多源空间数据融合的城市人居环境监测模型与应用研究
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摘要
人居环境监测作为城市人居环境建设与管理实践提升的基本,是目前人居环境研究落地的重点。传统的城市人居环境监测在数据更新速度、精度等方面存在不足,难以满足精细化管理需求。提出利用遥感数据与互联网的兴趣点POI(Point of interest)数据结合,建立人居环境监测模型。模型主要有两个关键环节,一是构建自动化提取建筑物算法,该算法通过建立地物特征集,以POI点对应样本为种子,利用全局最优和区域生长算法,自动提取城市建筑物,再利用全局最优算法确定其他地类的阈值;二是人居环境指标计算,将建筑物、绿地、水体信息提取结果与POI数据结合,利用密度类与距离类空间分析算法,分别计算自然、社会经济类指标。基于上述模型,利用2018年4月的北京二号遥感影像和POI点数据在北京市回龙观社区进行实验验证,结果显示:信息提取结果中,总体精度超过95%,Kappa系数超过92%,提取效率提高2.3倍,表明信息提取精度高且可信,适合工程化应用。计算回龙观社区人居环境监测指标,分析结果认为,社区内自然类指标差异不大,但缺乏水体生态系统,生物多样性不够丰富,社区内的商业比较繁华,但是学校和医疗不充足,尤其是缺乏大型公立医院。综上,通过人居环境监测模型研究和应用分析,将遥感数据和互联网数据结合应用于人居环境质量监测有效提高了精度和速度,有利于业务化,服务政府管理。
Monitoring of human settlement environment, a basic practice in urban human settlement environment construction and management, is the focus of human settlement environment research. The traditional urban human settlement environmental data have shortcomings in terms of renewal speed and accuracy. This paper proposes to use remote sensing images and point of interest(POI) from the internet to build a human settlement environment model. The model is composed of two key parts. The first part involves constructing an automatic building extraction algorithm, which are the global optimization and region growth algorithms, based on urban land coverage feature sets and taking corresponding POI point samples as seeds. The second part involves the calculation of the human settlement environment index using density and distance spatial analysis algorithms, which use the land cover extraction results with POI data as input data. Based on the above model, Beijing-2 remote sensing images and POI data in April 2018 were used to verify the results in the Huilongguan community of Beijing. The results show that the overall accuracy of the information extraction results is more than 95%, the Kappa coefficient is more than 92%, and the extraction efficiency is improved by 2.3-fold. On applying the model to monitor human settlements in Huilongguan community, it was found that there is little difference in natural indicators, but due to the overall lack of water ecosystem, biodiversity is not rich enough. Socioeconomic indicators mainly account for the impact of business, school, and medical care. On the whole, businesses in the community are prosperous, and schools and medical care are adequate, the latter is the case especially in large hospitals. Through the research and application analysis of the human settlements monitoring model, the application of remote sensing data and internet data fusion in human settlement environmental quality monitoring effectively improves the accuracy and speed and is conducive to business applications and government management.
Monitoring of human settlement environment, a basic practice in urban human settlement environment construction and management, is the focus of human settlement environment research. The traditional urban human settlement environmental data have shortcomings in terms of renewal speed and accuracy. This paper proposes to use remote sensing images and point of interest(POI) from the internet to build a human settlement environment model. The model is composed of two key parts. The first part involves constructing an automatic building extraction algorithm, which are the global optimization and region growth algorithms, based on urban land coverage feature sets and taking corresponding POI point samples as seeds. The second part involves the calculation of the human settlement environment index using density and distance spatial analysis algorithms, which use the land cover extraction results with POI data as input data. Based on the above model, Beijing-2 remote sensing images and POI data in April 2018 were used to verify the results in the Huilongguan community of Beijing. The results show that the overall accuracy of the information extraction results is more than 95%, the Kappa coefficient is more than 92%, and the extraction efficiency is improved by 2.3-fold. On applying the model to monitor human settlements in Huilongguan community, it was found that there is little difference in natural indicators, but due to the overall lack of water ecosystem, biodiversity is not rich enough. Socioeconomic indicators mainly account for the impact of business, school, and medical care. On the whole, businesses in the community are prosperous, and schools and medical care are adequate, the latter is the case especially in large hospitals. Through the research and application analysis of the human settlements monitoring model, the application of remote sensing data and internet data fusion in human settlement environmental quality monitoring effectively improves the accuracy and speed and is conducive to business applications and government management.
