遥感技术与应用 2021, Vol. 36 Issue (6): 1436-1445 DOI: 10.11873/j.issn.1004-0323.2021.6.1436 |
遥感应用 |
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基于Sentinel-2和全卷积网络的北京六环内高层建筑区提取与分析 |
朱金明1,2,李利伟2(),程钢1,高连如2,张兵2,3 |
1.河南理工大学 测绘与国土信息工程学院,河南 焦作 454000 2.中国科学院空天信息创新研究院 数字地球重点实验室,北京 100094 3.中国科学院大学,北京 100094 |
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Detection and Analysis of High-rising Buildings within The sixth Ring Road of Beijing based on Sentinel-2 and Fully Convolutional Network |
Jinming Zhu1,2,Liwei Li2(),Gang Cheng1,Lianru Gao2,Bing Zhang2,3 |
1.School of Surveying and Land Information Engineering,Henan Polytechnic University,Jiaozuo 454000,China 2.The Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing 100094,China 3.University of Chinese Academy of Sciences,Beijing 100049,China |
引用本文:
朱金明,李利伟,程钢,高连如,张兵. 基于Sentinel-2和全卷积网络的北京六环内高层建筑区提取与分析[J]. 遥感技术与应用, 2021, 36(6): 1436-1445.
Jinming Zhu,Liwei Li,Gang Cheng,Lianru Gao,Bing Zhang. Detection and Analysis of High-rising Buildings within The sixth Ring Road of Beijing based on Sentinel-2 and Fully Convolutional Network. Remote Sensing Technology and Application, 2021, 36(6): 1436-1445.
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1 |
Tang Yiqun, Yan Xuexin, Wang Jianxiu, et al. Model test study of influence of high-rise building on ground subsidence[J]. Journal of Tongji University(Natural Science Edition),2007,35(3):32-37.
|
1 |
唐益群, 严学新, 王建秀, 等. 高层建筑群对地面沉降影响的模型试验研究[J]. 同济大学学报(自然科学版), 2007,35(3):32-37.
|
2 |
Stewart I D, Oke T R. Local climate zones for urban temperature studies[J]. Bulletin of the American Meteorological Society, 2012, 93(12):1879-1900. DOI: 10.1175/BAMS-D-11-000191.
doi: 10.1175/BAMS-D-11-000191
|
3 |
Ge Yaning, Xu Xinliang, Li Jing, et al. Study on the influence of urban building density on the heat island effect in Beijing[J]. Journal of Geo-information Science,2016,18(12):1698-1706.
|
3 |
葛亚宁, 徐新良, 李静, 等. 北京城市建筑密度分布对热岛效应的影响研究[J]. 地球信息科学学报, 2016, 18(12):1698-1706.
|
4 |
Wang Jianhui. The investigation of airborne pollutant dispersion around high-rise residential buildings based on natural ventilation[D]. Chongqing: Chongqing University, 2011.
|
4 |
王建辉. 自然通风条件下高层居住建筑周围空气污染物扩散研究[D]. 重庆:重庆大学, 2011.
|
5 |
Miller R B, Small C. Cities from space: potential applications of remote sensing in urban environmental research and policy[J]. Environmental Science & Policy,2003,6:129-137. DOI:10.1016/S1462-9011(03)00002-9.
doi: 10.1016/S1462-9011(03)00002-9
|
6 |
Gong P, Li X, Wang J, et al. Annual maps of Global Artificial Impervious Area (GAIA) between 1985 and 2018[J]. Remote Sensing of Environment,2020,236,111510. DOI:10. 1016/j.rse.2019.111510.
doi: 10. 1016/j.rse.2019.111510
|
7 |
Guo Haitao, Liu Jishuang, Lu Jun. A integrative detection method of the areas containing high building based on correlative coefficient[J]. Journal of Geomatics Science and Technology,2009,26(2):125-127.
|
7 |
郭海涛, 刘继双, 卢俊. 基于相关系数的高层建筑物区域综合检测[J]. 测绘科学技术学报, 2009,26(2):125-127.
|
8 |
Li J, Roy D P. A global analysis of Sentinel-2A, Sentinel-2B and Landsat 8 data revisit intervals and implications for terrestrial monitoring[J]. Remote Sensing, 2017,9:902.
|
9 |
Yan Miao, Zhao Hongdong, Li Yuhai, et al. Multi-classification and recognition of hyperspectral remote sensing objects based on convolutional neural network[J]. Laser& Optoelectronics Progress,2019,56(2):191-198.
|
9 |
闫苗, 赵红东, 李宇海, 等. 基于卷积神经网络的高光谱遥感地物多分类识别[J]. 激光与光电子学进展,2019,56(2):191-198.
|
10 |
Song Tingqiang, Li Jixu, Zhang Xinye. Building recognition in high-resolution remote sensing image based on deep learning[J]. Computer Engineering and Applications,2020,56(8):26-34.
