Improved Recognition of Spectrally Mixed Land Cover Classes Using Spatial Textures and Voting Classifications

Authors:
Jonathan C.-W. Chan;Ruth S. DeFries;John Townshend
Affiliations:
-;-;-
Venue:
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Year:
2001

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Abstract

Regenerating forest is important to account for carbon sink. Mapping regenerating forest from satellite data is difficult because it is spectrally mixed with natural forest. This paper investigated the combined use of texture features and voting classifications to enhance recognition of these two classes. Bagging and boosting were applied on Learning Vector Quantization (LVQ) and decision tree. Our results show that spatial textures improved separability. After applying voting classifications, class accuracy of decision tree increased by 5-7% and that of LVQ by approximately 3%. Substantial reduction (between 23% to 40%) of confusions between regenerating forest and natural forest were recorded. Comparatively, bagging is more consistent than boosting. An interesting observation is that even LVQ, a stable learner, was able to benefit from both voting classification algorithms.