Segmentation of airborne hyperspectral images by integrating multi-level data fusion

This paper deals with the extraction of the hedgerow and copse network from hyperspectral images acquired with the Compact Airborne Spectrographic Imager (CASI). The strategy of segmentation integrates several levels of data fusion allowing a decision to be taken concerning the membership of each pixel to the hedgerow and copse network from the large set of original data. The first level leads to quantifying the membership of each pixel to specific features of the network. It includes data fusion based on physical properties, geometric context-dependent fuzzy fusion with an original consistency measure and the geometric fusion of decisions. The second level is a fuzzy fusion of methods allowing the membership of each pixel to the network to be quantified. Finally, the third level involves post-processing the data with a context-dependent fusion of decisions to obtain the final map of the hedgerow and copse network.