引文
[1] 陈呈奕,张文忠,湛东升,李雪丽. 环渤海地区城市人居环境质量评估及影响因素. 地理科学进展.2017, 36(12): 1562-1570.
[2] 王杨,孟庆香,宋文博,康凯,郭子龙. 基于层次分析法与主成分分析法的郑州市人居环境质量评价研究. 河南农业大学学报.2014,48(3):359-364.
[3] 赵林,韩增林,马慧强. 东北地区城市人居环境质量时空变化分析.地域研究与开发.2013,32(2):73-78.
[4] 李帅, 魏虹, 倪细炉, 顾艳文, 李昌晓. 基于层次分析法和熵权法的宁夏城市人居环境质量评价. 应用生态学报, 2014, 25(9): 2700-2708.
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[6] 魏秀岭. 遗传算法的全局阈值在图像分割中应用. 数字技术与应用, 2012, (6): 128-129.
[7] Manno-Kovacs A, Sziranyi T. Orientation-selective building detection in aerial images. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 108: 94-112.
[8] Xia X B, Wang R S. Extraction of residential building instances in suburban areas from mobile LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 144: 453-468.
[9] Izadi M, Saeedi P. Three-dimensional Polygonal Building Model Estimation from Single Satellite Image. IEEE Transactions on Geoscience and Remote Sensing, 2012,50(6):2254-2272.
[10] Ok A O. Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 86: 21-40.
[11] Sirmacek B, Unsalan C. A Probabilistic Framework to Detect Buildings in Aerial and Satellite Images. IEEE Transactions on Geoscience and Remote Sensing, 2011,49(1):211-221.
[12] Maruyama Y, Tashiro A, Yamazaki F. Use of digital surface model constructed from digital aerial images to detect collapsed buildings during earthquake. Procedia Engineering, 2011, 14: 552-558.
[13] Tournaire O, Brédif M, Boldo D, Durupt M. An efficient stochastic approach for building footprint extraction from digital elevation models. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65(4): 317-327.
[14] Ferro A, Brunner D, Bruzzone I. Automatic detection and reconstruction of building radar footprints from single VHR SAR images. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(2): 935-952.
[15] 刘文涛. 基于卷积神经网络的城市区域建筑物自动提取研究[D]. 成都: 电子科技大学, 2018.
[16] 陶超,谭毅华,彭碧发,田金文.一种基于概率潜在语义模型的高分辨率遥感影像分类方法.测绘学报,2011,40(2):156-162.
[17] Tang H, Shen L, Qi Y F, Chen Y H, Shu Y, Li J, Clausi D A. A multiscale latent dirichlet allocation model for object-oriented clustering of VHR panchromatic satellite images. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3): 1680-1692.
[18] Kabolizade M, Ebadi H, Ahmadi S. An improved snake model for automatic extraction of buildings from urban aerial images and LiDAR data. Computers, Environment and Urban Systems, 2010, 34(5): 435-441.
[19] Liasis G, Stavrou S. Building extraction in satellite images using active contours and colour features. International Journal of Remote Sensing, 2016, 37(6): 1127-1153.
[20] Alshehhi R, Marpu P R, Woon W L, Mura M D. Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 130: 139-149.
[21] Ma J C, Du K M, Zhang L X, Zheng F X, Chu J X, Sun Z F. A segmentation method for greenhouse vegetable foliar disease spots images using color information and region growing. Computers and Electronics in Agriculture, 2017, 142: 110-117.
[22] 余洁, 余峰, 张晶, 刘振宇. 结合区域生长与道路基元的高分辨率遥感影像道路提取. 武汉大学学报: 信息科学版, 2013, 38(7): 761-764.
[23] 肖奥, 赵文吉. 基于最小异质性区域生长法的多尺度城市地物影像分割. 首都师范大学学报: 自然科学版, 2010, 31(1): 60-64.
[24] 喻红艳. 基于遥感的城市小区居住环境质量综合评价研究[D]. 武汉: 华中科技大学,2007: 30-52.
[25] 曹雪芝,徐梦洁. 基于GIS的住宅小区人居环境评价. 地理空间信息,2018,16(1):83-85.
