收稿：2020-06-29，
纸质出版：2022-09-07
移动端阅览
及时准确地获取城中村的空间分布及其环境质量信息对于优化城市空间、改善人居环境具有重要意义。本文以广州市越秀区为例，提出了耦合GF-2高分遥感影像和百度街景影像的城中村识别方法。首先，从街景影像中提取越秀区的街道空间品质特征；其次，在对高分遥感影像预处理并进行多尺度分割的基础上计算光谱、形状、纹理、场景特征和建筑结构5类共计23个特征；最后，融合两种影像的特征用于构建随机森林分类器进行城中村识别。结果表明，基于高分影像和基于街景影像的城中村识别整体精度分别为94.5%和85.7%，Kappa系数分别为0.58和0.31，而两者融合后的分类精度和Kappa系数为96.1%和0.67；其中基于街景影像获取的度量街道空间品质的5个指标贡献了31.6%的特征重要性。鸟瞰视野高分影像和人本视角街景影像提供的信息综合互补，构建了更有区分度的特征空间，减少了城中村的错分现象。本文证实了高分影像和街景影像在特征尺度的融合提升了城中村识别精度。街景影像中的信息可以融入到高分遥感影像等数据源中，辅助进行城中村等非正规居住空间的识别。
China has been experiencing rapid urbanization at an unprecedented rate with the significantly changing urban internal spatial structure. As an inevitable byproduct
Urban Villages (UVs)
which refer to informal living spaces with substandard living conditions
have emerged in many newly and quickly industrialized regions and cities. Although UVs provide plenty of living spaces for floating populations
their poor living environment has a negative impact on the urban landscape and public health. Thus
obtaining the spatial distribution and environmental quality information of UVs in a timely manner and accurately for optimizing urban spaces and improving human settlements has practical significance. High-resolution Remote Sensing Images (RSIs) and Street View Images (SVIs) have been employed to quickly extract UV information. However
the combination of RSIs and SVIs for retrieving UV information has received little attention. In this study
we took Yuexiu District in Guangzhou City as the study area and then propose a UV identification method based on the GF-2 high-resolution RSIs and SVIs released by Baidu Company. First
street space quality information was derived from the SVIs using support vector machine and random forest. Then
on the basis of the pre-extraction results on the GF-2 images
multi-scale segmentation was performed based on object-based image analysis
including the building instance and block levels. Twenty-three features were obtained
including the spectrum
shape
texture
building structure
and scene from the RSIs
and five indicators were obtained to measure the street space quality on the basis of the SVIs. Finally
the random forest algorithm was applied to combine the features of the two kinds of images to identify the UVs. Experimental results demonstrate that the UV recognition based on RSIs has an overall accuracy of 94.5% and a Kappa coefficient of 0.58
and the overall accuracy and Kappa coefficient of the UV identification based on SVIs are 85.7% and 0.31
respectively. An overall accuracy of 96.1% and a Kappa coefficient of 0.67 were achieved by the fusion model of the two kinds of images
exhibiting the best performance in UV recognition. Street space quality
textural
structural
and shape features play an import role in the UV recognition based on the fusion model of RSIs and SVIs. The five indicators that measure street space quality on the basis of SVIs contributed 31.6% in feature importance to the fusion model. The information provided by RSIs from the bird view and the SVIs from the human perspective could complement each other
creating an outstanding feature space and reducing the misclassification phenomenon of UVs. The key to this method is integrating the information provided by SVIs into the UV extraction process based on high-resolution RSIs to obtain a highly stable and reliable UV classification result in Yuexiu District. Multi-source data fusion is an important method in improving the ability of RSIs
and other data should be collected to enrich the existing coupling methods and the technical system. This paper reveals that the fusion of high-resolution RSIs and SVIs in the feature level could improve the recognition accuracy of UVs
and the extracted UV distribution data can be used in urban planning and other studies related to urban development. The information in SVIs could be integrated into high-resolution RSIs and other data sources to assist in identifying informal living spaces
such as UVs. Therefore
retrieving highly accurate UV information is feasible through the combination of RSIs and SVIs.
