neural networks (CNNs). But learning CNNs needs large enough training sets to achieve the optimum weights of kernels while there are not usually sufficient training samples for PolSAR images. To deal with this difficulty, a convolutional kernel-based covariance descriptor (CKCD) is introduced for PolSAR image classification in this study. To extract contextual characteristics, compatible with the original image, the fixed-valued convolutional kernels randomly selected from the image are used, which do not require any learning, and so do not need any training samples. To include more local spatial information and find the relation among the polarimetric features, the covariance descriptor is constructed on the extracted feature maps. Then, the polarimetric-contextual features are given to a support vector machine with a matrix logarithm-based kernel. Finally, the guided filter is applied to the initial classification map to result a smoothed classification map with preserved edges. The experiments on three real PolSAR images show superiority of the proposed CKCD method compared to several PolSAR classification methods such as 2DCNN and 3DCNN in small sample size situations.