Ensemble Entropy Metric for Hyperspectral Anomaly Detection

In hyperspectral anomaly detection, anomalies are rare targets that exhibit distinct spectral signatures from the background. Thus, anomalies are with low probabilities of occurrence in hyperspectral images. In this article, we develop a new technique for hyperspectral anomaly detection that adopts a new information theory perspective, to fully utilize the aforementioned concepts. Our goal is to transform system entropy into quantitative metrics of anomaly conspicuousness of pixels. To do so, two tasks are first completed: first, the construction of occurrence probability of pixels based on the density peak clustering algorithm, and second, the valid system definitions for pixels in specific anomaly detection problems with multiviews. Specifically, three types of systems are separately established by pixel pairs to conform to the definitions of three entropy definitions in information theory, i.e., Shannon entropy, joint entropy, and relative entropy. Then, three individual entropy-based metrics that assess the anomaly conspicuousness are defined. In addition, we design a standard deviation-based ensemble strategy for the integrated representation of the three individual metrics, which considers both logic ``or'' and ``and'' operations to simultaneously improve the detection rate and reduce the false alarm rate. Our experimental results obtained on two publicly available datasets with anomalies of different sizes and shapes demonstrate the superiority of our newly proposed anomaly detection method.