Three Dimensional Change Detection in Urban Environment Using Object Based Image Analysis on DEMs

Authors
F. Tabib Mahmoudi
S. S. Hosseini
Shahid Rajaee Teacher Training University
Abstract


Introduction: Multi temporal changes in built up areas are mainly caused by natural disasters (such as floods and earthquakes) or urban sprawl. Detecting these changes which consist of construction, destruction and renovation of buildings can play an important role in updating three dimensional city models. Multi-temporal remote sensing data are one the powerful tools for detecting urban changes due to the increasing growth and then, for updating the three dimensional city models. Urban changes detection methods using various types of remotely sensed data have been proposed by many researchers to meet a wide range of applications (Singh, 1989). Considering the procedure of algorithms and the utilized multi-temporal remote sensing data, change detection algorithms can be divided into two dimensional and three dimensional categories (Qin et al., 2016). Many of the proposed urban change detection methodologies have utilized only the multi-spectral remote sensing data without considering digital elevation models, which caused some problems in buildings identification (Bouziani et al., 2010; Brunner et al., 2010; Huang et al., 2014; Vakalopoulou et al., 2015). Two dimensional changes detection methods have some serious problems such as high computational cost and inaccessible volumetric information due to the absence of altitude data. Moreover, as digital elevation models can be easily produced recently, the three dimensional changes detection methods are more concerned (Martha et al., 2010; Tian et al., 2014; Waser et al., 2008; Daniel & Doran, 2013; Gruen, 2013). Three dimensional change detection methods are suitable for identifying the changes of high altitude objects such as buildings and their results are more close to reality. Three dimensional change detection methods can be considered in one of the spectral-geometric analysis methods or geometric comparison (Qin et al., 2016).

Methodology: The objective of this study is to provide an effective method for three dimensional changes detection of buildings in urban areas based on Digital Elevation Models (DEMs). The proposed three dimensional building change detection algorithm in this research is considered for estimating the construction of new buildings in flat areas and renovation of low-rise buildings (up to three floors) in order to make high-rise ones (more than three floors). The proposed method in this paper consists of three main steps; 1) generating Digital Surface Model (DSM), Digital Terrain Model (DTM) and normalized DSM for two epochs, 2) performing object based image analysis consists of segmentation and structural classification of DEMs in order to generate multi temporal classification maps, 3) producing the change maps and analyzing the change percentages between various object classes.

Resullts & Discussion: The ability of the proposed algorithm is evaluated in a rapid developing urban area in Tehran, Iran in a 9-years interval. The obtained results represent that the ground and bare soil decreased for about -1.37% and low-rise buildings also decreased for about -9.7%. Moreover, the class of high-rise buildings increased for about +16.4% which conforms making new constructions in addition to renovation of low-rise buildings. As the objective of this research was to investigate the three aspects of changes in built up areas containing new constructions, destruction and renovation of buildings, some interesting results are obtained. The main changes occurred in this region are in the new construction category with 4.8% growth which is occurred to about 132680 square meters of the study area. Moreover, the renovation of low-rise buildings to high-rise ones is 3.05% of land use equivalent to 83889.5 square meters. The obtained results showed 3.89% destructions in the buildings which is occurred to 106896.25 square meters of this study area. Most of the destructions are in the low-rise building class which confirms decreasing the worn texture of the city and urban passages sweating.

Conclusion: According to the results, the construction of new buildings is faster than the vertical growth of the city and its destruction in this 9-years period. As it is clear from the results of this study, change detection in urban environment can help urban planners to manage land resources and prevent the growth of irregular constructions. As high-rise buildings prevent wind, disrupt the urban ecosystem and increase air pollution, it is important to control and manage the vertical growth of the cities.

Kay words: Three dimensional change detection, Building, Object Based Image Analysis, Segmentation, Normalized DSM

