A Novel Filter-Based Anomaly Detection Framework for Hyperspectral Imagery

Abstract

Hyperspectral images stand out from other remote sensing images in anomaly target detection because they contain unique distinguishing spectral information and attract great interest in applications of search and rescue. However, most of the popular techniques for hyperspectral anomaly detection tasks focus on improving accuracy with complicated algorithms and face difficulty in efficiently balancing performance and complexity. In this paper, we propose a novel anomaly detection approach using a selected band image extracted from the band selection model combined with an image filter. Singular value decomposition (SVD) is adopted for spectral dimensionality reduction. A dual-window guided filter is constructed to highlight the potential anomaly targets. To quickly calculate the abnormity degree, we design an efficient diagonal matrix operation to achieve the energy of each pixel, and a spatial regulation model is designed to enhance the subpixel target detection performance. Extensive experiments conducted on two real-world hyperspectral datasets demonstrate that, compared with the existing relevant state-of-the-art approaches, the proposed method requires less detection time and achieves higher detection accuracy.