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Super-resolution mapping of multiple-scale land cover features using a Hopfield neural network

Super-resolution mapping of multiple-scale land cover features using a Hopfield neural network
Super-resolution mapping of multiple-scale land cover features using a Hopfield neural network
Soft 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. Separate Hopfield neural network techniques for producing super-resolution maps from imagery of features larger and smaller than a pixel have been developed. However, the techniques have yet to be combined in order to produce super-resolution maps of multiple-scale land cover features. This paper presents the first results from combining the two approaches. The output from a soft classification and prior information of sub-pixel feature arrangement is used 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 simulated SPOT HRV imagery and the resultant maps provided an accurate and improved representation of the land covers studied
0780370317
3200-3202
IEEE
Tatem, A.J.
ab877a1f-6cc7-4eae-8c15-bb299417223f
Lewis, H.G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Tatem, A.J.
ab877a1f-6cc7-4eae-8c15-bb299417223f
Lewis, H.G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Tatem, A.J., Lewis, H.G., Atkinson, P.M. and Nixon, M.S. (2001) Super-resolution mapping of multiple-scale land cover features using a Hopfield neural network In, Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International. Sydney, Australia, IEEE pp. 3200-3202. (doi:10.1109/IGARSS.2001.978302).

Record type: Book Section

Abstract

Soft 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. Separate Hopfield neural network techniques for producing super-resolution maps from imagery of features larger and smaller than a pixel have been developed. However, the techniques have yet to be combined in order to produce super-resolution maps of multiple-scale land cover features. This paper presents the first results from combining the two approaches. The output from a soft classification and prior information of sub-pixel feature arrangement is used 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 simulated SPOT HRV imagery and the resultant maps provided an accurate and improved representation of the land covers studied

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More information

Published date: 2001
Venue - Dates: Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International, 2001-07-09 - 2001-07-13

Identifiers

Local EPrints ID: 17681
URI: http://eprints.soton.ac.uk/id/eprint/17681
ISBN: 0780370317
PURE UUID: 4e18e2f6-d680-4f7a-91a1-3ef2d8953fe2

Catalogue record

Date deposited: 25 Oct 2005
Last modified: 17 Jul 2017 16:37

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