Anomaly Detection in Hyperspectral Imagery Based on Gaussian Mixture Model

Abstract

Hyperspectral images (HSIs) with rich spectral information have been widely used in many fields. Anomaly detection is one of the most interesting and important applications. In this article, a novel Gaussian mixture model (GMM)-based anomaly detection (GMMD) method for HSI is proposed. The main contributions of this article are a new GMM-based extraction approach for extracting the anomaly pixels and an effective GMM-based weighting approach for fusing the extracted anomaly results. Specifically, based on the fact that the spectral values of anomaly pixels in some bands are different from those of background pixels, we propose a GMM-based anomaly extraction approach in which the HSI is characterized by the GMM and the anomaly pixels are extracted by a range prescribed by the GMM parameters. In order to fuse the extracted anomaly results, the GMM-based weighting method is introduced to adaptively construct the detection map. The detection map is rectified by using a guided filter to obtain the final anomaly detection map. Experimental results conducted on four hyperspectral data sets demonstrate the superior performance of the proposed GMMD method.