Hyperspectral anomaly detection using outlier removal from collaborative representation

Hyperspectral imagery systems have the ability to collect 3D digital images with rich spatial and spectral information. Anomaly detection is one of the interesting applications over last two decades in hyperspectral imagery. In this paper, we propose Collaborative Representation-Based with Outlier Removal Anomaly Detector (CRBORAD) method for HSI Anomaly Detection. We use both spectral and spatial information for detecting anomalies instead of using only spectral information that was introduced in our previous work. The proposed detector can adaptively estimate the background by its adjacent pixels within a sliding dual window. Before estimating background pixels, we remove outlier pixels that are significantly different from majority of pixels. It leads us to precise background approximation and better accuracy for detecting anomalies in subsequent stages. The residual image is constituted by subtracting the predicted background from the original HSI, and anomalies can be determined in the residual image, finally. Kernel extension of the proposed approach is also presented. We implemented the proposed algorithms on San Diego airport hyperspectral data. CRBORAD results are illustrated using receiver-operating-characteristic (ROC) curves, Area Under Curve (AUC) values and intuitive images. Comparing the results of the current study with four popular and previous methods shows that CRBORAD provides us an accurate method for detecting anomalies.