A Dual Sparsity Constrained Approach for Hyperspectral Target Detection
Dunbin Shen, Xiaorui Ma, Hongyu Wang, Jianjun Liu
Abstract
The problem of target detection in hyperspectral images is an unsupervised binary classification problem with extremely uneven samples. To highlight the target and suppress the background as much as possible, this paper proposes a target detection algorithm based on dual sparse constraints. Specifically, the original image can be decomposed into a background image and a target image. Combined with sparse representation, the target detection problem can be transformed into a problem of optimizing the target and background coefficient matrices. This problem can be solved by the alternating direction method of multipliers. Considering that both the target dictionary and background dictionary are unknown, this paper also proposes a dictionary construction algorithm based on spectral similarity and clustering to obtain relatively complete and pure target and background dictionaries. The experimental results demonstrate that the proposed method outperforms other competing methods in both quantitative performance and visual effect. Code and datasets are available at https://github.com/shendb2022/DSC.