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On appropriate modelling strategies for estimating land cover areas from satellite imagery

On appropriate modelling strategies for estimating land cover areas from satellite imagery
On appropriate modelling strategies for estimating land cover areas from satellite imagery
The mapping of land cover and land use is a key application of remotely sensed data. Studies have suggested the outputs of statistical models that estimate the posterior probability of class membership can be interpreted as subpixel area proportions. This paper examines the correlation between posterior probability of class membership, estimated using neural network and nearest neighbour models, and area proportion. In addition, the paper describes several models, again based on neural networks and nearest neighbour algorithms, that have been developed to estimate the land cover area proportions explicitly. Both types of model were applied to a Landsat TM data set. The results demonstrated that better estimates of the true land cover area were obtained using models that predicted the area proportion directly than were obtained using models that predicted the posterior probability of class membership. Further, it was found that a linear model (single-layer neural network) and a nearest neighbour smoothing model produced higher correlation and lower errors than the other models investigated.
443-448
IEEE
Lewis, H.G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049
Lewis, H.G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
Nixon, M.S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Brown, M.
52cf4f52-6839-4658-8cc5-ec51da626049

Lewis, H.G., Nixon, M.S. and Brown, M. (1999) On appropriate modelling strategies for estimating land cover areas from satellite imagery. In Proceedings of the 9th International Conference on Artificial Neural Networks (ICANN '99). IEEE. pp. 443-448 . (doi:10.1049/cp:19991149).

Record type: Conference or Workshop Item (Paper)

Abstract

The mapping of land cover and land use is a key application of remotely sensed data. Studies have suggested the outputs of statistical models that estimate the posterior probability of class membership can be interpreted as subpixel area proportions. This paper examines the correlation between posterior probability of class membership, estimated using neural network and nearest neighbour models, and area proportion. In addition, the paper describes several models, again based on neural networks and nearest neighbour algorithms, that have been developed to estimate the land cover area proportions explicitly. Both types of model were applied to a Landsat TM data set. The results demonstrated that better estimates of the true land cover area were obtained using models that predicted the area proportion directly than were obtained using models that predicted the posterior probability of class membership. Further, it was found that a linear model (single-layer neural network) and a nearest neighbour smoothing model produced higher correlation and lower errors than the other models investigated.

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More information

Published date: September 1999
Venue - Dates: Proceedings of the Ninth International Conference on Artificial Neural Networks (ICANN '99), 1999-09-01
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 251956
URI: http://eprints.soton.ac.uk/id/eprint/251956
PURE UUID: 81d0d1e1-6bf8-4d7e-a0e8-6097e4b0be82
ORCID for M.S. Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 18 Nov 1999
Last modified: 20 Jul 2019 01:28

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