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

Land cover mapping at the sub-pixel scale using a Hopfield neural network
Land cover mapping at the sub-pixel scale using a Hopfield neural network
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 ‘bestguess’ 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.
47-51
International Symposium on Remote Sensing of the Environment
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Lewis, Hugh G.
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Atkinson, Peter
96e96579-56fe-424d-a21c-17b6eed13b0b
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Tatem, Andrew J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
Lewis, Hugh G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
Atkinson, Peter
96e96579-56fe-424d-a21c-17b6eed13b0b
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Tatem, Andrew J., Lewis, Hugh G., Atkinson, Peter and Nixon, Mark 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. 47-51 .

Record type: Conference or Workshop Item (Paper)

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 ‘bestguess’ 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.

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More information

Published date: March 2000
Additional Information: Organisation: CSIR, NASA, SAI, ESA...
Venue - Dates: 28th International Symposium on Remote Sensing of Environment, Cape Town, South Africa, South Africa, 2000-03-27 - 2000-03-31
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 252956
URI: http://eprints.soton.ac.uk/id/eprint/252956
PURE UUID: c0879901-c7d8-425e-bc40-a97960764423
ORCID for Andrew J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X
ORCID for Peter Atkinson: ORCID iD orcid.org/0000-0002-5489-6880
ORCID for Mark S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 11 Apr 2000
Last modified: 30 Jan 2020 01:38

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