We present a complete processing line to generate an object based description of optical remote sensing (RS) images. A segmentation algorithm is used to generate a partition of regions and simplify the volume of data. Results are still at the pixel level. Based on topology analyses, a dynamical algorithm is proposed to retrieve, extract the segmented regions and encode them in a tree structure which describes their topological relations (adjacencies, inclusions). The overall collected information constitutes a consistent and independent database, generated efficiently on standard workstation. Many applications are possible, such as content based image retrieval, image description and compression, object classification or image-object fusion. A scenario is presented to emphasize interest of the method in the case of 3D visualization enhancement: image-objects are integrated on elevation data (digital elevation models, DEM) in order to generate more realistic rendering.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Object and topology extraction from remote sensing images


    Beteiligte:
    Maire, C. (Autor:in) / Datcu, M. (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    911223 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Object and Topology Extraction from Remote Sensing Images

    Maire, C. / Datcu, M. | British Library Conference Proceedings | 2005


    A review of road extraction from remote sensing images

    Weixing Wang / Nan Yang / Yi Zhang et al. | DOAJ | 2016

    Freier Zugriff

    Multi-threshold object selection in images of remote sensing systems

    V. Yu. Volkov / M. I. Bogachev / O. A. Markelov | DOAJ | 2019

    Freier Zugriff

    Road Information Extraction from Remote Sensing Images Based on Fully Convolutional Network

    Xiao, Peng / Yang, Dongfang / Li, Yongfei et al. | IEEE | 2022


    Ship object detection in remote sensing images using convolutional neural networks

    Huang, Jie / Jiang, Zhiguo / Zhang, Haopeng et al. | British Library Online Contents | 2017