Recognition and monitoring of buildings in aerodrome obstacle free space

doi:10.13474/j.cnki.11-2246.2022.0090

Abstract

Abstract: In view of the current building recognition and monitoring,the shadow height measurement method based on single highresolution remote sensing image and the stereo image pair method based on multi-view image are independent of the recognition of the contour or name of the building and the height monitoring,resulting in low automation level,high data redundancy and cost.Based on the data of digital surface model (DSM) and point of interest (POI),this paper proposes an integrated method for building recognition and monitoring in airport clearance area,and verifies the feasibility of this method by taking Shenzhen Baoan International Airport as a test area.The experimental results show that:① The accuracy of this method in building recognition and monitoring only depends on the accuracy of DSM,but there is no limitation on the data source of DSM.② Based on the height change rate of potentially dangerous buildings,the irregular dynamic monitoring can ensure the safety of airport clearance area during the monitoring interval and reduce the redundancy and cost of data problems caused by repeated identification and monitoring of all buildings.

Key words: building height;identification and monitoring;DSM;POI

CLC Number:

P237

TONG Kuang, SONG Yang, KONG Xiangfen. Recognition and monitoring of buildings in aerodrome obstacle free space[J]. Bulletin of Surveying and Mapping, 2022, 0(3): 127-131.

References

[1] ICAO. International standards and recommended practices:aerodromes; annex 14 to the Convention on International Civil Aviation; volume I:aerodrome design and operations[S]. Montreal:ICAO,1999.
[2] LIEBE U, PREISENDÖRFER P, BRUDERER E H. The social acceptance of airport expansion scenarios:a factorial survey experiment[J]. Transportation Research Part D:Transport and Environment, 2020, 84:102363.
[3] WEI Y D, LI H, YUE W Z. Urban land expansion and regional inequality in transitional China[J]. Landscape and Urban Planning, 2017, 163:17-31.
[4] 程国旗,张继贤,李阳春,等.高分影像建筑物阴影优化提取与高度估算[J].测绘科学, 2020, 45(8):103-109, 137.
[5] 赵强,杨志,苏红超,等.基于高分二号卫星遥感数据的建筑物高度提取[J].大气与环境光学学报, 2019, 14(6):455-462.
[6] 龙恩,汪源,孟钢,等.基于同名特征的建筑物高度高分影像提取方法[J].遥感技术与应用, 2019, 34(1):107-114.
[7] 田峰,李虎.联合星载光学与SAR图像的城市大面积建筑物高度快速提取[J].测绘学报, 2017, 46(7):891-899.
[8] 刘晓龙,张永红,宋伟东.时序PSInSAR研究建筑物高度信息提取[J].武汉大学学报(信息科学版), 2017, 42(4):482-487.
[9] 陈亭,祝善友,张桂欣,等.高分辨率遥感影像阴影与立体像对提取建筑物高度比较研究[J].地球信息科学学报, 2016, 18(9):1267-1275.
[10] XU Y, MA P F, NG E, et al. Fusion of WorldView-2 stereo and multitemporal TerraSAR-X images for building height extraction in urban areas[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12(8):1795-1799.
[11] SIDDIQUI F, TENG S, AWRANGJEB M, et al. A robust gradient based method for building extraction from LiDAR and photogrammetric imagery[J]. Sensors, 2016, 16(7):1110.
[12] XIA Liegang, ZHANG Xiongbo, ZHANG Junxia, et al. Refined extraction of buildings with the semantic edgeassisted approach from very high-resolution remotely sensed imagery[J]. International Journal of Remote Sensing, 2020, 41(21):8352-8365.
[13] LIASIS G, STAVROU S. Satellite images analysis for shadow detection and building height estimation[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 119:437-450.
[14] LI Xuecao, ZHOU Yuyu, GONG Peng, et al. Developing a method to estimate building height from Sentinel-1 data[J]. Remote Sensing of Environment, 2020, 240:111705.
[15] 蔡良才,邵斌,郑汝海,等.机场净空区范围确定方法[J].交通运输工程学报, 2004, 4(4):40-43.
[16] DI RITA M, NASCETTI A, CRESPI M. FOSS4G DATE for DSMs generation from tri-stereo optical satellite images:development and first results[J]. European Journal of Remote Sensing, 2018, 51(1):472-485.