Improved Local Covariance Matrix Representation for Hyperspectral Image Classification

Abstract

This paper proposes a novel spectral-spatial feature representation method for hyperspectral image (HSI) classification. It combines the advantages of adaptive weighted filtering (AWF) and local covariance matrix representation (L-CMR) to make full use of the spatial similarity and correlation among different spectral bands. Specifically, the proposed method first uses the maximum noise fraction (MNF) to reduce the dimensionality of HSI. Then, multiscale AWF (MAWF) is applied to make use of spatial information. N ext’ the spectral-spatial features are obtained by calculating the local covariance matrix of the given pixel and its neighbors. Finally, the learned spectral-spatial features of each pixels are fed into support vector machine (SVM) for classification. Experimental results on two publicly available HSI datasets show that the proposed method is superior to several existing methods in terms of both classification accuracy and classification visual effect, especially when the number of training samples is small.