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Superresolution mapping using a Hopfield neural network with fused images

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Superresolution mapping using a Hopfield neural network with fused images. / Minh, M. Q.; Atkinson, Peter M.; Lewis, Hugh G.
In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 44, No. 3, 03.2006, p. 736-749.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Minh, MQ, Atkinson, PM & Lewis, HG 2006, 'Superresolution mapping using a Hopfield neural network with fused images', IEEE Transactions on Geoscience and Remote Sensing, vol. 44, no. 3, pp. 736-749. https://doi.org/10.1109/TGRS.2005.861752

APA

Minh, M. Q., Atkinson, P. M., & Lewis, H. G. (2006). Superresolution mapping using a Hopfield neural network with fused images. IEEE Transactions on Geoscience and Remote Sensing, 44(3), 736-749. https://doi.org/10.1109/TGRS.2005.861752

Vancouver

Minh MQ, Atkinson PM, Lewis HG. Superresolution mapping using a Hopfield neural network with fused images. IEEE Transactions on Geoscience and Remote Sensing. 2006 Mar;44(3):736-749. doi: 10.1109/TGRS.2005.861752

Author

Minh, M. Q. ; Atkinson, Peter M. ; Lewis, Hugh G. / Superresolution mapping using a Hopfield neural network with fused images. In: IEEE Transactions on Geoscience and Remote Sensing. 2006 ; Vol. 44, No. 3. pp. 736-749.

Bibtex

@article{6423363912a648d88e6160baa58c4f54,
title = "Superresolution mapping using a Hopfield neural network with fused images",
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 land cover 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 endmember spectra for the reflectance constraint. A set of simulated images was used to test the new technique. The results suggest that fine spatial resolution fused imagery can be used as supplementary data for superresolution mapping from a coarser spatial resolution land cover proportion imagery.",
author = "Minh, {M. Q.} and Atkinson, {Peter M.} and Lewis, {Hugh G.}",
note = "M1 - 3",
year = "2006",
month = mar,
doi = "10.1109/TGRS.2005.861752",
language = "English",
volume = "44",
pages = "736--749",
journal = "IEEE Transactions on Geoscience and Remote Sensing",
issn = "0196-2892",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
number = "3",

}

RIS

TY - JOUR

T1 - Superresolution mapping using a Hopfield neural network with fused images

AU - Minh, M. Q.

AU - Atkinson, Peter M.

AU - Lewis, Hugh G.

N1 - M1 - 3

PY - 2006/3

Y1 - 2006/3

N2 - 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 land cover 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 endmember spectra for the reflectance constraint. A set of simulated images was used to test the new technique. The results suggest that fine spatial resolution fused imagery can be used as supplementary data for superresolution mapping from a coarser spatial resolution land cover proportion imagery.

AB - 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 land cover 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 endmember spectra for the reflectance constraint. A set of simulated images was used to test the new technique. The results suggest that fine spatial resolution fused imagery can be used as supplementary data for superresolution mapping from a coarser spatial resolution land cover proportion imagery.

U2 - 10.1109/TGRS.2005.861752

DO - 10.1109/TGRS.2005.861752

M3 - Journal article

VL - 44

SP - 736

EP - 749

JO - IEEE Transactions on Geoscience and Remote Sensing

JF - IEEE Transactions on Geoscience and Remote Sensing

SN - 0196-2892

IS - 3

ER -