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Super-resolution mapping of urban scenes from IKONOS imagery using a Hopfield neural network

Super-resolution mapping of urban scenes from IKONOS imagery using a Hopfield neural network
Super-resolution mapping of urban scenes from IKONOS imagery using a Hopfield neural network
The availability of 4-metre spatial resolution satellite sensor imagery represents an important step in the automated mapping of urban scenes. However, a large amount of class mixing is still evident within such imagery, making traditional 'hard' classification inappropriate for urban land cover mapping. 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. This paper examines the potential usage of a Hopfield neural network technique for super-resolution mapping of urban land cover from IKONOS imagery, using information of pixel composition determined from soft classification. 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. The results show that the Hopfield neural network represents a simple efficient tool for mapping urban land cover from IKONOS imagery, and can deliver requisite results for the analysis of practical remotely sensed imagery at the sub pixel scale.
satellite-sensor-imagery, super-resolution-mapping, urban-scenes, IKONOS-imagery, automated-mapping, class-mixing, image-classification, urban-land-cover-mapping, land-cover-class-composition, classification-techniques, Hopfield-neural-network-technique, pixel-composition, energy-function, spatial-distribution, remotely-sensed-imagery
3203-3205
Tatem, A.J.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
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.
6c6de104-a5f9-46e0-bb93-a1a7c980513e
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 urban scenes from IKONOS imagery using a Hopfield neural network. IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2001) Scanning The Present and Resolving the Future, Sydney, Australia. 09 - 13 Jul 2001. pp. 3203-3205 . (doi:10.1109/IGARSS.2001.978303).

Record type: Conference or Workshop Item (Other)

Abstract

The availability of 4-metre spatial resolution satellite sensor imagery represents an important step in the automated mapping of urban scenes. However, a large amount of class mixing is still evident within such imagery, making traditional 'hard' classification inappropriate for urban land cover mapping. 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. This paper examines the potential usage of a Hopfield neural network technique for super-resolution mapping of urban land cover from IKONOS imagery, using information of pixel composition determined from soft classification. 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. The results show that the Hopfield neural network represents a simple efficient tool for mapping urban land cover from IKONOS imagery, 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: 2001
Additional Information: Proceedings of IGARSS 2001 Scanning the Present and Resolving the Future Sydney, Australia ISBN 0780370317
Venue - Dates: IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2001) Scanning The Present and Resolving the Future, Sydney, Australia, 2001-07-09 - 2001-07-13
Keywords: satellite-sensor-imagery, super-resolution-mapping, urban-scenes, IKONOS-imagery, automated-mapping, class-mixing, image-classification, urban-land-cover-mapping, land-cover-class-composition, classification-techniques, Hopfield-neural-network-technique, pixel-composition, energy-function, spatial-distribution, remotely-sensed-imagery
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 256779
URI: https://eprints.soton.ac.uk/id/eprint/256779
PURE UUID: ff7ea14e-2f40-45c8-a3cf-93a7f108b206
ORCID for A.J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X
ORCID for P.M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880
ORCID for M.S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

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Date deposited: 27 Sep 2002
Last modified: 18 May 2019 00:38

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Contributors

Author: A.J. Tatem ORCID iD
Author: H.G. Lewis
Author: P.M. Atkinson ORCID iD
Author: M.S. Nixon ORCID iD

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