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With rapid developments in satellite and sensor technologies, there has been a dramatic increase in the availability of high resolution\ud(HR) remotely sensed images. Hence, the ability to collect images remotely is expected to far exceed our capacity to analyse these\udimages manually. Consequently, techniques that can handle large volumes of data are urgently needed. In many of today's multiscale\udtechniques the underlying representation of objects is still pixel-based, i.e. object entities are still described/accessed via pixelbased\uddescriptors, thereby creating a bottleneck when processing large volumes of data. Also, these techniques do not yet leverage\udthe topological and contextual information present in the image. We propose a framework for Discrete Topology based hierarchical\udsegmentation, addressing both the algorithms and data structures that will be required. The framework consists of three components:\ud1) Conversion to dart-based representation, 2) Size-Constrained-Region Merging to generate multiple segmentations, and 3) Update\udof two sparse arrays SIGMA and LAMBDA which together encode the topology of each region in the hierarchy. The results of our\udrepresentation are demonstrated both on a synthetic and a real high resolution images. Application of this representation to objectdetection\udis also discussed

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

DISCRETE TOPOLOGY BASED HIERARCHICAL SEGMENTATION FOR EFFICIENT OBJECT-BASED IMAGE ANALYIS: APPLICATION TO OBJECT DETECTION IN HIGH RESOLUTION SATELLITE IMAGES