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Texture classification of logged forests in tropical Africa using machine-learning algorithms

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This Letter describes a procedure that incorporates textural measures in the classification of logged forests from Landsat Thematic Mapper data. The objective was to increase classification accuracy by applying recently developed algorithms in machine learning that are fast in training. Three voting classification algorithms, Arc-4x, Adaboost and bagging were also tested. Initial results using a decision tree classifier showed that adding selected textural measures increased the accuracy of logged forest classification by almost 40%, although the class accuracy for logged forests was only approximately 50% when using spectral and textural features combined. No further significant increase in the classification of logged forests was obtained by voting classification.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/0143116021000050538

Affiliations: 1: Geography Department, University of Maryland, College Park, MD20742, USA 2: Earth System Science Interdisciplinary Center and Department of Geography, University of Maryland, College Park, MD20742, USA

Publication date: March 1, 2003

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