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Proceedings Paper

Super-resolution mapping using multiple observations and Hopfield neural network
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Paper Abstract

Super-resolution mapping is used to produces thematic maps at a scale finer than the source images. This paper presents a new super-resolution mapping approach that exploits the typically fine temporal resolution of coarse spatial resolution images as it input and an adoption of an active threshold surface using Hopfield neural network as a means to map land cover at a sub-pixel scale. The results demonstrated that the proposed technique is slightly more accurate than the existence technique in terms of site specific accuracy and produce better visualization on individual land cover map.

Paper Details

Date Published: 13 October 2010
PDF: 9 pages
Proc. SPIE 7830, Image and Signal Processing for Remote Sensing XVI, 783003 (13 October 2010); doi: 10.1117/12.865092
Show Author Affiliations
Anuar M. Muad, The Univ. of Nottingham (United Kingdom)
Giles M. Foody, The Univ. of Nottingham (United Kingdom)


Published in SPIE Proceedings Vol. 7830:
Image and Signal Processing for Remote Sensing XVI
Lorenzo Bruzzone, Editor(s)

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