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Super-resolution mapping of multiple-scale land cover features using a Hopfield neural network

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Super-resolution mapping of multiple-scale land cover features using a Hopfield neural network. / Tatem, Andrew J.; Lewis, Hugh G.; Atkinson, Peter M. et al.
Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International. Vol. 7 Sydney, Australia;Sydney, Australia, 2001. p. 3200-3202.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Tatem, AJ, Lewis, HG, Atkinson, PM & Nixon, MS 2001, Super-resolution mapping of multiple-scale land cover features using a Hopfield neural network. in Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International. vol. 7, Sydney, Australia;Sydney, Australia, pp. 3200-3202. https://doi.org/10.1109/IGARSS.2001.978302

APA

Tatem, A. J., Lewis, H. G., Atkinson, P. M., & Nixon, M. S. (2001). Super-resolution mapping of multiple-scale land cover features using a Hopfield neural network. In Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International (Vol. 7, pp. 3200-3202). https://doi.org/10.1109/IGARSS.2001.978302

Vancouver

Tatem AJ, Lewis HG, Atkinson PM, Nixon MS. Super-resolution mapping of multiple-scale land cover features using a Hopfield neural network. In Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International. Vol. 7. Sydney, Australia;Sydney, Australia. 2001. p. 3200-3202 doi: 10.1109/IGARSS.2001.978302

Author

Tatem, Andrew J. ; Lewis, Hugh G. ; Atkinson, Peter M. et al. / Super-resolution mapping of multiple-scale land cover features using a Hopfield neural network. Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International. Vol. 7 Sydney, Australia;Sydney, Australia, 2001. pp. 3200-3202

Bibtex

@inproceedings{5a3578e733144a7aab2197a5ff6ece06,
title = "Super-resolution mapping of multiple-scale land cover features using a Hopfield neural network",
abstract = "Soft classification techniques have been developed to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the pixel. Separate Hopfield neural network techniques for producing super-resolution maps from imagery of features larger and smaller than a pixel have been developed. However, the techniques have yet to be combined in order to produce super-resolution maps of multiple-scale land cover features. This paper presents the first results from combining the two approaches. The output from a soft classification and prior information of sub-pixel feature arrangement is used to constrain a Hopfield neural network formulated as an energy minimisation tool. The energy minimum represents a 'best guess' map of the spatial distribution of class components in each pixel. The technique was applied to simulated SPOT HRV imagery and the resultant maps provided an accurate and improved representation of the land covers studied",
author = "Tatem, {Andrew J.} and Lewis, {Hugh G.} and Atkinson, {Peter M.} and Nixon, {Mark S.}",
year = "2001",
doi = "10.1109/IGARSS.2001.978302",
language = "English",
isbn = "0780370317",
volume = "7",
pages = "3200--3202",
booktitle = "Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International",

}

RIS

TY - GEN

T1 - Super-resolution mapping of multiple-scale land cover features using a Hopfield neural network

AU - Tatem, Andrew J.

AU - Lewis, Hugh G.

AU - Atkinson, Peter M.

AU - Nixon, Mark S.

PY - 2001

Y1 - 2001

N2 - Soft classification techniques have been developed to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the pixel. Separate Hopfield neural network techniques for producing super-resolution maps from imagery of features larger and smaller than a pixel have been developed. However, the techniques have yet to be combined in order to produce super-resolution maps of multiple-scale land cover features. This paper presents the first results from combining the two approaches. The output from a soft classification and prior information of sub-pixel feature arrangement is used to constrain a Hopfield neural network formulated as an energy minimisation tool. The energy minimum represents a 'best guess' map of the spatial distribution of class components in each pixel. The technique was applied to simulated SPOT HRV imagery and the resultant maps provided an accurate and improved representation of the land covers studied

AB - Soft classification techniques have been developed to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the pixel. Separate Hopfield neural network techniques for producing super-resolution maps from imagery of features larger and smaller than a pixel have been developed. However, the techniques have yet to be combined in order to produce super-resolution maps of multiple-scale land cover features. This paper presents the first results from combining the two approaches. The output from a soft classification and prior information of sub-pixel feature arrangement is used to constrain a Hopfield neural network formulated as an energy minimisation tool. The energy minimum represents a 'best guess' map of the spatial distribution of class components in each pixel. The technique was applied to simulated SPOT HRV imagery and the resultant maps provided an accurate and improved representation of the land covers studied

U2 - 10.1109/IGARSS.2001.978302

DO - 10.1109/IGARSS.2001.978302

M3 - Conference contribution/Paper

SN - 0780370317

VL - 7

SP - 3200

EP - 3202

BT - Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International

CY - Sydney, Australia;Sydney, Australia

ER -