Object recognition in hyperspectral images using Binary Partition Tree representation

• New object detection technique by using hierarchical region-based image representations.
• Binary Partition Tree is proposed as a structured search space in order to incorporate the spectral and the spatial information.
• The strategy is applied on several datasets of hyperspectral images of urban areas.
• The obtained results show the interest of studying the objects of the scene with a region-based perspective.

In this work, an image representation based on Binary Partition Tree is proposed for object detection in hyperspectral images. This hierarchical region-based representation can be interpreted as a set of hierarchical regions stored in a tree structure, which succeeds in presenting: (i) the decomposition of the image in terms of coherent regions and (ii) the inclusion relations of the regions in the scene. Hence, the BPT representation defines a search space for constructing a robust object identification scheme. Spatial and spectral information are integrated in order to analyze hyperspectral images with a region-based perspective. For each region represented in the BPT, spatial and spectral descriptors are computed and the likelihood that they correspond to an instantiation of the object of interest is evaluated. Experimental results demonstrate the good performances of this BPT-based approach.