Kernel Collaborative Representation With Local Correlation Features for Hyperspectral Image Classification

Spatial information has widely been used in hyperspectral image (HSI) classification to improve classification accuracy. However, the structural information may not be fully explored when using spatial information, this paper proposes the joint collaborative representation classification with correlation matrix (CRC-CM) for HSI by using spatial correlation features in patches, which could keep the local intrinsic structure in band images. Considering spatial heterogeneity in a patch, local correlation matrices of a target neighborhood patch and training neighborhood patch are improved by a binary weight matrix and shape-adaptive neighborhood. To explore nonlinear nature of spatial features, corresponding kernel CRC-CM is also proposed. To evaluate the effectiveness of the proposed methods, three real HSIs with different degree of heterogeneity are used. The experimental results show that the proposed spatial correlation features outperform the original spectral feature and other spatial features which widely used in HSI classifiers.