Extraction of complex patterns from multiresolution remote sensing images: A hierarchical top-down methodology
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2012
- handle: 10670/1.vhutgu
- hal: hal-01694409
- doi: 10.1016/j.patcog.2011.07.017
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info:eu-repo/semantics/altIdentifier/doi/10.1016/j.patcog.2011.07.017
info:eu-repo/semantics/OpenAccess
Mots-clés
hierarchical segmentation multiresolution approaches binary partition trees urban analysis remote sensing multisource images clustering interactive/automated segmentationSujets proches
Dendrology Natural science Sciences Science of science Remote terrain sensing Remote sensing systems Remote-sensing imagery Sensing, Remote Terrain sensing, Remote Images, OpticalCiter ce document
Camille Kurtz et al., « Extraction of complex patterns from multiresolution remote sensing images: A hierarchical top-down methodology », HAL-SHS : géographie, ID : 10.1016/j.patcog.2011.07.017
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Résumé
The extraction of urban patterns from very high spatial resolution (VHSR) optical images presents several challenges related to the size, the accuracy and the complexity of the considered data. Based on the availability of several optical images of a same scene at various resolutions (medium, high, and very high spatial resolution), a hierarchical approach is proposed to progressively extract segments of interest from the lowest to the highest resolution data, and then finally determine urban patterns from VHSR images. This approach, inspired by the principle of photo-interpretation, has for purpose to use as much as possible the user’s skills while minimising his/her interaction. In order to do so, at each resolution, an interactive segmentation of one sample region is required for each semantic class of the image. Then, the user’s behaviour is automatically reproduced in the remainder of the image. This process is mainly based on tree-cuts in binary partition trees. Since it strongly relies on user-defined segmentation examples, it can involve only low level —spatial and radiometric— criteria, then enabling fast computation of comprehensive results. Experiments performed on urban images datasets provide satisfactory results which may be further used for classification purpose.