Global unsupervised Anomaly Extraction and Discrimination in hyperspectral images via Maximum Orthogonal-Complement analysis

In this paper we address the problem of global unsupervised detection, discrimination, and population estimation of anomalies in hyperspectral images. The proposed approach, denoted as Anomaly Extraction and Discrimination Algorithm (AXDA), detects anomalies via analysis of a signal-subspace obtained by the recently developed Maximum Orthogonal-Complement Algorithm (MOCA). MOCA is unique in providing an unsupervised combined estimation of signal-subspace that includes anomalies, and its rank. The main idea of AXDA is to iteratively reduce the anomaly vector subspace-rank, making the related anomalies to be poorly represented. This helps to detect them by a statistical analysis of the ℓ_2,∞-norm of data residuals. As a by-product, AXDA provides also an anomaly-free robust background subspace and rank estimation.