Iterative Spectral-Spatial Hyperspectral Anomaly Detection

• Published in: IEEE transactions on geoscience and remote sensing Vol. 61; pp. 1 - 30
• Main Authors: Chang, Chein-I, Lin, Chien-Yu, Chung, Pau-Choo, Hu, Peter Fuming
• Format: Journal Article
• Language: English
• Published: New York IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anomalies; Anomaly detection; Anomaly detection (AD); Current data; Detection; Detectors; Feature extraction; Feeds; Hyperspectral imaging; Indexes; Iterative methods; iterative spectral–spatial anomaly detection (ISSHAD); Reed–Xiaoli anomaly detector (RXAD); Spatial data; Tensors; Windows (computer programs)
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3247660

Anomaly detection (AD) requires spectral and spatial information to differentiate anomalies from their surrounding data samples. To capture spatial information, a general approach is to utilize local windows in various forms to adapt local characteristics of the background (BKG) from which unknown anomalies can be detected. This article develops a new approach, called iterative spectral-spatial hyperspectral AD (ISSHAD), which can improve an anomaly detector in its performance via an iterative process. Its key idea is to include an iterative process that captures spectral and spatial information from AD maps (ADMaps) obtained in previous iterations and feeds these anomaly maps back to the current data cube to create a new data cube for the next iteration. To terminate the iterative process, a Tanimoto index (TI)-based automatic stopping rule is particularly designed. Three types of spectral and spatial information, ADMaps, foreground map (FGMap), and spatial filtered map (SFMap), are introduced to develop seven various versions of ISSHAD. To demonstrate its full utilization in improving AD performance, a large number of extensive experiments are performed for ISSHAD along with its detailed comprehensive analysis among several most recently developed anomaly detectors, including classic, dual-window-based, low-rank representation model-based, and tensor-based AD methods for validation.