Joint Sparse Representation and Multitask Learning for Hyperspectral Anomaly Detection

Abstract

The sparse representation has been introduced for hyperspectral anomaly detection methods. However, the window parameter tuning and anomaly contamination problems are still the main issues with the background dictionary. In order to solve these problems, this paper proposed the joint sparse representation and multi-task learning method (JSM) for anomaly detection. This method utilizes a global background dictionary construction method to avoid the above window parameter tuning and anomaly contamination problems. Besides, the multi-task learning technology is employed to explore the hyperspectral images similarity within adjacent single-band images. Experiments were carried out on two hyperspectral images, and it was founded that JSM method shows a better detection performance than the other anomaly detection methods.