Comparison of texture-based and fuzzy classification approaches for regenerating tropical forest mapping using LANDSAT TM

Gintautas Palubinskas

doi:10.1117/12.182862

17 August 1994 Comparison of texture-based and fuzzy classification approaches for regenerating tropical forest mapping using LANDSAT TM

Gintautas Palubinskas

Author Affiliations +

Proceedings Volume 2357, ISPRS Commission III Symposium: Spatial Information from Digital Photogrammetry and Computer Vision; (1994) https://doi.org/10.1117/12.182862
Event: Spatial Information from Digital Photogrammetry and Computer Vision: ISPRS Commission III Symposium, 1994, Munich, Germany

Abstract

The two classification approaches based on texture and fuzzy sets were investigated for tropical forest regrowth mapping on Landsat TM (Manaus area, Brazil). Texture-based classifiers (based on Markov random field model consistently provided a higher classification accuracies (for testing set), indicating that they are more able to accurately characterize different tropical forest regeneration classes and two species of trees (cecropia and vismia). Memberships derived from the three classification algorithms: based on the probability density function, a posteriori probability, and the Mahalanobis distance were used for post- classification of fuzzy image. Post-classification (summation of memberships in the neighborhood or application of homogeneity approach for post-classification) of the fuzzy image can increase the classification accuracies (for training and testing data) by 10% in comparison with maximum likelihood classification for 11 classes of tropical forest region. Texture-based classification and post-classification of fuzzy image give the comparable classification accuracies for the same 11 classes of tropical forest region.

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Gintautas Palubinskas "Comparison of texture-based and fuzzy classification approaches for regenerating tropical forest mapping using LANDSAT TM", Proc. SPIE 2357, ISPRS Commission III Symposium: Spatial Information from Digital Photogrammetry and Computer Vision, (17 August 1994); https://doi.org/10.1117/12.182862

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