Structure Representation for Hyper spectral Images Using Binary Classification

Binary Partition Trees are hierarchical region-based representations of images. They define a reduced set of regions that covers the image support and that spans various levels of resolution. They are attractive for object detection as they tremendously reduce the search space. In this paper, several issues related to the use of BPT for object detection are studied. Concerning the tree construction, we analyze the compromise between computational complexity reduction and accuracy. This will lead us to define two parts in the BPT: one providing accuracy and one representing the Search space for the object detection task. 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. 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 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. Labeling 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 BPTbased representation