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Multiple-class land-cover mapping at the sub-pixel scale using a Hopfield neural network

Multiple-class land-cover mapping at the sub-pixel scale using a Hopfield neural network
Multiple-class land-cover mapping at the sub-pixel scale using a Hopfield neural network
Land cover class composition of image pixels can be estimated using soft classification techniques. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Robust techniques to provide an improved spatial representation of land cover have yet to be developed. The use of a Hopfield neural network technique to map the spatial distributions of classes reliably using information of pixel composition determined from soft classification was investigated in previous papers by Tatem et al. The network converges to a minimum of an energy function defined as a goal and several constraints. The approach involved designing the energy function to produce a ‘best guess’ prediction of the spatial distribution of class components in each pixel. Tatem et al described the application of the technique to target mapping at the sub-pixel scale, but only for single classes. We now show how this approach can be extended to map multiple classes at the sub-pixel scale, by adding new constraints into the energy formulation. The new technique has been applied to simulated SPOT HRV and Landsat TM agriculture imagery to derive accurate estimates of land cover. The results show that this extension of the neural network now represents a simple efficient tool for mapping land cover and can deliver requisite results for the analysis of practical remotely sensed imagery at the sub pixel scale.
remote sensing, spatial resolution, soft classification, optimization, neurons, energy function, constraints, accuracy assessment
0303-2434
184-190
Tatem, Andrew J.
cac5d599-ac59-4a77-8dd9-b39f8428fdcb
Lewis, Hugh G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Tatem, Andrew J.
cac5d599-ac59-4a77-8dd9-b39f8428fdcb
Lewis, Hugh G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Tatem, Andrew J., Lewis, Hugh G., Atkinson, Peter M. and Nixon, Mark S. (2002) Multiple-class land-cover mapping at the sub-pixel scale using a Hopfield neural network. International Journal of Applied Earth Observation and Geoinformation, 3 (2), 184-190. (doi:10.1016/S0303-2434(01)85010-8).

Record type: Article

Abstract

Land cover class composition of image pixels can be estimated using soft classification techniques. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Robust techniques to provide an improved spatial representation of land cover have yet to be developed. The use of a Hopfield neural network technique to map the spatial distributions of classes reliably using information of pixel composition determined from soft classification was investigated in previous papers by Tatem et al. The network converges to a minimum of an energy function defined as a goal and several constraints. The approach involved designing the energy function to produce a ‘best guess’ prediction of the spatial distribution of class components in each pixel. Tatem et al described the application of the technique to target mapping at the sub-pixel scale, but only for single classes. We now show how this approach can be extended to map multiple classes at the sub-pixel scale, by adding new constraints into the energy formulation. The new technique has been applied to simulated SPOT HRV and Landsat TM agriculture imagery to derive accurate estimates of land cover. The results show that this extension of the neural network now represents a simple efficient tool for mapping land cover and can deliver requisite results for the analysis of practical remotely sensed imagery at the sub pixel scale.

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More information

Published date: 1 August 2002
Keywords: remote sensing, spatial resolution, soft classification, optimization, neurons, energy function, constraints, accuracy assessment
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 256409
URI: http://eprints.soton.ac.uk/id/eprint/256409
ISSN: 0303-2434
PURE UUID: 016c9d17-5733-4222-ad83-6ed7bde887f9
ORCID for Hugh G. Lewis: ORCID iD orcid.org/0000-0002-3946-8757
ORCID for Peter M. 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: 20 Nov 2003
Last modified: 15 Mar 2024 02:54

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Contributors

Author: Andrew J. Tatem
Author: Hugh G. Lewis ORCID iD
Author: Peter M. Atkinson ORCID iD
Author: Mark S. Nixon ORCID iD

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