Weighted Hierarchical Sparse Representation for Hyperspectral Target Detection

TL;DR

This paper introduces a weighted hierarchical sparse representation method for hyperspectral target detection, addressing background dictionary construction and correlation analysis issues, leading to improved detection performance.

Contribution

It proposes a hierarchical background dictionary considering local and global spectral info and weights target scores based on dictionary quality, enhancing detection accuracy.

Findings

Outperforms state-of-the-art methods on three datasets.

Effectively reduces background dictionary impurity impact.

Improves hyperspectral target detection accuracy.

Abstract

Hyperspectral target detection has been widely studied in the field of remote sensing. However, background dictionary building issue and the correlation analysis of target and background dictionary issue have not been well studied. To tackle these issues, a \emph{Weighted Hierarchical Sparse Representation} for hyperspectral target detection is proposed. The main contributions of this work are listed as follows. 1) Considering the insufficient representation of the traditional background dictionary building by dual concentric window structure, a hierarchical background dictionary is built considering the local and global spectral information simultaneously. 2) To reduce the impureness impact of background dictionary, target scores from target dictionary and background dictionary are weighted considered according to the dictionary quality. Three hyperspectral target detection data sets are utilized to verify the effectiveness of the proposed method. And the experimental results show a better performance when compared with the state-of-the-arts.