Anguelov D , Dulong C , Filip D , Frueh C , Lafon S , Lyon R , Ogale A , Vincent L and Weaver J . 2010 . Google street view: capturing the world at street level . Computer , 43 ( 6 ): 32 - 38 [ DOI: 10.1109/MC.2010.170 http://dx.doi.org/10.1109/MC.2010.170 ]
Barbierato E , Bernetti I , Capecchi I and Saragosa C . 2020 . Integrating remote sensing and street view images to quantify urban forest ecosystem services . Remote Sensing , 12 ( 2 ): 329 [ DOI: 10.3390/rs12020329 http://dx.doi.org/10.3390/rs12020329 ]
Breiman L . 2001 , Random forests . Machine learning , 2001, 45 ( 1 ): 5 - 32 [ DOI: 10.1023/A:1010933404324 http://dx.doi.org/10.1023/A:1010933404324 ]
Blasch E P and Huang S H . 2000 . Multilevel feature-based fuzzy fusion for target recognition . Proceedings of SPIE -The International Society for Optical Engineering , 4051 : 279 - 288 [ DOI: 10.1117/12.381640 http://dx.doi.org/10.1117/12.381640 ]
Cao R , Tu W , Yang C X , Li Q , Liu J , Zhu J S , Zhang Q , Li Q Q and Qiu G P . 2020 . Deep learning-based remote and social sensing data fusion for urban region function recognition . ISPRS Journal of Photogrammetry and Remote Sensing , 163 : 82 - 97 [ DOI: 10.1016/j.isprsjprs.2020.02.014 http://dx.doi.org/10.1016/j.isprsjprs.2020.02.014 ]
Cui C , Ren H Y , Zhao L and Zhuang D F . 2020 . Street space quality evaluation in Yuexiu District of Guangzhou City based on multi-feature fusion of street view imagery . Journal of Geo-Information Science , 22 ( 6 ): 1330 - 1338
崔成 , 任红艳 , 赵璐 , 庄大方 . 2020 . 基于街景影像多特征融合的广州市越秀区街道空间品质评估 . 地球信息科学学报 , 22 ( 6 ): 1330 - 1338 [ DOI: 10.12082/dqxxkx.2020.200072 http://dx.doi.org/10.12082/dqxxkx.2020.200072 ]
D’Oleire-Oltmanns S , Coenradie B and Kleinschmit B . 2011 . An object-based classification approach for mapping Migrant housing in the mega-urban area of the Pearl River Delta (China) . Remote Sensing , 3 ( 8 ): 1710 - 1723 [ DOI: 10.3390/rs3081710 http://dx.doi.org/10.3390/rs3081710 ]
Gan X Y , She T W and Long Y . 2018 . Understanding urban informality in street built environment combining manual evaluation with machine learning in processing the Beijing old city’s street-view images . Time Architecture , ( 1 ): 62 - 68
甘欣悦 , 佘天唯 , 龙瀛 . 2018 . 街道建成环境中的城市非正规性——基于北京老城街景图片的人工打分与机器学习相结合的识别探索 . 时代建筑 , ( 1 ): 62 - 68 [ DOI: 10.13717/j.cnki.ta.2018.01.012 http://dx.doi.org/10.13717/j.cnki.ta.2018.01.012 ]
Ghasempour A . 2015 . Informal settlement; concept, challenges and intervention approaches . Specialty Journal of Architecture and Construction , 1 ( 3 ): 10 - 16
Guo H X , Huang Y and Zhao D Q . 2013 . Modeling of spatial distribution of urban population density: a case study of Tianhe district, Guangzhou . Tropical Geography , 33 ( 1 ): 81 - 87 .
