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Superresolution mapping using a Hopfield neural network with lidar data

Superresolution mapping using a Hopfield neural network with lidar data
Superresolution mapping using a Hopfield neural network with lidar data
Superresolution mapping is a set of techniques to obtain a subpixel map from land cover proportion images produced by soft classification. Together with the information from the land cover proportion images, supplementary information at the subpixel level can be used to produce more detailed and accurate land cover maps. This research aims to use the elevation data from light detection and ranging (lidar) as an additional source of information for superresolution mapping using the Hopfield neural network (HNN). A new height function was added to the energy function of the HNN for superresolution mapping. The value of the height function was calculated for each subpixel of a certain class based on the Gaussian distribution. A set of simulated data was used to test the new technique. The results suggest that 0.8-m spatial resolution digital surface models can be combined with optical data at 4-m spatial resolution for superresolution mapping.
data fusion, hopfield neural network (hnn) optimization, light detection and ranging (lidar), superresolution mapping
1545-598X
366-370
Nguyen, Minh Q.
a6a2e6ab-6ca9-4342-92b5-ea5987d478e7
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Lewis, Hugh G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
Nguyen, Minh Q.
a6a2e6ab-6ca9-4342-92b5-ea5987d478e7
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Lewis, Hugh G.
e9048cd8-c188-49cb-8e2a-45f6b316336a

Nguyen, Minh Q., Atkinson, Peter M. and Lewis, Hugh G. (2005) Superresolution mapping using a Hopfield neural network with lidar data. IEEE Geoscience and Remote Sensing Letters, 2 (3), 366-370. (doi:10.1109/LGRS.2005.851551).

Record type: Article

Abstract

Superresolution mapping is a set of techniques to obtain a subpixel map from land cover proportion images produced by soft classification. Together with the information from the land cover proportion images, supplementary information at the subpixel level can be used to produce more detailed and accurate land cover maps. This research aims to use the elevation data from light detection and ranging (lidar) as an additional source of information for superresolution mapping using the Hopfield neural network (HNN). A new height function was added to the energy function of the HNN for superresolution mapping. The value of the height function was calculated for each subpixel of a certain class based on the Gaussian distribution. A set of simulated data was used to test the new technique. The results suggest that 0.8-m spatial resolution digital surface models can be combined with optical data at 4-m spatial resolution for superresolution mapping.

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More information

Published date: 2005
Keywords: data fusion, hopfield neural network (hnn) optimization, light detection and ranging (lidar), superresolution mapping

Identifiers

Local EPrints ID: 23467
URI: http://eprints.soton.ac.uk/id/eprint/23467
ISSN: 1545-598X
PURE UUID: 8a1322d2-354a-4f3b-8caa-95fad792e763
ORCID for Peter M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880
ORCID for Hugh G. Lewis: ORCID iD orcid.org/0000-0002-3946-8757

Catalogue record

Date deposited: 17 Mar 2006
Last modified: 16 Mar 2024 02:55

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Contributors

Author: Minh Q. Nguyen
Author: Peter M. Atkinson ORCID iD
Author: Hugh G. Lewis ORCID iD

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