A graphical estimation and multiple-sparse representation strategy for hyperspectral anomaly detection

Abstract

Most hyperspectral anomaly detection (AD) methods only utilize spectral information in hyperspectral images (HSI) to distinguish anomaly targets from backgrounds. However, the features extracted by spectral difference are incomplete and will limit the performance of AD. To overcome the drawbacks and obtain abundant feature information of diverse components, we propose a novel hyperspectral AD strategy based on graphical estimation and multiple-sparse representation. First, the potential anomalies can be screened out by a prior graphical connected region (PGCR) estimation, by which the potential anomalies are distinguished from robust background spatially. Second, based on sparse representation (SR) theory, a multiple SR framework is utilized to select spectral feature information. It can pick out the representative spectra that further enlarge anomaly’s distinction from background. Consequently, a fusion strategy is designed to comprehensively determine different groups of results generated by multiple SR method and to obtain the final detection result. In the experiment, the proposed method achieves a more superior result than other classic or popular AD detectors. A detailed analysis of the relevant parameters and their sensitivity under specific condition are also discussed in the experimental section.