Hyperspectral Imagery Classification Based on Rotation-Invariant Spectral-Spatial Feature

In this letter, we present a novel approach for spectral-spatial classification in hyperspectral imagery. After applying principal component (PC) analysis for dimensionality reduction, we extract the spectral-spatial information by first reorganizing the local image patch with the first d PCs into a vector representation, followed by a sorting scheme to make the vector invariant to local image rotation. Since no additional operation except sorting the pixels is required, this step is performed efficiently. Afterward, the resulting feature descriptors are embedded into a linear support vector machine for classification. To evaluate the proposed method, experiments are preformed on two hyperspectral images with high spatial resolution. The experimental results confirm that the proposed method outperforms the existing algorithms on classification accuracy.