Real-Time Hyperspectral Anomaly Detection using Collaborative Superpixel Representation with Boundary Refinement

Hyperspectral anomaly detection (HAD) is a crucial task that aims to classify the given image into abnormal pixels and background pixels. Besides, the classification boundary between the abnormal pixels and the background pixels is implicit, making HAD a challenging problem. An existing method for anomaly detection is proposed based on collaborative representation. Since the method performs the detection on each pixel, it is not computationally efficient. To reduce the computational cost, we develop a new method based on collaborative representation. First, superpixel segmentation is utilized to cluster the image. Then, we perform the collaborative representation on each superpixel to obtain a rough detection result. According to the preliminary result, a threshold is automatically calculated to classify potential abnormal superpixels and background superpixels. At last, the boundaries of abnormal superpixels are refined to yield a more accurate detection result. In the real data experiments, we show that our method has satisfactory visual qualities and state-of-the-art quantitative performance.