Game Theory-Based Hyperspectral Anomaly Detection

Abstract

In this article, a novel game theory-based approach is proposed for anomaly detection in hyperspectral images (HSIs) via effectively exploring multiple spectral and spatial characteristics of anomalies. This approach comprises three main steps. First, spectral, extend morphological profiles (EMPs), and Gabor texture features are captured from an input HSI. Then, we define the anomaly detection problem as an anomaly game model, in which image regions (superpixels) of different features are modeled as players who select to be “anomaly” or “background” as their strategies. Three initial detection results are produced based on each player’s strategy in the Nash equilibrium of the anomaly game. Last, a saliency-based decision fusion technique is used to combine the complementary information in different features, so as to obtain a fused detection map. The performance of the proposed anomaly detection technique is evaluated on four real-scene HSIs. Experimental results validate that our approach can outperform some state-of-the-art anomaly detection methods.