A spatial-spectral classification strategy for very high-resolution images using region covariance descriptors and multiple kernel learning algorithms

Abstract

Extracting and modeling the spatial information content of very high resolution (VHR) images can dramatically increase the performances of urban area classification. However, extracting spatial features is a highly challenging task. During the years, several spatial feature extraction methods have been proposed, most of which are mainly designed for grayscale images. To use these methods for a multispectral image, usually, a dimensionality reduction step is required. As a result, these methods cannot optimally extract the spatial information contents of different bands of a multispectral image. To address this issue, we proposed the use of the region covariance descriptor (RCD) for spatial feature extraction from VHR images. The RCD features consider the covariance matrix of a local neighborhood of each pixel as the features. These features can model both the spatial information and the spectral relationship between bands. The RCD features lie in a Riemannian manifold, on which the common classification algorithms cannot be applied. To overcome this, we used Riemannian kernel functions. Also, we proposed a multiple kernel learning strategy for combining RCD and spectral features. The proposed strategy was evaluated for classifying a VHR image acquired over the urban area of Tehran, Iran. Furthermore, its obtained results were compared with those of ten other common spatial feature extraction methods. The results showed that the proposed classification strategy using the RCD features yielded at least 5% higher accuracies than the other feature extraction methods.