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Super-resolution mapping using Hopfield neural network with panchromatic imagery

Super-resolution mapping using Hopfield neural network with panchromatic imagery
Super-resolution mapping using Hopfield neural network with panchromatic imagery
Land-cover proportions of mixed pixels can be predicted using soft classification. From the land-cover proportions, a hard land-cover map can be predicted at sub-pixel spatial resolution using super-resolution mapping techniques. It has been demonstrated that the Hopfield Neural Network (HNN) provides a suitable method for super-resolution mapping. To increase the detail and accuracy of the sub-pixel land-cover map, supplementary information at an intermediate spatial resolution can be used. In this research, panchromatic (PAN) imagery was used as an additional source of information for super-resolution mapping. Information from the PAN image was captured by a new PAN reflectance constraint in the energy function of the HNN. The value of the new PAN reflectance constraint was defined based on forward and inverse models with local end-member spectra and local convolution weighting factors. Two sets of simulated and degraded data were used to test the new technique. The results indicate that PAN imagery can be used as a source of supplementary information to increase the detail and accuracy of sub-pixel land-cover maps produced by super-resolution mapping from land-cover proportion images.
0143-1161
6149-6176
Minh, Nguyen
5f132ca1-5e0e-47a8-befb-6bbe8ea6add7
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Lewis, H.G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
Minh, Nguyen
5f132ca1-5e0e-47a8-befb-6bbe8ea6add7
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Lewis, H.G.
e9048cd8-c188-49cb-8e2a-45f6b316336a

Minh, Nguyen, Atkinson, P.M. and Lewis, H.G. (2011) Super-resolution mapping using Hopfield neural network with panchromatic imagery. International Journal of Remote Sensing, 32 (21), 6149-6176. (doi:10.1080/01431161.2010.507797).

Record type: Article

Abstract

Land-cover proportions of mixed pixels can be predicted using soft classification. From the land-cover proportions, a hard land-cover map can be predicted at sub-pixel spatial resolution using super-resolution mapping techniques. It has been demonstrated that the Hopfield Neural Network (HNN) provides a suitable method for super-resolution mapping. To increase the detail and accuracy of the sub-pixel land-cover map, supplementary information at an intermediate spatial resolution can be used. In this research, panchromatic (PAN) imagery was used as an additional source of information for super-resolution mapping. Information from the PAN image was captured by a new PAN reflectance constraint in the energy function of the HNN. The value of the new PAN reflectance constraint was defined based on forward and inverse models with local end-member spectra and local convolution weighting factors. Two sets of simulated and degraded data were used to test the new technique. The results indicate that PAN imagery can be used as a source of supplementary information to increase the detail and accuracy of sub-pixel land-cover maps produced by super-resolution mapping from land-cover proportion images.

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More information

Published date: 12 July 2011
Organisations: Global Env Change & Earth Observation

Identifiers

Local EPrints ID: 343290
URI: http://eprints.soton.ac.uk/id/eprint/343290
ISSN: 0143-1161
PURE UUID: 31128ef9-4dbf-4f79-bdb3-7be4f597d1eb
ORCID for P.M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880
ORCID for H.G. Lewis: ORCID iD orcid.org/0000-0002-3946-8757

Catalogue record

Date deposited: 03 Oct 2012 09:53
Last modified: 15 Mar 2024 02:54

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Contributors

Author: Nguyen Minh
Author: P.M. Atkinson ORCID iD
Author: H.G. Lewis ORCID iD

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