Robust Anomaly Detection Algorithm for Hyperspectral Images Using Spectral Unmixing

Anomaly detection uses spectral pixels to distinguish between one pixel or group of pixels in a hyperspectral image and its\their background pixels. Most of the anomaly detection algorithms depend on the assumptions of the background distribution such as the RX algorithm which assumes the gaussian distribution of the background which is not valid for most cases of hyperspectral images. Moreover, most of the algorithms have problems with the false alarms which is noise and detected as anomalies. To overcome these drawbacks, we propose a simple and easy anomaly detection algorithm which depends mainly on the spectral unmixing. Instead of using the raw pixels as given data to detect anomalies, we apply the spectral unmixing algorithm first to estimate the abundance maps and use these maps as features for anomaly detection. Next, we use edge detection algorithm for all abundance maps to detect all boundaries and anomalies in the scene. This gives robustness to the detection algorithm as every anomaly is detected in two abundance maps. We used AVIRIS hyperspectral imaging data cubes to evaluate the proposed algorithm.