Hyper spectral Image Segmentation Using the Concept of Data Stuctures

Aditya P .Bakshi, Prof. Vijaya K. Shandilya


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 capabilities, we then propose a pruning strategy in order to perform a classification. Labeling each BPT node with SVM classi.ers 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.

Keywords: Hyper Spectral Imaging, Binary Partition Tree, segmentation, classification, filtering.

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DOI: https://doi.org/10.26483/ijarcs.v3i2.1052


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