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Sub-pixal target mapping from soft classified remotely sensed imagery

Research output: Contribution to journalJournal articlepeer-review

Published
<mark>Journal publication date</mark>07/2005
<mark>Journal</mark>Photogrammetric Engineering and Remote Sensing
Issue number7
Volume71
Number of pages8
Pages (from-to)839-846
Publication StatusPublished
<mark>Original language</mark>English

Abstract

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” subpixel 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.

Bibliographic note

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