HIGH RISE BUILDING IDENTIFICATION FROM SPOT 6 MULTISPECTRAL AND DIGITAL SURFACE MODEL (DSM) USING OBJECT BASED IMAGE ANALYSIS

Zylshal Zylshal(1), Jalu Tejo Nugroho(2), Indah Prasasti(3),


(1) Remote Sensing Application Center, Indonesian National Institute of Aeronautics and Space (LAPAN), Jl. Kalisari No. 8, Pekayon, Pasar Rebo, Jakarta Timur, Indonesia
(2) Remote Sensing Application Center, Indonesian National Institute of Aeronautics and Space (LAPAN), Jl. Kalisari No. 8, Pekayon, Pasar Rebo, Jakarta Timur, Indonesia
(3) Remote Sensing Application Center, Indonesian National Institute of Aeronautics and Space (LAPAN), Jl. Kalisari No. 8, Pekayon, Pasar Rebo, Jakarta Timur, Indonesia

Abstract

This study focuses on one aspect of urban geometry called urban canyon. Urban canyon defined by a relatively narrow street lined by tall buildings. The initial step to extract the urban canyon is to identify the tall buildings. This study aims to discuss the potential use of the SPOT-6 multispectral data and its digital surface model (DSM), using object-based image analysis methods and terrain analysis, to identify the high-rise buildings in some part of Jakarta, Indonesia. Using slope and elevation percentile from the DSM as well as the spectral information of the SPOT-6 image, we then processed using the Object Image Analysis (OBIA) method and decision tree algorithm (crisp classification), we are able to obtained the identification rate of 78% with mean location accuracy of 30 meter (5 pixels).

Keywords

High-rise Building, SPOT-6, OBIA, DSM, Urban Geometry

Full Text:

PDF

References

REFERENCES

Amin, M. B. Al. (2015). Pemanfaatan Teknologi Lidar Dalam Analisis Genangan Banjir Akibat Luapan Sungai Berdasarkan Simulasi Model Hidrodinamik. Info Teknik, 16(1), 21–32.

Ando, H., Morishima, W., Yokoyama, H., & Akasaka, I. (2009). Effects of Urban Geometry on Urban Heat Islands in Tokyo. In The Seventh International Conference on Urban Climate (p. 4). Yokohama, Japan.

Astrium. (2014). SPOT 6/SPOT 7 Technical Sheet, 1–4.

Bachofer, F., & Hochschild, V. (2015). A SVM-based Approach to Extract Building Footprints from Pléiades Satellite Imagery. In Geotech Rwanda 2015 (pp. 1–4). Kigali, Rwanda.

Battista, G., Evangelisti, L., Guattari, C., & Vollaro, R. D. L. (2015). On the Influence of Geometrical Features and Wind Direction over an Urban Canyon Applying a FEM Analysis. Energy Procedia, 81, 11–21. https://doi.org/10.1016/j.egypro.2015.12.054

Belgiu, M., Dragut, L., & Strobl, J. (2014). Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 87, 205–215. https://doi.org/10.1016/j.isprsjprs.2013.11.007

Benediktsson, J. A., Chanussot, J., & Moon, W. M. (2012). Very high-resolution remote sensing: challenges and opportunities. Proceedings of the IEEE, 100(6), 1907–1910. https://doi.org/10.1109/JPROC.2012.2190811

Bernard, M., Decluseau, D., Gabet, L., & Nonin, P. (2013). 3D Capabilities of Spot 6. Retrieved from http://www.intelligence-airbusds.com/files/pmedia/public/r28533_9_icc2013_3d_capabilities_of_spot_6.pdf

Beyer, H. L. (2012). Geospatial Modelling Environment. Geospatial Modeling Environment.

Blaschke, T., & Hay, G. J. (2001). Object-oriented image analysis and scale-space: theory and methods for modeling and evaluating multiscale landscape structure. International Archives of Photogrammetry and Remote Sensing, 34(4), 22–29.

