Effective Anomaly Space for Hyperspectral Anomaly Detection

Abstract

Due to unavailability of prior knowledge about anomalies, background suppression (BS) is a crucial factor in anomaly detection (AD). The difficulty with dealing with BS arises from the fact that anomalies are generally sandwiched between background (BKG) and noise. This paper presents a new concept, called effective anomaly space (EAS) to resolve this dilemma. To accomplish this goal, the well-known independent component analysis (ICA) is used to address the between BKG and anomalies issue by removing the first two orders of data statistics (2OS), while sparsity cardinality (SC) is used to address the between anomalies and noise issue by removing non-Gaussian noises and interferers from anomalies. Specifically, SC is re-derived as fixed SC (FSC) for a spectral vector and a spatial band corresponding to fixed length coding and variable SC (VSC) for a spectral-spatial sample and spatial-spectral band corresponding to variable length coding from information theory. Combining ICA and the new versions of SC allows EAS not only to remove BKG-characterized by 2OS including Gaussian-distributed signal sources, but also to remove non-Gaussian noises/interferers from anomalies. As a result, EAS can significantly increase anomaly detectability. In particular, one of great benefits resulting from EAS is that EAS can also improve current low rank and sparse representation (LRaSR)-based methods used for AD.