Keywords

Subjects

عبداللهی، علی اصغر، خبازی، مصطفی، درانی زاده، زهرا، "مدلسازی تغییرات کاربری اراضی با استفاده از شبکه‌ی عصبی پرسپترون (مطالعه موردی : شهر لاهیجان) " برنامه ریزی و آمایش فضا، دوره 24، شماره 1، ص. 79-49، 1399.
علیمحمدی، عباس، عیسوی، وحید، کرمی، جلال، "افزایش دقت در طبقه بندی کاربری و پوشش اراضی مبتنی بر شاخص های قابل استخراج از واریوگرام در تصاویر ماهواره ای" برنامه ریزی و آمایش فضا، دوره 15، شماره 3، ص. 18-1، 1390.
علیمحمدی، عباس، موسیوند، علی جعفر، شایان، سیاوش، "پیش بینی تغییرات کاربری و پوشش زمین با استفاده از تصاویر ماهواره ای و مدل زنجیره ای مارکوف" برنامه ریزی و آمایش فضا، دوره 14، شماره 3، ص. 131-117، 1389.
Abdollahi, A., Khabbazi, M. and Daranizadeh, Z. “Modeling Land Use Changes Using Perceptron Neural Network (Case Study: Lahijan City),” The Journal of Spatial Planning 24 (1), 49-79, 2020.
Akca, D., Freeman, M., Sargent, I. and Gruen, A. “Quality assessment of 3D building data”. The Photogrammetric Record 25 (132), 339-355, 2010.
Alimohammadi, A., Eisavi, V. and Karami, J. “Increasing accuracy in land use/ cover classification based on extracted indicators from variogram in satellite images,” The Journal of Spatial Planning 15 (3), 1-18, 2001.
Alimohammadi, A., Mousivand, A.J. and Shayan, S. “Predicting land use/ cover changes using satellite images and the Markov chain model,” The Journal of Spatial Planning 14 (3), 117-131, 2000.
Bouziani, M., Goïta, K. and He, D.-C. “Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge”. ISPRS Journal of Photogrammetry and Remote Sensing 65 (1), 143-153, 2010.
Brunner, D., Lemoine, G. and Bruzzone, L. “Earthquake damage assessment of buildings using VHR optical and SAR imagery”. Geoscience and Remote Sensing, IEEE Transactions on 48 (5), 2403-2420, 2010.
Chaabouni-Chouayakh, H. and Reinartz, P. “Towards automatic 3D change detection inside urban areas by combining height and shape information”. Photogrammetrie-Fernerkundung-Geoinformation 2011 (4), 205-217, 2011.
Chaabouni-Chouayakh, H., d'Angelo, P., Krauss, T. and Reinartz, P. “Automatic urban area monitoring using digital surface models and shape features”. In: Urban Remote Sensing Event (JURSE), 2011 Joint, pp. 85-88, 2011.
Chaabouni-Chouayakh, H., Krauss, T., d’Angelo, P. and Reinartz, P. “3D Change Detection Inside Urban Areas Using Different Digital Surface Models”. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 38 86-91, 2010.
Champion, N., Boldo, D., Pierrot-Deseilligny, M. and Stamon, G. “2D building change detection from high resolution satellite imagery: A two-step hierarchical method based on 3D invariant primitives”. Pattern Recognition Letters 31 (10), 1138-1147, 2010.
Daniel, S. and Doran, M. A. “geoSmartCity: geomatics contribution to the smart city”. In: Proceedings of the 14th Annual International Conference on Digital Government Research, pp. 65-71, 2013.
Dini, G., Jacobsen, K., Rottensteiner, F., Al Rajhi, M. and Heipke, C. “3D Building Change Detection Using High Resolution Stereo Images and a GIS Database”. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 1 299-304, 2012.
Eden, I. and Cooper, D. B. “Using 3D line segments for robust and efficient change detection from multiple noisy images”. In: 10th European Conference on Computer Vision, Marseille, France, 12-18, October, pp. 172-185, 2008.
Gong, P., Biging, G. S. and Standiford, R. “Technical Note: Use of Digital Surface Model for Hardwood Rangeland Monitoring”. Journal of Range Management 53 (6), 622-626, 2000.
Gruen, A. “Next generation smart cities-the role of geomatics”. BBC 26.17: 32.81 G 547 (25), 25, 2013.
Gruen, A. and Akca, D. “Least squares 3D surface and curve matching”. ISPRS Journal of Photogrammetry and Remote Sensing 59 (3), 151-174, 2005.
Guerin, C., Binet, R. and Pierrot-Deseilligny, M. “Automatic Detection of Elevation Changes by Differential DSM Analysis: Application to Urban Areas”. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7 (10), 4020-4037, 2014.
Huang, X., Zhang, L., and Zhu, T. “Building change detection from multi temporal high-resolution remotely sensed images based on a morphological building index”. Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of 7 (1), 105-115, 2014.
