Super-resolution target mapping from soft classified remotely sensed imagery
Atkinson, P.M. (2005) Super-resolution target mapping from soft classified remotely sensed imagery. Photogrammetric Engineering and Remote Sensing, 71, (7), 839-846.
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A simple, efficient algorithm is presented for sub-pixel target mapping from remotely-sensed images. Following an initial random allocation of “soft” pixel proportions to “hard” sub-pixel binary classes, the algorithm works in a series of iterations, each of which contains three stages. For each pixel, for all sub-pixel locations, a distance-weighted function of neighboring sub-pixels is computed. Then, for each pixel, the sub-pixel representing the target class with the minimum value of the function, and the sub-pixel representing the background with the maximum value of the function are found. Third, these two sub-pixels are swapped if the swap results in an increase in spatial correlation between sub-pixels. The new algorithm predicted accurately when applied to simple simulated and real images. It represents an accessible tool that can be coded and applied readily by remote sensing investigators.
|Subjects:||G Geography. Anthropology. Recreation > G Geography (General)
G Geography. Anthropology. Recreation > GE Environmental Sciences
G Geography. Anthropology. Recreation > GB Physical geography
|Divisions :||University Structure - Pre August 2011 > School of Geography > Remote Sensing and Spatial Analysis
|Accepted Date and Publication Date:||
|Date Deposited:||01 Aug 2008|
|Last Modified:||31 Mar 2016 12:34|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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