Gabor phase feature-based hyperspectral imagery classification

Sen Jia, Huimin Xie, Lin Deng, Linlin Shen

Abstract: In this paper, a three-dimensional (3D) Gabor phase coding and Hamming distance matching approach, called 3DGPC-HDM, is proposed for hyperspectral imagery classification. Specifically, the Gabor phase features with certain orientations are utilized, which are then encoded by a simple quadrant bit coding scheme. Next, a normalized Hamming distance matching method has been introduced to determine the similarity of two samples, and the nearest neighbor classifier is routinely used for recognition. The extensive experiments on two real hyper-spectral data sets have demonstrated superior performance of the proposed 3DGPC-HDM approach over the state-of-the-art methods in the literature.