Independent Encoding Joint Sparse Representation and Multitask Learning for Hyperspectral Target Detection

Abstract

Target detection is playing an important role in hyperspectral image (HSI) processing. Many traditional detection methods utilize the discriminative information within all the single-band images to distinguish the target and the background. The critical challenge with these methods is simultaneously reducing spectral redundancy and preserving the discriminative information. The multitask learning (MTL) technique has the potential to solve the aforementioned challenge, since it can further explore the inherent spectral similarity between the adjacent single-band images. This letter proposes an independent encoding joint sparse representation and an MTL method. This approach has the following capabilities: 1) explores the inherent spectral similarity to construct multiple sub-HSIs in order to reduce spectral redundancy for each sub-HSI; 2) takes full advantage of the prior class label information to construct reasonable joint sparse representation and MTL models for the target and the background; 3) explores the great difference between the target dictionary and background dictionary with different regularization strategies in order to better encode the task relatedness for two joint sparse representation and MTL models; and 4) makes the detection decision by comparing the reconstruction residuals under different prior class labels. Experiments on two HSIs illustrated the effectiveness of the proposed method.