Superresolution mapping using a hopfield neural network with fused images
Superresolution mapping using a hopfield neural network with fused images
Superresolution mapping is a set of techniques to increase the spatial resolution of a land cover map obtained by soft classification methods. In addition to the information from the landcover proportion images, supplementary information at the subpixel level can be used to produce more detailed and accurate land cover maps. The proposed method in this research aims to use fused imagery as an additional source of information for superresolution mapping using the Hopfield neural network (HNN). Forward and inverse models were incorporated in the HNN to support a new reflectance constraint added to the energy function. The value of the function was calculated based on a linear mixture model. In addition, a new model was used to calculate the local end member spectra for the reflectance constraint. A set of simulated images was used to test the new technique. The results suggest thatfine spatial resolution fused imagery can be used as supplementary data for superresolution mapping from a coarser spatial resolution land cover proportion imagery.
fused images, hopfield neural network (hnn) optimization, soft classification, superresolution mapping
736-749
Nguyen, Minh Q.
a6a2e6ab-6ca9-4342-92b5-ea5987d478e7
Atkinson, Peter M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Lewis, Hugh G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
March 2006
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.
(2006)
Superresolution mapping using a hopfield neural network with fused images.
IEEE Transactions on Geoscience and Remote Sensing, 44 (3), .
(doi:10.1109/TGRS.2005.861752).
Abstract
Superresolution mapping is a set of techniques to increase the spatial resolution of a land cover map obtained by soft classification methods. In addition to the information from the landcover proportion images, supplementary information at the subpixel level can be used to produce more detailed and accurate land cover maps. The proposed method in this research aims to use fused imagery as an additional source of information for superresolution mapping using the Hopfield neural network (HNN). Forward and inverse models were incorporated in the HNN to support a new reflectance constraint added to the energy function. The value of the function was calculated based on a linear mixture model. In addition, a new model was used to calculate the local end member spectra for the reflectance constraint. A set of simulated images was used to test the new technique. The results suggest thatfine spatial resolution fused imagery can be used as supplementary data for superresolution mapping from a coarser spatial resolution land cover proportion imagery.
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Published date: March 2006
Keywords:
fused images, hopfield neural network (hnn) optimization, soft classification, superresolution mapping
Organisations:
Engineering Sciences
Identifiers
Local EPrints ID: 23469
URI: http://eprints.soton.ac.uk/id/eprint/23469
ISSN: 0196-2892
PURE UUID: 5cede2ea-ec6c-489d-b26d-27cab14b25bf
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Date deposited: 20 Mar 2006
Last modified: 16 Mar 2024 02:55
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Author:
Minh Q. Nguyen
Author:
Peter M. Atkinson
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