An Improved Camouflage Target Detection Using Hyperspectral Image Based on Block-Diagonal and Low-Rank Representation
Accurate camouflage target distinction is often resorted to hyperspectral spectral imaging technique as for the rich spectral information contained in hyperspectral images.In this paper,a novel block-diagonal representation based camouflage target detection method is proposed for hyperspectral imagery.To better represent the multi-mode cluster background,an hyperspectral image is first clustered into different background clusters according to their spectral features.Then,an orthogonal background dictionary is learned for each cluster via a principle component analysis(PCA)learning scheme.The background and camouflage target often show different structures when projected onto those dictionaries.The former exhibits block-diagonal structure while the latter shows sparse structure.Inspired by this fact,we cast the block-diagonal structure into a low-rank representation model.With proper optimization of such model,the sparse camouflage targets can be accurately separated from the block-diagonal background.Experimental results on the real-world camouflage target datasets demonstrate that the proposed method outperforms the state-in-art hyperspectral camouflage target detection methods.
Hyperspectral image Camouflage target detection Block-diagonal structure Sparse representation Dictionary learning
Fei Li Xiuwei Zhang Lei Zhang Yanning Zhang Dongmei Jiang Genping Zhao
School of Computing Science,Northwestern Polytechnical University,Xian 710071,Shaanxi,China;Shaanxi School of Computers,Guangdong University and Technology,Guangzhou 510006,China
国际会议
广州
英文
384-395
2018-11-23（万方平台首次上网日期，不代表论文的发表时间）