We present an approach for classification of remotely sensed imagery using spatial information extracted from multi-resolution approximations. The wavelet transform is used to obtain multiple representations of an image at different resolutions to capture different details inherently found in different structures. Then, pixels at each resolution are grouped into contiguous regions using clustering and mathematical morphology-based segmentation algorithms. The resulting regions are modeled using the statistical summaries of their spectral, textural and shape properties. These models are used to cluster the regions, and the cluster memberships assigned to each region in multiple resolution levels are used to classify the corresponding pixels into land cover/land use categories. Final classification is done using decision tree classifiers. Experiments with two ground truth data sets show the effectiveness of the proposed approach over traditional techniques that do not make strong use of region-based spatial information.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Multi-resolution segmentation and shape analysis for remote sensing image classification


    Beteiligte:
    Aksoy, S. (Autor:in) / Akcay, H.G. (Autor:in)


    Erscheinungsdatum :

    01.01.2005


    Format / Umfang :

    1259914 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Multi-resolution Image Fusion in Remote Sensing

    Joshi, Manjunath V. / Upla, Kishor P. | TIBKAT | 2018





    Applications of High-Resolution Remote Sensing Image Data

    Shahrokhi, F. / Tarabzouni, N. / Jasentuliyana, N. | AIAA | 1990