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Remote Sensing for Natural Resources    2023, Vol. 35 Issue (3) : 97-106     DOI: 10.6046/zrzyyg.2022226
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A method for identifying the number of building floors based on shadow information
LI Zhixin1(), WANG Mengfei2(), JIA Weijie2, JI Song1, WANG Yufei3
1. Institute of Geospatial Information,Information Engineering University, Zhengzhou 450001, China
2. China Aero Geophysical Survey and Remote Sensing Center for Natural Resources, Beijing 100083,China
3. Journal of Management World, Beijing 100048,China
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Abstract  

Acquiring the number of building floors can provide data support and decision-making services for urban safety and disaster hazards. The number is primarily acquired through manual investigation and statistics currently. Furthermore, the automatic inversion of building heights based on remote sensing images suffers from low algorithmic efficiency, incomplete extraction, and a low automation degree. To acquire the number of building floors quickly and extensively, this study designed an identification algorithm based on GF-7 satellite images. First, shadow lines were automatically extracted using the fishing net method based on preprocessing such as principal component analysis. Then, the building height was calculated based on the geometric relationship formed by the shadow, and the building height was then converted into the number of building floors. Finally, the error in the extraction results was corrected through support vector machine regression, aiming to eliminate the influence of the measurement error of the shadow length. With Chaoyang District in Beijing as the study area, this study conducted model training and testing of the identification algorithm. As shown by the experimental results with Zhengzhou City in Henan Province as the verification area, the overall identification accuracy was 90.21%, with an identification error of three floors at most for buildings with 6~50 floors. This study provides novel technical support and application service for automatically acquiring the number of building floors rapidly and extensively based on satellite data.

Keywords detection of building shadow      feature optimization      automatic extraction of shadow lines based on the fishing net method      support vector machine regression      identification of the number of building floors     
ZTFLH:  TP79  
Issue Date: 19 September 2023
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Articles by authors
Zhixin LI
Mengfei WANG
Weijie JIA
Song JI
Yufei WANG
Cite this article:   
Zhixin LI,Mengfei WANG,Weijie JIA, et al. A method for identifying the number of building floors based on shadow information[J]. Remote Sensing for Natural Resources, 2023, 35(3): 97-106.
URL:  
https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022226     OR     https://www.gtzyyg.com/EN/Y2023/V35/I3/97
Fig.1  Building floor extraction process
Fig.2  Shadow extraction and optimization process
Fig.3  Shadow length calculation process
Fig.4  A geometric diagram of the sun, satellites and buildings
Fig.5  Study area sample
Fig.6  Baidu panoramic azimuth map
数据 地区 时间 范围 样本数/个
训练集
测试集
验证集
北京市朝阳区
北京市朝阳区
河南省郑州市
2020-11-16 11:20:58
2020-11-16 11:20:58
2021-09-20 11:31:28
N39°49'~40°05',E116°21'~116°38'
N39°49'~40°05',E116°21'~116°38'
N34°30'~34°51',E113°38'~113°56'
700
300
300
Tab.1  Sample data
楼层范围 原始影像 阴影提取结果 阴影线提取结果 楼层提取结果
6~10层
11~20层
21~30层
30层以上
Tab.2  Examples of floor extraction results in study area
提取结果 建筑物编号
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
初提取层数
改正后层数
实际层数
改正前误差
改正后误差
5
6
6
1
0
11
14
11
0
3
9
12
12
3
0
12
15
13
1
2
10
13
14
4
1
15
19
18
3
1
15
19
20
5
1
13
17
21
8
4
15
19
21
6
2
17
22
21
4
1
16
20
22
6
2
19
24
24
5
0
19
24
25
6
1
20
25
27
7
2
20
25
28
8
3
21
27
28
7
1
23
28
28
5
0
24
31
28
4
3
24
31
30
6
1
26
33
33
7
0
改正前平均误差楼层数: 4.16; P=78.54%; σ=1.263 改正后平均误差楼层数: 1.39; P=90.21%; σ=0.972
Tab.3  Building floor test results
Fig.7  Histogram of experimental results
Fig.8  Building floor prediction results
建筑物楼层范围/层 平均提取误差/层 总体精度/% 标准差/层
6~10
11~20
21~30
>30
1.33
1.07
1.63
1.27
84.61
93.40
97.36
98.84
0.471 4
1.032 6
1.298 4
1.052 1
平均值 1.32 93.55 0.963 6
Tab.4  Building level verification results
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