Land cover mapping at the sub-pixel scale using a Hopfield neural network


Tatem, A.J., Lewis, H.G., Atkinson, P.M. and Nixon, M.S. (2000) Land cover mapping at the sub-pixel scale using a Hopfield neural network In Proceedings of the 28th International Symposium on Remote Sensing of the Environment. International Symposium on Remote Sensing of the Environment., pp. 47-50.

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Description/Abstract

Fuzzy classification techniques have been developed to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the pixel. As such, it remains for robust techniques that provide improved spatial representation of land cover to be developed. The use of a Hopfield neural network to map the spatial distribution of classes more reliably was investigated. An approach was adopted which used the output from a fuzzy classification to constrain a Hopfield neural network formulated as an energy minimisation tool. The energy minimum represents a ‘best guess’ map of the spatial distribution of class components in each pixel. The technique was applied to both synthetic and Landsat TM imagery. The resultant maps provided an accurate and improved representation of the land covers studied. The Hopfield neural network used in this way represents a simple, robust and efficient tool for mapping land cover from remotely sensed imagery at the sub-pixel scale

Item Type: Conference or Workshop Item (Paper)
Venue - Dates: Proceedings of the28th International Symposium on Remote Sensing of the Environment, 2000-03-27 - 2000-03-31
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ePrint ID: 21622
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Date Event
2000Published
Date Deposited: 27 Feb 2007
Last Modified: 16 Apr 2017 22:55
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/21622

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