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Superresolution mapping using a Hopfield neural network with lidar data

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Superresolution mapping using a Hopfield neural network with lidar data. / Nguyen, Minh Q.; Atkinson, Peter M.; Lewis, Hugh G.
In: IEEE Geoscience and Remote Sensing Letters, Vol. 2, No. 3, 07.2005, p. 366-370.

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

Harvard

Nguyen, MQ, Atkinson, PM & Lewis, HG 2005, 'Superresolution mapping using a Hopfield neural network with lidar data', IEEE Geoscience and Remote Sensing Letters, vol. 2, no. 3, pp. 366-370. https://doi.org/10.1109/LGRS.2005.851551

APA

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

Vancouver

Nguyen MQ, Atkinson PM, Lewis HG. Superresolution mapping using a Hopfield neural network with lidar data. IEEE Geoscience and Remote Sensing Letters. 2005 Jul;2(3):366-370. doi: 10.1109/LGRS.2005.851551

Author

Nguyen, Minh Q. ; Atkinson, Peter M. ; Lewis, Hugh G. / Superresolution mapping using a Hopfield neural network with lidar data. In: IEEE Geoscience and Remote Sensing Letters. 2005 ; Vol. 2, No. 3. pp. 366-370.

Bibtex

@article{58d8f67f6b9a4cfcb04935ec391b38b4,
title = "Superresolution mapping using a Hopfield neural network with lidar data",
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.",
author = "Nguyen, {Minh Q.} and Atkinson, {Peter M.} and Lewis, {Hugh G.}",
note = "M1 - 3",
year = "2005",
month = jul,
doi = "10.1109/LGRS.2005.851551",
language = "English",
volume = "2",
pages = "366--370",
journal = "IEEE Geoscience and Remote Sensing Letters",
issn = "1545-598X",
publisher = "IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC",
number = "3",

}

RIS

TY - JOUR

T1 - Superresolution mapping using a Hopfield neural network with lidar data

AU - Nguyen, Minh Q.

AU - Atkinson, Peter M.

AU - Lewis, Hugh G.

N1 - M1 - 3

PY - 2005/7

Y1 - 2005/7

N2 - 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.

AB - 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.

U2 - 10.1109/LGRS.2005.851551

DO - 10.1109/LGRS.2005.851551

M3 - Journal article

VL - 2

SP - 366

EP - 370

JO - IEEE Geoscience and Remote Sensing Letters

JF - IEEE Geoscience and Remote Sensing Letters

SN - 1545-598X

IS - 3

ER -