Sub-pixel mapping of remotely sensed imagery with hybrid intra- and inter-pixel dependence
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.
Document Type: Research Article
Affiliations: Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, China
Publication date: 10 January 2013
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