郭洪旭 , 黄莹 , 赵黛青 . 2013 . 城市居住人口空间分布的模拟研究——以广州市天河区为例 . 热带地理 . 33 ( 1 ): 81 - 87 [ DOI: 10.13284/j.cnki.rddl.002307 http://dx.doi.org/10.13284/j.cnki.rddl.002307 ]
Hao P , Geertman S , Hooimeijer P and Sliuzas R . 2013 . Spatial analyses of the urban village development process in Shenzhen, China . International Journal of Urban and Regional Research , 37 ( 6 ): 2177 - 2197 [ DOI: 10.1111/j.1468-2427.2012.01109.x http://dx.doi.org/10.1111/j.1468-2427.2012.01109.x ]
He L , Páez A and Liu D S . 2017 . Built environment and violent crime: an environmental audit approach using Google Street View . Computers, Environment and Urban Systems , 66 : 83 - 95 [ DOI: 10.1016/j.compenvurbsys.2017.08.001 http://dx.doi.org/10.1016/j.compenvurbsys.2017.08.001 ]
Hoffmann E J , Wang Y Y , Werner M , Kang J and Zhu X X . 2019 . Model fusion for building type classification from aerial and street view images . Remote Sensing , 11 ( 11 ): 1259 [ DOI: 10.3390/rs11111259 http://dx.doi.org/10.3390/rs11111259 ]
Hofmann P , Strobl J , Blaschke T and Kux H . 2008 . Detecting informal settlements from Quick Bird data in Rio de Janeiro using an object-based approach. Object-Based image analysis . Berlin, Heidelberg : Springer : 531 - 533 [ DOI: 10.1007/978-3-540-77058-9_29 http://dx.doi.org/10.1007/978-3-540-77058-9_29 ]
Hu T Y , Yang J , Li X C and Gong P . 2016 . Mapping urban land use by using Landsat Images and open social data . Remote Sensing , 8 ( 2 ): 151 [ DOI: 10.3390/rs8020151 http://dx.doi.org/10.3390/rs8020151 ]
Huang X , Liu H and Zhang L P . 2015 . Spatiotemporal detection and analysis of urban villages in mega city regions of China using high-resolution remotely sensed imagery . IEEE Transactions on Geoscience and Remote Sensing , 53 ( 7 ): 3639 - 3657 [ DOI: 10.1109/TGRS.2014.2380779 http://dx.doi.org/10.1109/TGRS.2014.2380779 ]
Jia Y X , Ge Y , Ling F , Guo X , Wang J H , Wang L , Chen Y H and Li X D . 2018 . Urban land use mapping by combining remote sensing imagery and mobile phone positioning data . Remote Sensing , 10 ( 3 ): 446 [ DOI: 10.3390/rs10030446 http://dx.doi.org/10.3390/rs10030446 ]
Jing R , Gong Z N , Zhu W D , Guan H L , Zhao W J and Zhang T . 2020 . Extraction of buildings from remote sensing imagery based on multi-scale SLIC-GMRF and FCNSVM . Journal of Remote Sensing , 24 ( 1 ): 11 - 26
井然 , 宫兆宁 , 朱文定 , 关鸿亮 , 赵文吉 , 张涛 . 2020 . 多尺度SLIC-GMRF与FCNSVM联合的高分影像建筑物提取 . 遥感学报 , 24 ( 1 ): 11 - 26 [ DOI: 10.11834/jrs.20208221 http://dx.doi.org/10.11834/jrs.20208221 ]
Kohli D , Warwadekar P , Kerle N , Sliuzas R and Stein A . 2013 . Transferability of object-oriented image analysis methods for slum identification . Remote Sensing , 5 ( 9 ): 4209 - 4228 [ DOI: 10.3390/rs5094209 http://dx.doi.org/10.3390/rs5094209 ]
Kuffer M , Pfeffer K and Sliuzas R . 2016 . Slums from space—15 years of slum mapping using remote sensing . Remote Sensing , 8 ( 6 ): 455 [ DOI: 10.3390/rs8060455 http://dx.doi.org/10.3390/rs8060455 ]
Li L X . 2005 . The social and economic characteristics of urban village: a case study of typical urban village in Guangzhou . Beijing Planning Review , 12 ( 3 ): 34 - 37
李立勋 . 2005 . 城中村的经济社会特征——以广州市典型城中村为例 . 北京规划建设 , 12 ( 3 ): 34 - 37 [ DOI: 10.3969/j.issn.1003-627X.2005.03.010 http://dx.doi.org/10.3969/j.issn.1003-627X.2005.03.010 ]
