Hyperspectral Target Detection via Adaptive Joint Sparse Representation and Multi-Task Learning with Locality Information

Yuxiang Zhang,Xiangyun Hu,Bo Du,Liangpei Zhang,Ke Wu

doi:10.3390/rs9050482

Abstract

Target detection from hyperspectral images is an important problem but encounters a critical challenge of simultaneously reducing spectral redundancy and preserving the discriminative information. Recently, the joint sparse representation and multi-task learning (JSR-MTL) approach was proposed to address the challenge. However, it does not fully explore the prior class label information of the training samples and the difference between the target dictionary and background dictionary when constructing the model. Besides, there may exist estimation bias for the unknown coefficient matrix with the use of minimization which is usually inconsistent in variable selection. To address these problems, this paper proposes an adaptive joint sparse representation and multi-task learning detector with locality information (JSRMTL-ALI). The proposed method has the following capabilities: (1) it takes full advantage of the prior class label information to construct an adaptive joint sparse representation and multi-task learning model; (2) it explores the great difference between the target dictionary and background dictionary with different regularization strategies in order to better encode the task relatedness; (3) it applies locality information by imposing an iterative weight on the coefficient matrix in order to reduce the estimation bias. Extensive experiments were carried out on three hyperspectral images, and it was found that JSRMTL-ALI generally shows a better detection performance than the other target detection methods.

Highlights

Target detection is essentially a binary classification problem, which aims to separate specific target pixels from various backgrounds with prior knowledge of the targets [1,2]
The spectral resolution of hyperspectral images (HSIs) is so high that the adjacent single-band images present a great spectral similarity or redundancy, and this spectral redundancy provides an obstacle for effective target detection
To address the above problems, this paper proposes an adaptive joint sparse representation and multi-task learning detector with locality information (JSRMTL-ALI)

Summary

Introduction

Target detection is essentially a binary classification problem, which aims to separate specific target pixels from various backgrounds with prior knowledge of the targets [1,2]. 2017, 9, 482 hypothesis testing theory [10,11,12], filtering or projection technique [13,14,15], and sparse representation technique [16,17,18,19] These existing target detection methods, using a uniform vector of test pixel’s spectrum as input, usually employ all the original bands to both construct the model and perform the detection. For the hyperspectral hyperspectral target detection.imagery (HSI), as discussed in [29], the adjacent single band images are similar to each other and MTL technology is introduced to utilize the spectral similarity for. Usesrelated the domain-specific information in make the training signals of related method tasks, which canThere are two key techniques of MTL. 2-D plots of the detection map of all the comparison algorithms with the three data sets 2-D

Results

Discussion

Conclusion