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Conference papers

Region-based classification of remote sensing images with the morphological tree of shapes

Abstract : Satellite image classification is a key task used in remote sensing for the automatic interpretation of a large amount of information. Today there exist many types of classification algorithms using advanced image processing methods enhancing the classification accuracy rate. One of the best state-of-the-art methods which improves significantly the classification of complex scenes relies on Self-Dual Attribute Profiles (SDAPs). In this approach, the underlying representation of an image is the Tree of Shapes, which encodes the inclusion of connected components of the image. The SDAP computes for each pixel a vector of attributes providing a local multi-scale representation of the information and hence leading to a fine description of the local structures of the image. Instead of performing a pixel-wise classification on features extracted from the Tree of Shapes, it is proposed to directly classify its nodes. Extending a specific interactive segmentation algorithm enables it to deal with the multi-class classification problem. The method does not involve any statistical learning and it is based entirely on morphological information related to the tree. Consequently, a very simple and effective region-based classifier relying on basic attributes is presented.
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Submitted on : Friday, February 24, 2017 - 4:32:08 PM
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Gabriele Cavallaro, Mauro Dalla Mura, Edwin Carlinet, Thierry Géraud, Nicola Falco, et al.. Region-based classification of remote sensing images with the morphological tree of shapes. IGARSS 2016 - IEEE International Geoscience and Remote Sensing Symposium, Jul 2016, Beijing, China. pp.5087 - 5090, ⟨10.1109/IGARSS.2016.7730326⟩. ⟨hal-01476244⟩



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