期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 53, 期 2, 页码 631-644出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2014.2326655
关键词
Band selection; compressive sensing (CS); hyperspectral image; immune clonal strategy (ICS); machine learning; multitask learning (MTL)
类别
资金
- National Basic Research Program of China (973 Program) [2011CB707104]
- National Natural Science Foundation of China [61172143, 61105012, 61379094]
- Fundamental Research Funds for the Central Universities [3102014JC02020G07]
Hyperspectral images have been proved to be effective for a wide range of applications; however, the large volume and redundant information also bring a lot of inconvenience at the same time. To cope with this problem, hyperspectral band selection is a pertinent technique, which takes advantage of removing redundant components without compromising the original contents from the raw image cubes. Because of its usefulness, hyperspectral band selection has been successfully applied to many practical applications of hyperspectral remote sensing, such as land cover map generation and color visualization. This paper focuses on groupwise band selection and proposes a new framework, including the following contributions: 1) a smart yet intrinsic descriptor for efficient band representation; 2) an evolutionary strategy to handle the high computational burden associated with groupwise-selection-based methods; and 3) a novel MTSP-based criterion to evaluate the performance of each candidate band combination. To verify the superiority of the proposed framework, experiments have been conducted on both hyperspectral classification and color visualization. Experimental results on three real-world hyperspectral images demonstrate that the proposed framework can lead to a significant advancement in these two applications compared with other competitors.
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