期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 56, 期 11, 页码 6763-6778出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2018.2842748
关键词
Hyperspectral imaging; spectral unmixing; subpixel mapping; super-resolution mapping
类别
资金
- National Key Research and Development Program of China [2018YFB0505404]
- National Natural Science Foundation of China [41401398, 41325005, 41201426, 41171352, 41171327]
In this paper, a new joint spectral-spatial subpixel mapping model is proposed for hyperspectral remotely sensed imagery. Conventional approaches generally use an intermediate step based on the derivation of fractional abundance maps obtained after a spectral unmixing process, and thus the rich spectral information contained in the original hyperspectral data set may not be utilized fully. In this paper, a concept of subpixel abundance map, which calculates the abundance fraction of each subpixel to belong to a given class, was introduced. This allows us to directly connect the original (coarser) hyperspectral image with the final subpixel result. Furthermore, the proposed approach incorporates the spectral information contained in the original hyperspectral imagery and the concept of spatial dependence to generate a final subpixel mapping result. The proposed approach has been experimentally evaluated using both synthetic and real hyperspectral images, and the obtained results demonstrate that the method achieves better results when compared to other seven subpixel mapping methods. The numerical comparisons are based on different indexes such as the overall accuracy and the CPU time. Moreover, the obtained results are statistically significant at 95% confidence.
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