Li P L . 2002 . Tremendous changes: the end of villages-A study of villages in the center of Guangzhou city . Social Sciences in China , ( 1 ): 168 - 179
李培林 . 2002 . 巨变: 村落的终结——都市里的村庄研究 . 中国社会科学 , ( 1 ): 168 - 179
Li X J , Zhang C R , Li W D , Ricard R , Meng Q Y and Zhang W X . 2015 . Assessing street-level urban greenery using Google Street View and a modified green view index . Urban Forestry and Urban Greening , 14 ( 3 ): 675 - 685 [ DOI: 10.1016/j.ufug.2015.06.006 http://dx.doi.org/10.1016/j.ufug.2015.06.006 ]
Li Z G and Wu F L . 2013 . Residential satisfaction in China’s informal settlements: a case study of Beijing, Shanghai, and Guangzhou . Urban Geography , 34 ( 7 ): 923 - 949 [ DOI: 10.1080/02723638.2013.778694 http://dx.doi.org/10.1080/02723638.2013.778694 ]
Lin W S , Feng J and Li Y . 2018 . Influence of ICT on housing and employment related migration space of residents in urban villages: a case study of five urban villages in Beijing . Progress in Geography , 37 ( 2 ): 276 - 286
林文盛 , 冯健 , 李烨 . 2018 . ICT对城中村居民居住和就业迁移空间的影响——以北京5个城中村调查为例 . 地理科学进展 , 37 ( 2 ): 276 - 286 [ DOI: 10.18306/dlkxjz.2018.02.010 http://dx.doi.org/10.18306/dlkxjz.2018.02.010 ]
Liu H . 2018 . Study of Urban Village Detection Methods Based on High-Resolution Remote Sensing Imagery . Wuhan : Wuhan University
刘辉 . 2018 . 基于高分辨率遥感影像的城中村提取方法研究 . 武汉 : 武汉大学
Liu L , Silva E A , Wu C Y and Wang H . 2017a . A machine learning-based method for the large-scale evaluation of the qualities of the urban environment . Computers, Environment and Urban Systems , 65 : 113 - 125 [ DOI: 10.1016/j.compenvurbsys.2017.06.003 http://dx.doi.org/10.1016/j.compenvurbsys.2017.06.003 ]
Liu X P , He J L , Yao Y , Zhang J B , Liang H L , Wang H and Hong Y . 2017b . Classifying urban land use by integrating remote sensing and social media data . International Journal of Geographical Information Science , 31 ( 8 ): 1675 - 1696 [ DOI: 10.1080/13658816.2017.1324976 http://dx.doi.org/10.1080/13658816.2017.1324976 ]
Liu Y T , He S J , Wu F L and Webster C . 2010 . Urban villages under China’s rapid urbanization: unregulated assets and transitional neighbourhoods . Habitat International , 34 ( 2 ): 135 - 144 [ DOI: 10.1016/j.habitatint.2009.08.003 http://dx.doi.org/10.1016/j.habitatint.2009.08.003 ]
Lu B , Wei X K and Bi D Y . 2005 . Application of Support Vector Machine in Classification . Journal of Image and Graphics , 10 ( 8 ): 1029 - 1035
陆波 , 尉询楷 , 毕笃彦 . 2005 . 支持向量机在分类中的应用 . 中国图象图形学报 , 10 ( 8 ): 1029 - 1035 [ DOI: 10.11834/jig.200508190 http://dx.doi.org/10.11834/jig.200508190 ]
Ministry of Housing and Urban-Rural Development of the People’s Republic of China . 2016 . GJ J37 - 2012 ( 2016 ): Code for Design of Urban Road Engineering
中华人民共和国住房和城乡建设部 . 2016 . GJ J37 - 2012 ( 2016 ): 城市道路工程设计规范(2016版)
Ren H Y , Wu W , Li T G and Yang Z C . 2019 . Urban villages as transfer stations for dengue fever epidemic: a case study in the Guangzhou, China . PLoS Neglected Tropical Diseases , 13 ( 4 ): e 0007350 [ DOI: 10.1371/journal.pntd.0007350 http://dx.doi.org/10.1371/journal.pntd.0007350 ]
Song J C , Lin T , Li X H and Prishchepov A V . 2018 . Mapping urban functional zones by integrating very high spatial resolution remote sensing imagery and points of interest: a case study of Xiamen, China . Remote Sensing , 10 ( 11 ): 1737 [ DOI: 10.3390/rs10111737 http://dx.doi.org/10.3390/rs10111737 ]