|
10 |
宋廷强,李继旭,张信耶.基于深度学习的高分辨率遥感图像建筑物识别[J]. 计算机工程与应用,2020,56(8):26-34.
|
11 |
Dong Zhipeng, Wang Mi, Li Deren, et al. Object detection in remote sensing imagery based on convolutional neural networks with suitable scale features[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(10):1285-1295.
|
11 |
董志鹏, 王密, 李德仁, 等. 遥感影像目标的尺度特征卷积神经网络识别法[J]. 测绘学报, 2019, 48(10): 1285-1295.
|
12 |
Xu X, Li W, Ran Q, et al. Multisource remote sensing data classification based on convolutional neural network[J]. IEEE Transactions on Geoscience & Remote Sensing,2018,56:937-949. DOI: 10.1109/TGRS.2017.2756851.
doi: 10.1109/TGRS.2017.2756851
|
13 |
Huang B, Zhao B, Song Y. Urban land-use mapping using a deep convolutional neural network with high spatial resolution multi-spectral remote sensing imagery[J]. Remote Sensing of Environment,2018,214,73-86. DOI:10.1016/j.rse.2018. 04.050.
doi: 10.1016/j.rse.2018. 04.050
|
14 |
Zhang L, Zhang L, Du B. Deep learning for remote sensing data: a technical tutorial on the state of the art[J]. IEEE Geoence & Remote Sensing Magazine,2016,4(2):22-40. DOI: 10.1109/MGRS.2016.2540798.
doi: 10.1109/MGRS.2016.2540798
|
15 |
Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2017, 39(4):640-650. DOI:10.1109/CVPR.2015.7298965.
doi: 10.1109/CVPR.2015.7298965
|
16 |
Peng C, Li Y, Jiao L, et al. Densely based multi-scale and multi-modal fully convolutional networks for high-resolution remote sensing image semantic segmentation[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2019, 12(8):2612-2626. DOI: 10.1109/JSTARS.2019.2906387.
doi: 10.1109/JSTARS.2019.2906387
|
17 |
Liang Zheyu, Wu Yanlan, Yang Hui, et al. Automatic water extraction from remote sensing images based on dense connected fully convolutional network[J]. Remote Sensing Information,2020,35(4):68-77.
|
17 |
梁泽毓, 吴艳兰, 杨辉, 等. 基于密集连接全卷积神经网络的遥感影像水体全自动提取方法[J]. 遥感信息,2020,35(4):68-77.
|
18 |
Pang Bo, Huang Zuoju, Wu Yanlan, et al. Extraction and mapping of impervious surface from high resolution remote sensing images based on improved fully convolutional network[J]. Remote Sensing Information,2020,35(4):47-55.
|
18 |
庞博, 黄祚继, 吴艳兰, 等. 基于改进全卷积神经网络的高分遥感影像不透水面提取制图[J]. 遥感信息,2020,35(4):47-55.
|
19 |
Zhang Yonghong, Xia Guanghao, Kai Xi, et al. Road extraction from multi-source high resolution remote sensing image based on fully convolutional neural network [J]. Journal of Computer Applications,2018,38(7):2070-2075.
|
19 |
张永宏, 夏广浩, 阚希, 等. 基于全卷积神经网络的多源高分辨率遥感道路提取[J]. 计算机应用,2018,38(7):2070-2075.
|
20 |
An Jie, Ma Jinwen. Automatic cloud segmentation based on the fully convolutional neural networks[J]. Journal of Signal Processing,2019,35(4):556-562.
|
20 |
安捷, 马尽文. 基于全卷积网络的遥感图像自动云检测[J]. 信号处理,2019,35(4): 556-562.
|
21 |
Li L, Yan Z, Shen Q, et al. Water body extraction from veryHigh spatial resolution remote sensing data based on fully convolutional networks[J].Remote Sensing,2019,11:1162.DOI:10.3390/rs11101162.
doi: 10.3390/rs11101162
|
22 |
Yan Zhi, Li Liwei, Cheng Gang. Extracting high-rise and low-rise building areas from Sentinel-2 Image using full convolutional network[J]. Bulletin of Surveying and Mapping, 2019(7):73-77.
|
22 |
闫智, 李利伟, 程钢. 利用全卷积网络提取Sentinel-2影像高低层建筑区[J]. 测绘通报,2019(7):73-77.
|
23 |
Li L, Zhu J, Gao L, et al. Detecting and analyzing the increase of high-rising buildings to monitor the dynamic of the Xiong’an new area[J]. Sustainability,2020,12:4355. DOI: 10.3390/su12114355.
doi: 10.3390/su12114355
|
24 |
Unified Standard for Civil Building Design: [S]. Beijing: China Architecture & Building Press, 2019.
|
24 |
民用建筑设计统一标准: [S]. 北京: 中国建筑工业出版社, 2019.
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