Hyperspectral Anomaly Detection Using Dual Window Density

Abstract

Hyperspectral anomaly detection is one of the most active topics in hyperspectral image (HSI) analysis. The fine spectral information of HSIs allows us to uncover anomalies with very high accuracy. Recently, an intrinsic image decomposition (IID) model has been introduced for low-rank IID (LRIID) in multispectral images. Inspired by the LRIID, which is able to effectively recover the reflectance and shading components of the multispectral image, this article adapts the LRIID for obtaining the reflectance component of HSIs (which is the key feature for the discrimination of different objects). In order to exploit the reflectance component, we also propose a new dual window density (DWD)-based detector for anomaly detection, which is based on the idea that anomalies are usually rare pixels and, thus, exhibit low density in the image. The density analysis of DWD is intended not only to circumvent the Gaussian assumption regarding the distribution of HSI data, but also to mitigate the contamination of background statistics caused by anomalies. The dual window operation of our DWD is specifically designed to adaptively calculate the density of each pixel under test, so as to identify anomalies with nonspecific sizes. Our experimental results, obtained on a database of real HSIs including Airport, Beach, and Urban scenes, demonstrate the superiority of the proposed method in terms of detection performance when compared to other widely used anomaly detection methods.