Kernel Minimum Noise Fraction Transformation-Based Background Separation Model for Hyperspectral Anomaly Detection

A significant challenge in methods for anomaly detection (AD) hyperspectral images (HSIs) is determining how to construct an efficient representation anomalies and background information. Considering the high-order structures of HSIs estimation information AD, this article proposes a kernel minimum noise fraction transformation-based separation model (KMNF-BSM) separate First, spectral-domain KMNF transformation performed on original data fully mine correlation between spectral bands. Then, BSM that combines outlier removal, iteration strategy, Reed–Xiaoli detector (RXD) proposed obtain accurate anomalous pixel sets based extracted features. Finally, are used as input detectors improve suppression capabilities. Experiments several with different spatial resolutions over scenes performed. The results demonstrate KMNF-BSM-based algorithms have better target detectability suppressibility than other state-of-the-art algorithms.