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Open AccessArticle

A Multi-Scale Filtering Building Index for Building Extraction in Very High-Resolution Satellite Imagery

by Qi Bi 1, Kun Qin 1,*, Han Zhang 1, Ye Zhang 1, Zhili Li 2 and Kai Xu 2
1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2
Faculty of Information Engineering, China University of Geosciences, Wuhan 430074, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(5), 482; https://doi.org/10.3390/rs11050482
Received: 12 January 2019 / Revised: 19 February 2019 / Accepted: 21 February 2019 / Published: 26 February 2019
(This article belongs to the Special Issue Advanced Topics in Remote Sensing)
Building extraction plays a significant role in many high-resolution remote sensing image applications. Many current building extraction methods need training samples while it is common knowledge that different samples often lead to different generalization ability. Morphological building index (MBI), representing morphological features of building regions in an index form, can effectively extract building regions especially in Chinese urban regions without any training samples and has drawn much attention. However, some problems like the heavy computation cost of multi-scale and multi-direction morphological operations still exist. In this paper, a multi-scale filtering building index (MFBI) is proposed in the hope of overcoming these drawbacks and dealing with the increasing noise in very high-resolution remote sensing image. The profile of multi-scale average filtering is averaged and normalized to generate this index. Moreover, to fully utilize the relatively little spectral information in very high-resolution remote sensing image, two scenarios to generate the multi-channel multi-scale filtering index (MMFBI) are proposed. While no high-resolution remote sensing image building extraction dataset is open to the public now and the current very high-resolution remote sensing image building extraction datasets usually contain samples from the Northern American or European regions, we offer a very high-resolution remote sensing image building extraction datasets in which the samples contain multiple building styles from multiple Chinese regions. The proposed MFBI and MMFBI outperform MBI and the currently used object based segmentation method on the dataset, with a high recall and F-score. Meanwhile, the computation time of MFBI and MBI is compared on three large-scale very high-resolution satellite image and the sensitivity analysis demonstrates the robustness of the proposed method. View Full-Text
Keywords: building extraction; multi-scale filtering index; remote sensing dataset; very high-resolution remote sensing image building extraction; multi-scale filtering index; remote sensing dataset; very high-resolution remote sensing image
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MDPI and ACS Style

Bi, Q.; Qin, K.; Zhang, H.; Zhang, Y.; Li, Z.; Xu, K. A Multi-Scale Filtering Building Index for Building Extraction in Very High-Resolution Satellite Imagery. Remote Sens. 2019, 11, 482.

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