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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. © 2006 IEEE.

Original publication

DOI

10.1109/LGRS.2009.2014400

Type

Journal article

Journal

IEEE Geoscience and Remote Sensing Letters

Publication Date

01/07/2009

Volume

6

Pages

388 - 392