Multi-type spectral spatial feature for hyperspectral image classification

In recent years, many methods have been proposed to capture intra-spectrum features for the hyperspectral image classification task. However, most of these methods ignore inter-spectra information. In consideration of this, we propose a novel 3-D Inter-Spectra Difference Feature (ISDF) descriptor, which models the relationship between adjacent spectra using the difference between a center pixel and each of its spectral-adjacent spatial-neighbor pixels. Moreover, to increase the completeness of ISDF, the Neighbor Spectral Difference Feature (NSDF) guided by local spatial information is proposed as a supplement to the insufficient description of intra-spectrum information. At last, the Multi-type Spectral Spatial Feature (MSSF) is constructed by fusing ISDF, NSDF, and a global spatial texture feature. Experimental results on three public hyperspectral image datasets demonstrate that our proposed MSSF is effective and can outperform eight representative hyperspectral image classification methods.