The University of Southampton
University of Southampton Institutional Repository

Super-Resolution Land Cover Pattern Prediction Using a Hopfield Neural Network

Super-Resolution Land Cover Pattern Prediction Using a Hopfield Neural Network
Super-Resolution Land Cover Pattern Prediction Using a Hopfield Neural Network
Landscape pattern represents a key variable in management and understanding of the environment, as well as driving many environmental models. Remote sensing can be used to provide information on the spatial pattern of land cover features, but analysis and classification of such imagery suffers from the problem of class mixing within pixels. Fuzzy classification techniques can estimate the class composition of image pixels. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Techniques to provide an improved spatial representation of land cover targets larger than the size of a pixel have been developed, however, the mapping of sub-pixel scale land cover features has yet to be investigated. We recently described the application of a Hopfield neural network technique to super-resolution mapping of land cover features larger than a pixel (Tatem et al., 2000), using information of pixel composition determined from fuzzy classification, and (was but) now show how our approach can be extended in a new way (added) to predict the spatial pattern of sub-pixel scale features. The network converges to a minimum of an energy function defined as a goal and several constraints. Prior information on the typical spatial arrangement of the particular land cover types is incorporated into the energy function as a constraint. This produces a prediction of the spatial pattern of the land cover in question, at the sub-pixel scale. The technique is applied to synthetic and simulated Landsat TM imagery, and compared to results of an existing super-resolution target identification technique. Results show that the new approach (was Hopfield neural network) represents a simple, robust and efficient tool for super-resolution land cover pattern prediction from remotely sensed imagery.
0034-4257
1-14
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. (2002) Super-Resolution Land Cover Pattern Prediction Using a Hopfield Neural Network. Remote Sensing of Environment, 79 (1), 1-14. (doi:10.1016/S0034-4257(01)00229-2).

Record type: Article

Abstract

Landscape pattern represents a key variable in management and understanding of the environment, as well as driving many environmental models. Remote sensing can be used to provide information on the spatial pattern of land cover features, but analysis and classification of such imagery suffers from the problem of class mixing within pixels. Fuzzy classification techniques can estimate the class composition of image pixels. However, their output provides no indication of how such classes are distributed spatially within the instantaneous field of view represented by the pixel. Techniques to provide an improved spatial representation of land cover targets larger than the size of a pixel have been developed, however, the mapping of sub-pixel scale land cover features has yet to be investigated. We recently described the application of a Hopfield neural network technique to super-resolution mapping of land cover features larger than a pixel (Tatem et al., 2000), using information of pixel composition determined from fuzzy classification, and (was but) now show how our approach can be extended in a new way (added) to predict the spatial pattern of sub-pixel scale features. The network converges to a minimum of an energy function defined as a goal and several constraints. Prior information on the typical spatial arrangement of the particular land cover types is incorporated into the energy function as a constraint. This produces a prediction of the spatial pattern of the land cover in question, at the sub-pixel scale. The technique is applied to synthetic and simulated Landsat TM imagery, and compared to results of an existing super-resolution target identification technique. Results show that the new approach (was Hopfield neural network) represents a simple, robust and efficient tool for super-resolution land cover pattern prediction from remotely sensed imagery.

Text
tatem_rse.pdf - Other
Download (819kB)

More information

Submitted date: 20 November 2000
Published date: January 2002
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 256187
URI: http://eprints.soton.ac.uk/id/eprint/256187
ISSN: 0034-4257
PURE UUID: b71f8b30-46f8-4766-b5c2-6cbdf29bd71f
ORCID for P.M. Atkinson: ORCID iD orcid.org/0000-0002-5489-6880
ORCID for M.S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 05 Mar 2004
Last modified: 10 Dec 2019 01:59

Export record

Altmetrics

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×