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A comparison of texture measures for the per-field classification of Mediterranean land cover

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<mark>Journal publication date</mark>2004
<mark>Journal</mark>International Journal of Remote Sensing
Issue number19
Volume25
Number of pages23
Pages (from-to)3943-3965
Publication StatusPublished
<mark>Original language</mark>English

Abstract

Land cover of a Mediterranean region was classified within an artificial neural network (ANN) on a per-field basis using Landsat Thematic Mapper (TM) imagery. In addition to spectral information, the classifier used geostatistical structure functions and texture measures extracted from the co-occurrence matrix. Geostatistical measures of texture resulted in a more accurate classification of Mediterranean land cover than statistics derived from the co-occurrence matrix. The primary advantage of geostatistical measures was their robustness over a wide range of land cover types, field sizes and forms of class mixing. Spectral information and the variogram (geostatistical texture measure) resulted in the highest overall classification accuracies.

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