Texture-based forest cover classification using random forests and ensemble margin

This work investigates the discriminative power of wavelet decomposition based texture features in forest cover classification. Our texture features are used as inputs in a random forests classifier. The performances of this tree-based ensemble classifier are assessed by classification accuracy as w...

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Bibliographic Details
Published in2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) pp. 3072 - 3075
Main Authors Boukir, S., Regniers, O., Guo, L., Bombrun, L., Germain, C.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2015
Subjects
Accuracy
Context modeling
Electronic mail
ensemble learning
ensemble margin
forest
Remote sensing
Spatial resolution
Texture features
Vegetation
wavelet decomposition
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Summary:This work investigates the discriminative power of wavelet decomposition based texture features in forest cover classification. Our texture features are used as inputs in a random forests classifier. The performances of this tree-based ensemble classifier are assessed by classification accuracy as well as classification confidence provided by an unsupervised version of ensemble margin. The effectiveness of the proposed texture based multiple classifier system is demonstrated in performing mapping of very high resolution forest imagery. Traditional grey level co-occurrence matrix derived texture features are also evaluated through our ensemble classification framework for comparison.
ISSN:2153-6996
2153-7003
DOI:10.1109/IGARSS.2015.7326465