Covariance of Textural Features: A New Feature Descriptor for SAR Image Classification

Abstract

Synthetic aperture radar (SAR) image land-cover classification is an important research topic in SAR image interpretation. However, speckle, inherent of active coherent imaging systems, diminishes the performances of feature extraction techniques and classifiers. In this article, we firstly present a simple yet powerful feature extraction method based on the covariance matrix of textural feature. In comparison with the original textural feature, the use of second-order statistics makes the proposed covariance descriptor more distinguishable for various land covers and robust to speckle noise. Considering that the covariance descriptors are symmetric positive definite (SPD) matrices which form a Riemannian manifold, we then propose to map the manifold with Log-Euclidean metric in a high dimensional reproducing kernel Hilbert space by means of the positive definite kernel defined on manifolds. As a result, the kernel not only allows us to make full use of algorithms developed for linear spaces but also preserves geometric structure of SPD matrices. Our experiments demonstrate that the proposed method can provide promising results in terms of classification accuracy and label consistency.