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Open AccessArticle

Automated Detection of Buildings from Heterogeneous VHR Satellite Images for Rapid Response to Natural Disasters

by Shaodan Li 1,2, Hong Tang 1,2,*, Xin Huang 3, Ting Mao 1,2 and Xiaonan Niu 1,2
1
Key Laboratory of Environment Change and Natural Disaster, Ministry of Education, Beijing Normal University, Beijing 100875, China
2
State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
3
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2017, 9(11), 1177; https://doi.org/10.3390/rs9111177
Received: 22 August 2017 / Revised: 3 November 2017 / Accepted: 14 November 2017 / Published: 17 November 2017
(This article belongs to the Section Remote Sensing Image Processing)
In this paper, we present a novel approach for automatically detecting buildings from multiple heterogeneous and uncalibrated very high-resolution (VHR) satellite images for a rapid response to natural disasters. In the proposed method, a simple and efficient visual attention method is first used to extract built-up area candidates (BACs) from each multispectral (MS) satellite image. After this, morphological building indices (MBIs) are extracted from all the masked panchromatic (PAN) and MS images with BACs to characterize the structural features of buildings. Finally, buildings are automatically detected in a hierarchical probabilistic model by fusing the MBI and masked PAN images. The experimental results show that the proposed method is comparable to supervised classification methods in terms of recall, precision and F-value. View Full-Text
Keywords: Chinese restaurant franchise; morphological building index; building rooftop Chinese restaurant franchise; morphological building index; building rooftop
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Li, S.; Tang, H.; Huang, X.; Mao, T.; Niu, X. Automated Detection of Buildings from Heterogeneous VHR Satellite Images for Rapid Response to Natural Disasters. Remote Sens. 2017, 9, 1177.

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