Abstract:
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The optimal exploitation of the information provided by
hyperspectral images requires the development of advanced
image processing tools. This paper introduces a new hierarchical
structure representation for such images using binary
partition trees (BPT). Based on region merging techniques using
statistical measures, this region-based representation reduces
the number of elementary primitives and allows a more
robust filtering, segmentation, classification or information
retrieval. To demonstrate BPT capabilites, we first discuss
the construction of BPT in the specific framework of hyperspectral
data. We then propose a pruning strategy in order to
perform a classification. Labelling each BPT node with SVM
classifiers outputs, a pruning decision based on an impurity
measure is addressed. Experimental results on two different
hyperspectral data sets have demonstrated the good performances
of a BPT-based representation |