A sparse dictionary learning method for hyperspectral anomaly detection with capped norm

Hyperspectral anomaly detection is playing an important role in remote sensing field. Most conventional detectors based on the Reed-Xiaoli (RX) method assume the background signature obeys a Gaussian distribution. However, it is definitely hard to be satisfied in practice. Moreover, background statistics is susceptible to contamination of anomalies in the processing windows, which may lead to many false alarms and sensitiveness to the size of windows. To solve these problems, a novel sparse dictionary learning hyperspectral anomaly detection method with capped norm constraint is proposed. Contributions are claimed in threefold: 1) requiring no assumptions on the background distribution makes the method more adaptive to different scenes; 2) benefiting from the capped norm our method has a stronger distinctiveness to anomalies; and 3) it also has better adaptability to detect different sizes of anomalies without using the sliding dual window. The extensive experimental results demonstrate the desirable performance of our method.