MORPHOLOGICAL RANDOM WALKER FOR HYPERSPECTRAL ANOMALY DETECTION

This paper introduces a novel morphological random walker (MRW) technique for hyperspectral anomaly detection. The proposed MRW detector introduces a morphology-based objective function into a random walker (RW) optimization model, which can fully exploit the morphological property of anomalies for detection. Specifically, the proposed algorithm consists of two main steps. First, the extended morphological profiles and differential operations are employed to exploit the morphological property of anomalies. Then, based on the morphological property, a morphology-based objective function is constructed, and this objective function is incorporated into the RW-based optimization model for anomaly detection. Experimental results show that the proposed detector outperforms several state-of-the-art anomaly detectors.