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), pp. 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.

Item Type: Article
ISSNs: 0099-1112 (print)
Related URLs:

ePrint ID: 54927
Date :
Date Event
July 2005Published
Date Deposited: 01 Aug 2008
Last Modified: 16 Apr 2017 17:45
Further Information:Google Scholar

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