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Texture classification of Mediterranean land cover

Record type: Article

Maximum likelihood (ML) and artificial neural network (ANN) classifiers were applied to three Landsat Thematic Mapper (TM) image sub-scenes (termed urban, agricultural and semi-natural) of Cukurova, Turkey. Inputs to the classifications comprised (i) spectral data and (ii) spectral data in combination with texture measures derived on a per-pixel basis. The texture measures used were: the standard deviation and variance and statistics derived from the co-occurrence matrix and the variogram. The addition of texture measures increased classification accuracy for the urban sub-scene but decreased classification accuracy for agricultural and semi-natural sub-scenes. Classification accuracy was dependent on the nature of the spatial variation in the image sub-scene and, in particular, the relation between the frequency of spatial variation and the spatial resolution of the imagery. For Mediterranean land, texture classification applied to Landsat TM imagery may be appropriate for the classification of urban areas only.

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Citation

Berberoglu, S., Lloyd, C.D., Atkinson, P.M. and Curran, P.J. (2007) Texture classification of Mediterranean land cover International Journal of Applied Earth Observation and Geoinformation, 9, pp. 322-334. (doi:10.1016/j.jag.2006.11.004).

More information

Published date: 2007
Keywords: Classification, Landsat TM, Texture, Artificial neural networks

Identifiers

Local EPrints ID: 54982
URI: http://eprints.soton.ac.uk/id/eprint/54982
ISSN: 0303-2434
PURE UUID: 4bcc957f-ce3b-441d-9741-31147b210ec1

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Date deposited: 01 Aug 2008
Last modified: 17 Jul 2017 14:34

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Contributors

Author: S. Berberoglu
Author: C.D. Lloyd
Author: P.M. Atkinson
Author: P.J. Curran

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