| Compressed hyperspectral image sensing with joint sparsity reconstruction | |
| LiuHaiying; LiYunsong; ZhangJing; SongJuan; LvPei; Liu Haiying | |
| 2011 | |
| 会议名称 | Conference on Satellite Data Compression, Communications, and Processing VII |
| 会议录名称 | Proceedings of SPIE - The International Society for Optical Engineering |
| 页码 | 815703 |
| 会议日期 | AUG 23-24, 2011 |
| 会议地点 | San Diego, CA |
| 出版地 | P.O. Box 10, Bellingham, WA 98227-0009, United States |
| 出版者 | SPIE |
| 产权排序 | 2 |
| 摘要 | Recent compressed sensing (CS) results show that it is possible to accurately reconstruct images from a small number of linear measurements via convex optimization techniques. In this paper, according to the correlation analysis of linear measurements for hyperspectral images, a joint sparsity reconstruction algorithm based on interband prediction and joint optimization is proposed. In the method, linear prediction is first applied to remove the correlations among successive spectral band measurement vectors. The obtained residual measurement vectors are then recovered using the proposed joint optimization based POCS (projections onto convex sets) algorithm with the steepest descent method. In addition, a pixel-guided stopping criterion is introduced to stop the iteration. Experimental results show that the proposed algorithm exhibits its superiority over other known CS reconstruction algorithms in the literature at the same measurement rates, while with a faster convergence speed. |
| 关键词 | Hyperspectral Imagery Compressive Sensing (Cs) Linear Prediction Projections Onto Convex Sets(Pocs) Steepest Descent Method |
| 作者部门 | 研究生部 |
| 收录类别 | EI ; CPCI |
| 语种 | 英语 |
| ISSN号 | 0277-786X |
| 文献类型 | 会议论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/20156 |
| 专题 | 研究生部 |
| 通讯作者 | Liu Haiying |
| 推荐引用方式 GB/T 7714 | LiuHaiying,LiYunsong,ZhangJing,et al. Compressed hyperspectral image sensing with joint sparsity reconstruction[C]. P.O. Box 10, Bellingham, WA 98227-0009, United States:SPIE,2011:815703. |
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