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

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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


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.

Bibliographic note

M1 - 19