Supervised multichannel image classification algorithm using hierarchical histogram representation

Multichannel image processing, in particular, supervised classification, requires designing novel time effective algorithms, because in the most of the cases slightly dimension increase leads to significant processing time growth. In this paper we describe our supervised multichannel image classific...

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Bibliographic Details
Main Authors: Denisova, A.Y., Sergeyev, V.V.
Format: Conference Proceeding
Language:English
Subjects:
decision trees
hierarchical histogram
hyperspectral remote sensing image
multichannel images
supervised classification
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Summary:Multichannel image processing, in particular, supervised classification, requires designing novel time effective algorithms, because in the most of the cases slightly dimension increase leads to significant processing time growth. In this paper we describe our supervised multichannel image classification algorithm based on a hierarchical representation of multivariate histograms. The algorithm estimates the joint sample set distribution, the particular distributions of each class and the decision rule by means of specific data structure called histogram-tree. Proposed algorithm provides faster learning and classification of multidimensional input data. The experimental evaluation of the algorithm has been conducted for the hyperspectral remote sensing images. The results demonstrate that proposed algorithm is faster than the commonly used C4.5 classifier.
ISSN:1877-7058
1877-7058
DOI:10.1016/j.proeng.2017.09.616