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Remote Sens. 2016, 8(2), 155; doi:10.3390/rs8020155

Representation of Block-Based Image Features in a Multi-Scale Framework for Built-Up Area Detection

1
College of Information Engineering, Shenzhen University, Shenzhen 518060, China
2
Key Laboratory for Geo-Environmental Monitoring of Coastal Zone of the National Administration of Surveying, Mapping and Geoinformation & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen 518060, China
3
National Key Laboratory of Science and Technology on Multi-spectral Information Processing, School of Automation, Huazhong University of Science and Technology, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Academic Editors: Janet Nichol, Guoqing Zhou, Norman Kerle and Prasad S. Thenkabail
Received: 24 August 2015 / Revised: 17 January 2016 / Accepted: 2 February 2016 / Published: 18 February 2016
View Full-Text   |   Download PDF [6765 KB, uploaded 18 February 2016]   |  

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. View Full-Text
Keywords: block-based feature extraction; multi-scale feature representation; scale-space theory; built-up area detection; Harris corner point; high spatial resolution image block-based feature extraction; multi-scale feature representation; scale-space theory; built-up area detection; Harris corner point; high spatial resolution image
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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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.

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