Hyperspectral image classification using Gradient Local Auto-Correlations

Abstract

Spatial information has been verified to be helpful in hyperspectral image classification. In this paper, a spatial feature extraction method utilizing spatial and orientational auto-correlations of image local gradients is presented for hyperspectral imagery (HSI) classification. The Gradient Local Auto-Correlations (GLAC) method employs second order statistics (i.e., auto-correlations) to capture richer information from images than the histogram-based methods (e.g., Histogram of Oriented Gradients) which use first order statistics (i.e., histograms). The experiments carried out on two hyperspectral images proved the effectiveness of the proposed method compared to the state-of-the-art spatial feature extraction methods for HSI classification.