Nonnegative-Constrained Joint Collaborative Representation With Union Dictionary for Hyperspectral Anomaly Detection

Recently, many collaborative representation (CR)-based algorithms have been proposed for hyperspectral anomaly detection (AD). CR-based detectors approximate the image by a linear combination of background dictionaries and the coefficient matrix and derive the detection map by utilizing recovery residuals. However, these CR-based detectors are often established on the premise of precise background features and strong image representation, which are very difficult to obtain. In addition, pursuing the coefficient matrix reinforced by the general
$l_{2}$
-min is very time-consuming. To address these issues, a nonnegative-constrained joint collaborative representation (NJCR) model is proposed in this article for the hyperspectral AD task. To extract reliable samples, a union dictionary consisting of background and anomaly subdictionaries is designed, where the background subdictionary is obtained at the superpixel level and the anomaly subdictionary is extracted by the predetection process. And the coefficient matrix is jointly optimized by the Frobenius norm regularization with a nonnegative constraint and a sum-to-one constraint. After the optimization process, the abnormal information is finally derived by calculating the residuals that exclude the assumed background information. To conduct comparable experiments, the proposed nonnegative-constrained joint collaborative representation (NJCR) model and its kernel version (KNJCR) are tested in four hyperspectral images (HSIs) datasets and achieve superior results compared with other state-of-the-art detectors. The codes of the proposed method will be available online (
https://github.com/ShizhenChang/NJCR
).