Superpixel-feature-based multiple kernel sparse representation for hyperspectral image classification

Abstract

Spatial information explored by superpixels can provide significant improvement for the accuracy of hyperspectral image classification. However, superpixels based sparse representation classification (SSRC) methods always introduce joint sparse pattern, which leads to the higher computational complexity. In this paper, a novel superpixel-feature-based multiple kernel sparse representation classification (SFMKSRC) method is proposed. Different from the traditional SSRC methods, SFMKSRC develops two new kinds of superpixel-based features by exploiting local mean operator within superpixels and weighted average operator among superpixels, which can describe the spatial information from both the local and global perspectives. Also, the novel superpixel-based features can avoid joint reconstruction to reduce the computational complexity. Then, the spectral-spatial features are extracted to combine with superpixels-based features and capture the detail structures of HSI to improve the classification performance. As pixels in high-dimensional feature space always trend to be linearly inseparable, a novel SFMKSRC model is designed to address the linearly inseparable problem of HSI classification. In addition, composite kernel constructed by generating base kernels for different features and optimally determining the weights of base kernels is embedded into MKSRC to exploit the strong correlations among different features while still preserve their diversities in a more flexible way. Experimental results on three real-world HSI datasets demonstrated that the proposed SFMKSRC method obtains a competitive performance and outperforms several state-of-the-art classification methods.