Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection

Abstract

With the high spectral resolution, hyperspectral images (HSIs) provide great potential for target detection, which is playing an increasingly important role in HSI processing. Many target detection methods uniformly utilize all the spectral information or employ reduced spectral information to distinguish the targets and background. Simultaneously reducing spectral redundancy and preserving the discriminative information is a challenging problem in hyperspectral target detection. The multitask learning (MTL) technique may have the potential to solve the above problem, since it can explore the redundancy knowledge to construct multiple sub-HSIs and integrate them without any information loss. This paper proposes the joint sparse representation and MTL (JSR-MTL) method for hyperspectral target detection. This approach: 1) explores the HSIs similarity by a band cross-grouping strategy to construct multiple sub-HSIs; 2) takes full advantage of the MTL technique to integrate the sparse representation models for the multiple related sub-HSIs; and 3) applies the total reconstruction error difference accumulated over all the tasks to detect the targets. Extensive experiments were carried out on three HSIs, and it was founded that JSR-MTL generally shows a better detection performance than the other target detection methods.