期刊
INTERNATIONAL JOURNAL OF REMOTE SENSING
卷 34, 期 1, 页码 341-357出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/01431161.2012.705441
关键词
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资金
- National Natural Science Foundation of China [40801186, 40801045]
- Wuhan Youth Chenguang Project [200950431218]
- Knowledge Innovation Programme of the Chinese Academy of Sciences [kzcx2-yw-141]
Sub-pixel mapping of remotely sensed imagery is often performed by assuming that land cover is spatially dependent both within and between image pixels. Intra- and inter-pixel dependencies are two widely used approaches to represent different land-cover spatial dependencies at present. However, merely using intra-or inter-pixel dependence alone often fails to fully describe land-cover spatial dependence, making current sub-pixel mapping models defective. A more reasonable object for sub-pixel mapping is maximizing both intra-and inter-pixel dependencies simultaneously instead of using only one of them. In this article, the differences between intra-and inter-pixel dependencies are discussed theoretically, and a novel sub-pixel mapping model aiming to maximize hybrid intra-and inter-pixel dependence is proposed. In the proposed model, spatial dependence is formulated as a weighted sum of intra-pixel dependence and inter-pixel dependence to satisfy both intra-and inter-pixel dependencies. By application to artificial and synthetic images, the proposed model was evaluated both visually and quantitatively by comparing with three representative sub-pixel mapping algorithms: the pixel swapping algorithm, the sub-pixel/pixel attraction algorithm, and the pixel swapping initialized with sub-pixel/pixel attraction algorithm. The results showed increased accuracy of the proposed algorithm when compared with these traditional sub-pixel mapping algorithms.
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