Hierarchical analysis of remote sensing data: morphological attribute profiles and binary partition trees
Document typeConference report
Rights accessRestricted access - publisher's policy
The new generation of very high resolution sensors in airborne or satellite remote sensing open the door to countless new applications with a high societal impact. In order to bridge the gap between the potential offered by these new sensors and the needs of the end-users to actually face tomorrows challenges, advanced image processing methods need to be designed. In this paper we discuss two of the most promising strategies aiming at a hierarchical description and analysis of remote sensing data, namely the Extended Attribute Profiles (EAP) and the Binary Partition Trees (BPT). The EAP computes for each pixel a vector of attributes providing a local multiscale representation of the information and hence leading to a fine description of the local structures of the image. Using different attributes allows to address different contexts or applications. The BPTs provide a complete hierarchical description of the image, from the pixels (the leaves) to larger regions as the merging process goes on. The pruning of the tree provides a partition of the image and can address various goals (segmentation, object extraction, classification). The EAP and BPT approaches are used in experiments and the obtained results demonstrate their importance.
CitationBenediktsson, J. [et al.]. Hierarchical analysis of remote sensing data: morphological attribute profiles and binary partition trees. A: International Symposium on Mathematical Morphology. "Mathematical Morphology and Its Applications to Image and Signal Processing". Verbania-Intra: Springer Verlag, 2012, p. 306-319.