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• W2275332693 abstract "The accurate extraction and mapping of built-up areas play an important role in many social, economic, and environmental studies. In this paper, we propose a novel approach for built-up area detection from high spatial resolution remote sensing images, using a block-based multi-scale feature representation framework. First, an image is divided into small blocks, in which the spectral, textural, and structural features are extracted and represented using a multi-scale framework; a set of refined Harris corner points is then used to select blocks as training samples; finally, a built-up index image is obtained by minimizing the normalized spectral, textural, and structural distances to the training samples, and a built-up area map is obtained by thresholding the index image. Experiments confirm that the proposed approach is effective for high-resolution optical and synthetic aperture radar images, with different scenes and different spatial resolutions." @default.
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• W2275332693 date "2016-02-18" @default.
• W2275332693 modified "2023-09-24" @default.
• W2275332693 title "Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection" @default.
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• W2275332693 doi "https://doi.org/10.3390/rs8020155" @default.
• W2275332693 hasPublicationYear "2016" @default.
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