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.
geophysical-measurement-technique, land-surface, terrain-mapping, remote-sensing, super-resolution-mapping, land-cover, multiple-scale-features, image-classification, soft-classification, class-composition, Hopfield-neural-net, neural-net, super-resolution-maps, sub-pixel-feature, energy-minimisation-tool, multiscale-feature, energy-minimum, best-guess, spatial-distribution, visible-, IR-, infrared-, multispectral-remote-sensing
3200 -3202
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
2001
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.
IEEE.
.
(doi:10.1109/IGARSS.2001.978302).
Record type:
Conference or Workshop Item
(Paper)
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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Published date: 2001
Additional Information:
Event Dates: July, 2001
Venue - Dates:
Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International, Sydney, Australia, 2001-07-09 - 2001-07-13
Keywords:
geophysical-measurement-technique, land-surface, terrain-mapping, remote-sensing, super-resolution-mapping, land-cover, multiple-scale-features, image-classification, soft-classification, class-composition, Hopfield-neural-net, neural-net, super-resolution-maps, sub-pixel-feature, energy-minimisation-tool, multiscale-feature, energy-minimum, best-guess, spatial-distribution, visible-, IR-, infrared-, multispectral-remote-sensing
Organisations:
Southampton Wireless Group
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Local EPrints ID: 256781
URI: http://eprints.soton.ac.uk/id/eprint/256781
PURE UUID: 5eb44c5f-ac3f-45ea-95ec-b41af34b56bb
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Date deposited: 27 Sep 2002
Last modified: 16 Mar 2024 02:55
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Author:
A.J. Tatem
Author:
P.M. Atkinson
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