Extracting Floor Area Ratio of the Classified Buildings from Very High Resolution Satellite Image Using Multiple Features

YE Min; WANG Bin; WANG Siyuan; LIU Changzheng; LI Yanxia; CEN Wei

doi:10.13203/j.whugis20180241

Volume 44 Issue 11

Nov. 2019

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Geomatics and Information Science of Wuhan University > 2019 > 44(11): 1674-1684. > DOI: 10.13203/j.whugis20180241

YE Min, WANG Bin, WANG Siyuan, LIU Changzheng, LI Yanxia, CEN Wei. Extracting Floor Area Ratio of the Classified Buildings from Very High Resolution Satellite Image Using Multiple Features[J]. Geomatics and Information Science of Wuhan University, 2019, 44(11): 1674-1684. DOI: 10.13203/j.whugis20180241

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Extracting Floor Area Ratio of the Classified Buildings from Very High Resolution Satellite Image Using Multiple Features

YE Min^1,,
WANG Bin^1, ,,
WANG Siyuan^{2, 3},
LIU Changzheng⁴,
LI Yanxia¹,
CEN Wei¹

1.
School of Civil Engineering and Architecture, Beijing Jiaotong University, Beijing 100044, China
2.
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
3.
Chinese Academy of Sciences University, Beijing 100045, China
4.
School of Information Science and Technology, Shihezi University, Shihezi 832003, China

Funds:

Natural Science Foundation of China 91547107

Natural Science Foundation of China 41428103

Xinjiang Production and Construction Corps (XPCC) Scientific and Technological Project 2015AD018

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Author Bio:
YE Min, postgraduate, specializes in intersection between remote sensing information and rail transit information. E-mail:16121176@bjtu.edu.cn
Corresponding author:
WANG Bin, associate professor. E-mail: bwang@bjtu.edu.cn
Received Date: August 15, 2018
Published Date: November 04, 2019

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Abstract

Abstract

With the rapid development of urbanization, problems such as shortage of land resource and low efficiency of land use have emerge and grown increasingly serious. The extraction of floor area ratio of the classified buildings in urban areas is of great significance to the floor area ratio(FAR) management and regulatory detailed planning in urban land development. Moreover, high-resolution data which provide analysis results of high precision are under comprehensive application in various lines of work, especially in the field of territorial resources. Given these, an object-based method for FAR extraction from very high resolution satellite image using multiple features, which are principal components, main direction, border index and rectangular fit, and Bayesian classifier is proposed. Meanwhile, FAR extraction results calculated via shadow area and shadow length are compared. We applied this method to a clip of WorldView-3 image and validated the FAR results of building units included one by one. Experimental results show that the average accuracy for shadow area method is 93.90% and 85.19% for shadow length method and shadow area method is more effective than shadow length method.
- floor area ratio(FAR),
- shadow,
- building,
- features,
- accuracy analysis

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References

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Extracting Floor Area Ratio of the Classified Buildings from Very High Resolution Satellite Image Using Multiple Features

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