Song M H . 2019 . Object-oriented urban land classification with GF-2 remote sensing image . Remote Sensing Technology and Application , 34 ( 3 ): 547 - 552 , 629
宋明辉 . 2019 . 基于高分二号数据的面向对象城市土地利用分类研究 . 遥感技术与应用 , 34 ( 3 ): 547- 552 , 629 [ DOI: 10.11873/j.issn.1004-0323.2019.3.0547 http://dx.doi.org/10.11873/j.issn.1004-0323.2019.3.0547 ]
Tan K , Zhang Y S , Wang X and Chen Y . 2019 . Object-based change detection using multiple classifiers and multi-scale uncertainty analysis . Remote Sensing , 11 ( 3 ): 359 [ DOI: 10.3390/rs11030359 http://dx.doi.org/10.3390/rs11030359 ]
Tong D , Feng C C and Deng J J . 2011 . Spatial evolution and cause analysis of urban villages: a case study of Shenzhen Special Economic Zone . Geographical Research , 30 ( 3 ): 437 - 446
仝德 , 冯长春 , 邓金杰 . 2011 . 城中村空间形态的演化特征及原因——以深圳特区为例 . 地理研究 , 30 ( 3 ): 437 - 446 [ DOI: 10.11821/yj2011030005 http://dx.doi.org/10.11821/yj2011030005 ]
Wang S G and Wang Y . 2009 . Shadow detection and compensation in high resolution satellite image based on retinex. Proceedings of the 5th International Conference on Image and Graphics . Xi’an, China : IEEE 2009 : 209 - 212 [ DOI: 10.1109/ICIG.2009.56 http://dx.doi.org/10.1109/ICIG.2009.56 ]
Ye Y , Zhang Z X , Zhang X H and Zeng W . 2019 . Human-scale quality on streets: a large-scale and efficient analytical approach based on street view images and new urban analytical tools . Urban Planning International , 34 ( 1 ): 18 - 27
叶宇 , 张昭希 , 张啸虎 , 曾伟 . 2019 . 人本尺度的街道空间品质测度——结合街景数据和新分析技术的大规模、高精度评价框架 . 国际城市规划 , 34 ( 1 ): 18 - 27 [ DOI: 10.22217/upi.2018.490 http://dx.doi.org/10.22217/upi.2018.490 ]
Zhang L P and Shen H F . 2016 . Progress and future of remote sensing data fusion . Journal of Remote Sensing , 20 ( 5 ): 1050 - 1061
张良培 , 沈焕锋 . 2016 . 遥感数据融合的进展与前瞻 . 遥感学报 , 20 ( 5 ): 1050 - 1061 [ DOI: 10.11834/jrs.20166243 http://dx.doi.org/10.11834/jrs.20166243 ]
Zhang L Y , Pei T , Chen Y J , Song C and Liu X Q . 2019 . A review of urban environmental assessment based on street view images . Journal of Geo-Information Science , 21 ( 1 ): 46 - 58
张丽英 , 裴韬 , 陈宜金 , 宋辞 , 刘小茜 . 2019 . 基于街景图像的城市环境评价研究综述 . 地球信息科学学报 , 21 ( 1 ): 46 - 58 [ DOI: 10.12082/dqxxkx.2019.180311 http://dx.doi.org/10.12082/dqxxkx.2019.180311 ]
Zhao C Y . 2018 . Restoration and Classification of Remote Sensing Imagery Based on Multiple Feature Learning . Xi’an : Xidian University
赵崇悦 . 2018 . 基于多特征学习的遥感影像复原与分类 . 西安 : 西安电子科技大学
Zhao L , Ren H , Cui C , Huang Y . 2020 . A Partition-Based Detection of Urban Villages Using High-Resolution Remote Sensing Imagery in Guangzhou, China . Remote Sensing . 12 ( 14 ): 2334 [ DOI: 10.3390/rs12142334 http://dx.doi.org/10.3390/rs12142334 ]
Zhao Y H , Chen G Q , Chen G L , Liu X P and Niu N . 2018 . Integrating multi-source big data to extract buildings of urban villages: a case study of Tianhe district, Guangzhou . Geography and Geo-Information Science , 34 ( 5 ): 7 - 13
赵云涵 , 陈刚强 , 陈广亮 , 刘小平 , 牛宁 . 2018 . 耦合多源大数据提取城中村建筑物——以广州市天河区为例 . 地理与地理信息科学 , 34 ( 5 ): 7 - 13 [ DOI: 10.3969/j.issn.1672-0504.2018.05.002 http://dx.doi.org/10.3969/j.issn.1672-0504.2018.05.002 ]
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