Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection
AbstractThe 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. View Full-Text
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Hu, Z.; Li, Q.; Zhang, Q.; Wu, G. Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection. Remote Sens. 2016, 8, 155.
Hu Z, Li Q, Zhang Q, Wu G. Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection. Remote Sensing. 2016; 8(2):155.Chicago/Turabian Style
Hu, Zhongwen; Li, Qingquan; Zhang, Qian; Wu, Guofeng. 2016. "Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection." Remote Sens. 8, no. 2: 155.
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