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Super-resolution target identification from remotely sensed images using a Hopfield neural network

Super-resolution target identification from remotely sensed images using a Hopfield neural network
Super-resolution target identification from remotely sensed images using a Hopfield neural network
Fuzzy classification techniques have been developed recently to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the instantaneous field of view represented by the pixel. As such, while the accuracy of land cover target identification has been improved using fuzzy classification, it remains for robust techniques that provide better spatial representation of land cover to be developed. Such techniques could provide more accurate land cover metrics for determining social or environmental policy, for example. The use of a Hopfield neural network to map the spatial distribution of classes more reliably using prior information of pixel composition determined from fuzzy classification was investigated. An approach was adopted that used the output from a fuzzy classification to constrain a Hopfield neural network formulated as an energy minimization tool. The network converges to a minimum of an energy function, defined as a goal and several constraints. Extracting the spatial distribution of target class components within each pixel was, therefore, formulated as a constraint satisfaction problem with an optimal solution determined by the minimum of the energy function. This energy minimum represents a “best guess” map of the spatial distribution of class components in each pixel. The technique was applied to both synthetic and simulated Landsat TM imagery, and the resultant maps provided an accurate and improved representation of the land covers studied, with root mean square errors (RMSEs) for Landsat imagery of the order of 0.09 pixels in the new fine resolution image recorded
Hopfield neural nets, geophysical signal processing, geophysical techniques, geophysics computing, image classification, remote sensing, terrain mapping
0196-2892
781-96
Tatem, A.J.
ab877a1f-6cc7-4eae-8c15-bb299417223f
Lewis, H.G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Tatem, A.J.
ab877a1f-6cc7-4eae-8c15-bb299417223f
Lewis, H.G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
Atkinson, P.M.
96e96579-56fe-424d-a21c-17b6eed13b0b
Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12

Tatem, A.J., Lewis, H.G., Atkinson, P.M. and Nixon, M.S. (2001) Super-resolution target identification from remotely sensed images using a Hopfield neural network IEEE Transactions on Geoscience and Remote Sensing, 39, (4), pp. 781-96. (doi:10.1109/36.917895).

Record type: Article

Abstract

Fuzzy classification techniques have been developed recently to estimate the class composition of image pixels, but their output provides no indication of how these classes are distributed spatially within the instantaneous field of view represented by the pixel. As such, while the accuracy of land cover target identification has been improved using fuzzy classification, it remains for robust techniques that provide better spatial representation of land cover to be developed. Such techniques could provide more accurate land cover metrics for determining social or environmental policy, for example. The use of a Hopfield neural network to map the spatial distribution of classes more reliably using prior information of pixel composition determined from fuzzy classification was investigated. An approach was adopted that used the output from a fuzzy classification to constrain a Hopfield neural network formulated as an energy minimization tool. The network converges to a minimum of an energy function, defined as a goal and several constraints. Extracting the spatial distribution of target class components within each pixel was, therefore, formulated as a constraint satisfaction problem with an optimal solution determined by the minimum of the energy function. This energy minimum represents a “best guess” map of the spatial distribution of class components in each pixel. The technique was applied to both synthetic and simulated Landsat TM imagery, and the resultant maps provided an accurate and improved representation of the land covers studied, with root mean square errors (RMSEs) for Landsat imagery of the order of 0.09 pixels in the new fine resolution image recorded

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Published date: 2001
Keywords: Hopfield neural nets, geophysical signal processing, geophysical techniques, geophysics computing, image classification, remote sensing, terrain mapping

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Local EPrints ID: 21974
URI: http://eprints.soton.ac.uk/id/eprint/21974
ISSN: 0196-2892
PURE UUID: 9eb576f0-fd35-4027-9cbc-bc98ce485209

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Date deposited: 17 Mar 2006
Last modified: 17 Jul 2017 16:24

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