Urban-area segmentation using visual words

Lior Weizman, Jacob Goldberger

    Research output: Contribution to journalArticlepeer-review

    50 Scopus citations


    In this letter, we address the problem of urban-area extraction by using a feature-free image representation concept known as Visual Words. This method is based on building a dictionary of small patches, some of which appear mainly in urban areas. The proposed algorithm is based on a new pixel-level variant of visual words and is based on three parts: building a visual dictionary, learning urban words from labeled images, and detecting urban regions in a new image. Using normalized patches makes the method more robust to changes in illumination during acquisition time. The improved performance of the method is demonstrated on real satellite images from three different sensors: LANDSAT, SPOT, and IKONOS. To assess the robustness of our method, the learning and testing procedures were carried out on different and independent images.

    Original languageEnglish
    Article number4787085
    Pages (from-to)388-392
    Number of pages5
    JournalIEEE Geoscience and Remote Sensing Letters
    Issue number3
    StatePublished - Jul 2009


    • Map updating
    • Object detection
    • Remote sensing
    • Segmentation
    • Urban areas
    • Visual words


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