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Mapping sub-pixel variation in land cover in the UK from AVHRR imagery

Mapping sub-pixel variation in land cover in the UK from AVHRR imagery
Mapping sub-pixel variation in land cover in the UK from AVHRR imagery
A problem with NOAA AVHRR imagery is that the intrinsic scale of spatial variation in land cover in the U.K. is usually finer than the scale of sampling imposed by the image pixels. The result is that most NOAA AVHRR pixels contain a mixture of land cover types (sub-pixel mixing). Three techniques for mapping the sub-pixel proportions of land cover classes in the New Forest, U.K. were compared: (i) artificial neural networks (ANN); (ii) mixture modelling; and (iii) fuzzy c -means classification. NOAA AVHRR imagery and SPOT HRV imagery, both for 28 June 1994, were obtained. The SPOT HRV images were classified using the maximum likelihood method, and used to derive the 'known' sub-pixel proportions of each land cover class for each NOAA AVHRR pixel. These data were then used to evaluate the predictions made (using the three techniques and the NOAA AVHRR imagery) in terms of the amount of information provided, the accuracy with which that information is provided, and the ease of implementation. The ANN was the most accurate technique, but its successful implementation depended on accurate co-registration and the availability of a training data set. Supervised fuzzy c -means classification was slightly more accurate than mixture modelling.
0143-1161
917-935
Atkinson, P.
19494d04-ff44-4dd6-bc6a-11020f8dd35c
Cutler, M.
42e8cc7a-f9a0-4a02-9fa8-f435ca0a3f0d
Lewis, H.G.
e9048cd8-c188-49cb-8e2a-45f6b316336a
Atkinson, P.
19494d04-ff44-4dd6-bc6a-11020f8dd35c
Cutler, M.
42e8cc7a-f9a0-4a02-9fa8-f435ca0a3f0d
Lewis, H.G.
e9048cd8-c188-49cb-8e2a-45f6b316336a

Atkinson, P., Cutler, M. and Lewis, H.G. (1997) Mapping sub-pixel variation in land cover in the UK from AVHRR imagery. International Journal of Remote Sensing, 18 (4), 917-935. (doi:10.1080/014311697218836).

Record type: Article

Abstract

A problem with NOAA AVHRR imagery is that the intrinsic scale of spatial variation in land cover in the U.K. is usually finer than the scale of sampling imposed by the image pixels. The result is that most NOAA AVHRR pixels contain a mixture of land cover types (sub-pixel mixing). Three techniques for mapping the sub-pixel proportions of land cover classes in the New Forest, U.K. were compared: (i) artificial neural networks (ANN); (ii) mixture modelling; and (iii) fuzzy c -means classification. NOAA AVHRR imagery and SPOT HRV imagery, both for 28 June 1994, were obtained. The SPOT HRV images were classified using the maximum likelihood method, and used to derive the 'known' sub-pixel proportions of each land cover class for each NOAA AVHRR pixel. These data were then used to evaluate the predictions made (using the three techniques and the NOAA AVHRR imagery) in terms of the amount of information provided, the accuracy with which that information is provided, and the ease of implementation. The ANN was the most accurate technique, but its successful implementation depended on accurate co-registration and the availability of a training data set. Supervised fuzzy c -means classification was slightly more accurate than mixture modelling.

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More information

Published date: 1997

Identifiers

Local EPrints ID: 23619
URI: http://eprints.soton.ac.uk/id/eprint/23619
ISSN: 0143-1161
PURE UUID: d0517c01-3472-474f-86b4-fed96c737bbd
ORCID for H.G. Lewis: ORCID iD orcid.org/0000-0002-3946-8757

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Date deposited: 31 Jan 2007
Last modified: 16 Mar 2024 02:55

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Contributors

Author: P. Atkinson
Author: M. Cutler
Author: H.G. Lewis ORCID iD

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