[2] 王杨,孟庆香,宋文博,康凯,郭子龙. 基于层次分析法与主成分分析法的郑州市人居环境质量评价研究. 河南农业大学学报.2014,48(3):359-364.
[3] 赵林,韩增林,马慧强. 东北地区城市人居环境质量时空变化分析.地域研究与开发.2013,32(2):73-78.
[4] 李帅, 魏虹, 倪细炉, 顾艳文, 李昌晓. 基于层次分析法和熵权法的宁夏城市人居环境质量评价. 应用生态学报, 2014, 25(9): 2700-2708.
[5] 章毓晋. 图像工程(第二版). 北京: 清华大学出版社, 2007: 286-288.
[6] 魏秀岭. 遗传算法的全局阈值在图像分割中应用. 数字技术与应用, 2012, (6): 128-129.
[7] Manno-Kovacs A, Sziranyi T. Orientation-selective building detection in aerial images. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 108: 94-112.
[8] Xia X B, Wang R S. Extraction of residential building instances in suburban areas from mobile LiDAR data. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 144: 453-468.
[9] Izadi M, Saeedi P. Three-dimensional Polygonal Building Model Estimation from Single Satellite Image. IEEE Transactions on Geoscience and Remote Sensing, 2012,50(6):2254-2272.
[10] Ok A O. Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts. ISPRS Journal of Photogrammetry and Remote Sensing, 2013, 86: 21-40.
[11] Sirmacek B, Unsalan C. A Probabilistic Framework to Detect Buildings in Aerial and Satellite Images. IEEE Transactions on Geoscience and Remote Sensing, 2011,49(1):211-221.
[12] Maruyama Y, Tashiro A, Yamazaki F. Use of digital surface model constructed from digital aerial images to detect collapsed buildings during earthquake. Procedia Engineering, 2011, 14: 552-558.
[13] Tournaire O, Brédif M, Boldo D, Durupt M. An efficient stochastic approach for building footprint extraction from digital elevation models. ISPRS Journal of Photogrammetry and Remote Sensing, 2010, 65(4): 317-327.
[14] Ferro A, Brunner D, Bruzzone I. Automatic detection and reconstruction of building radar footprints from single VHR SAR images. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(2): 935-952.
[15] 刘文涛. 基于卷积神经网络的城市区域建筑物自动提取研究[D]. 成都: 电子科技大学, 2018.
[16] 陶超,谭毅华,彭碧发,田金文.一种基于概率潜在语义模型的高分辨率遥感影像分类方法.测绘学报,2011,40(2):156-162.
[17] Tang H, Shen L, Qi Y F, Chen Y H, Shu Y, Li J, Clausi D A. A multiscale latent dirichlet allocation model for object-oriented clustering of VHR panchromatic satellite images. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(3): 1680-1692.
[18] Kabolizade M, Ebadi H, Ahmadi S. An improved snake model for automatic extraction of buildings from urban aerial images and LiDAR data. Computers, Environment and Urban Systems, 2010, 34(5): 435-441.
[19] Liasis G, Stavrou S. Building extraction in satellite images using active contours and colour features. International Journal of Remote Sensing, 2016, 37(6): 1127-1153.
[20] Alshehhi R, Marpu P R, Woon W L, Mura M D. Simultaneous extraction of roads and buildings in remote sensing imagery with convolutional neural networks. ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 130: 139-149.
[21] Ma J C, Du K M, Zhang L X, Zheng F X, Chu J X, Sun Z F. A segmentation method for greenhouse vegetable foliar disease spots images using color information and region growing. Computers and Electronics in Agriculture, 2017, 142: 110-117.
[22] 余洁, 余峰, 张晶, 刘振宇. 结合区域生长与道路基元的高分辨率遥感影像道路提取. 武汉大学学报: 信息科学版, 2013, 38(7): 761-764.
[23] 肖奥, 赵文吉. 基于最小异质性区域生长法的多尺度城市地物影像分割. 首都师范大学学报: 自然科学版, 2010, 31(1): 60-64.
[24] 喻红艳. 基于遥感的城市小区居住环境质量综合评价研究[D]. 武汉: 华中科技大学,2007: 30-52.
[25] 曹雪芝,徐梦洁. 基于GIS的住宅小区人居环境评价. 地理空间信息,2018,16(1):83-85.