Martha, T. R., Kerle, N., Jetten, V., Westen, C. J. and Kumar, K. V. “Landslide volumetric analysis using Cartosat-1-derived DEMs”. IEEE Geoscience and Remote Sensing Letters 7 (3), 582-586, 2010.
Matikainen, L., Hyyppä, J., Ahokas, E., Markelin, L. and Kaartinen, H. “Automatic detection of buildings and changes in buildings for updating of maps”. Remote Sensing 2 (5), 1217-1248, 2010.
Nebiker, S., Lack, N. and Deuber, M. “Building Change Detection from Historical Aerial Photographs Using Dense Image Matching and Object-Based Image Analysis”. Remote Sensing 6 (9), 8310-8336, 2014.
Pang, S., Hu, X., Wang, Z. and Lu, Y. “Object-Based Analysis of Airborne LiDAR Data for Building Change Detection”. Remote Sensing 6 (11), 10733-10749, 2014.
Qin, R. and Gruen, A. “3D change detection at street level using mobile laser scanning point clouds and terrestrial images”. ISPRS Journal of Photogrammetry and Remote Sensing 90 (2014), 23-35, 2014.
Qin, R., Tian, J. and Reinartz, P. “3D change detection – Approaches and applications”. ISPRS Journal of Photogrammetry and Remote Sensing (122). 41-56. 10.1016/j.isprsjprs.2016.09.013, 2016.
Rottensteiner, F., Trinder, J., Clode, S. and Kubik, K. “Building detection by fusion of airborne laser scanner data and multi-spectral images: Performance evaluation and sensitivity analysis”. ISPRS Journal of Photogrammetry and Remote Sensing 62 (2), 135-149, 2007.
Sasagawa, A., Baltsavias, E., Aksakal, S. K. and Wegner, J. D. “Investigation on automatic change detection using pixel-changes and DSM-changes with ALOS-PRISM triplet images”. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 1 (2), 213-217, 2013.
Sasagawa, A., Watanabe, K., Nakajima, S., Koido, K., Ohno, H. and Fujimura, H. “Automatic change detection based on pixel-change and DSM-change”. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 37 (Part B7), 1645-1650, 2008.
Schenk, T., Krupnik, A. and Postolov, Y. “Comparative study of surface matching algorithms”. International Archives of Photogrammetry and Remote Sensing 33 (B4), 518-524, 2000.
Singh, A. “Digital Change Detection Techniques Using Remotely-Sensed Data”. International Journal of Remote Sensing 10 (6), 989-1003, 1989.
Stal, C., Tack, F., De Maeyer, P., De Wulf, A. and Goossens, R. “Airborne photogrammetry and lidar for DSM extraction and 3D change detection over an urban area–a comparative study”. International Journal of Remote Sensing 34 (4), 1087-1110, 2013.
Tabib Mahmoudi, F., Samadzadegan, F., Reinartz, P. “Object oriented image analysis based on multi-agent recognition system”. Computers & Geosciences 54 (2013), 219–230, 2013.
Tian, J., Nielsen, A. A. and Reinartz, P. “Improving change detection in forest areas based on stereo panchromatic imagery using kernel MNF”. IEEE Transactions on Geoscience and Remote Sensing 52 (11), 7130 – 7139, 2014.
Vakalopoulou, M., Karantzalos, K., Komodakis, N. and Paragios, N. “Simultaneous registration and change detection in multitemporal, very high resolution remote sensing data”. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 61-69, 2015.
Vu, T., Matsuoka, M. and Yamazaki, F. “LIDAR-based change detection of buildings in dense urban areas”. In: Geoscience and Remote Sensing Symposium. IGARSS'04. IEEE International, pp. 3413-3416, 2004.
Waser, L., Baltsavias, E., Ecker, K., Eisenbeiss, H., Feldmeyer-Christe, E., Ginzler, C., Küchler, M. and Zhang, L. “Assessing changes of forest area and shrub encroachment in a mire ecosystem using digital surface models and CIR aerial images”. Remote Sensing of Environment 112 (5), 1956-1968, 2008.
Xiao, W., Vallet, B. and Paparoditis, N. “Change detection in 3D point clouds acquired by a mobile mapping system”. ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences 1 (2), 331-336, 2013.
Zavodny, A. G. “Change detection in LiDAR scans of urban environments”. Computer Science and Engineering, University of Notre Dame, 2012.
The Journal of Spatial Planning and Geomatics
Volume 24, Issue 4
December 2020
Pages 99-116