Brédif, M., Tournaire, O., Vallet, B., & Champion, N. (2013). Extracting polygonal building footprints from digital surface models: A fully-automatic global optimization framework. ISPRS Journal of Photogrammetry and Remote Sensing, 77, 57–65. https://doi.org/10.1016/j.isprsjprs.2012.11.007

Burnett, C., & Blaschke, T. (2003). A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecological Modelling, 168(3), 233–249. https://doi.org/10.1016/S0304-3800(03)00139-X

Craighead, G. (2009). High-Rise Building Definition, Development, and Use. High-Rise Security and Fire Life Safety (Third Edition), (August 2005), 1–26. https://doi.org/10.1016/B978-1-85617-555-5.00001-8

Drǎguţ, L., Tiede, D., & Levick, S. R. (2010). ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data. International Journal of Geographical Information Science, 24(6), 859–871. https://doi.org/10.1080/13658810903174803

Duke, G. D., Kienzle, S. W., Johnson, D. L., & Byrne, J. M. (2003). Improving overland flow routing by incorporating ancillary road data into Digital Elevation Models. Journal of Spatial Hydrology, 3(2), 27.

Dupuy, S., Barbe, E., & Balestrat, M. (2012). An object-based image analysis method for monitoring land conversion by artificial sprawl use of RapidEye and IRS data. Remote Sensing, 4(2), 404–423. https://doi.org/10.3390/rs4020404

Duro, D. C., Franklin, S. E., & Dube, M. G. (2012). A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment, 118, 259–272. https://doi.org/10.1016/j.rse.2011.11.020

Fraser, C. S., Baltsavias, E., & Gruen, A. (2002). Processing of Ikonos imagery for submetre 3D positioning and building extraction. ISPRS Journal of Photogrammetry and Remote Sensing, 56(3), 177–194. https://doi.org/http://dx.doi.org/10.1016/S0924-2716(02)00045-X

Gerçek, D., Toprak, V., & Strobl, J. (2011). Object-based classification of landforms based on their local geometry and geomorphometric context. International Journal of Geographical Information Science, 25(6), 1011–1023. https://doi.org/10.1080/13658816.2011.558845

Hagenlocher, M., Lang, S., & Tiede, D. (2012). Integrated assessment of the environmental impact of an IDP camp in Sudan based on very high resolution multi-temporal satellite imagery. Remote Sensing of Environment, 126(August 2016), 27–38. https://doi.org/10.1016/j.rse.2012.08.010

Hay, G. J., & Castilla, G. (2008). Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline. In T. Blaschke, S. Lang, & G. J. Hay (Eds.), Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications (pp. 75–89). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-77058-9_4

Huggett, R. J., & Cheesman, J. (2002). Topography and the Environment. Prentice Hall.

Hutchinson, M. F., Stein, J. A., Stein, J. L., & Xu, T. (2009). Locally adaptive gridding of noisy high resolution topographic data. 18th World IMACS Congress and MODSIM09 International Congress on Modelling and Simulation: Interfacing Modelling and Simulation with Mathematical and Computational Sciences, Proceedings, (July), 2493–2499.

Hutchinson, M., Xu, T., & Stein, J. (2011). Recent Progress in the ANUDEM Elevation Gridding Procedure. In T. Hengel, I. S. Evans, J. P. Wilson, & M. Gould (Eds.), Geomorphometry 2011 (pp. 19–22). Redlands, California.

Jin, X., & Davis, C. H. (2005). Automated Building Extraction from High-Resolution Satellite Imagery in Urban Areas Using Structural, Contextual, and Spectral Information. EURASIP Journal on Advances in Signal Processing, 2005(14), 2196–2206. https://doi.org/10.1155/ASP.2005.2196

Kontoes, C., Wilkinson, G. G., Burrill, A., Goffredo, S., & Mégier, J. (1993). An experimental system for the integration of GIS data in knowledge-based image analysis for remote sensing of agriculture. International Journal of Geographical Information Systems, 7(3), 247–262. https://doi.org/10.1080/02693799308901955

LAPAN. (2014). The Remote Sensing Monitoring Program of Indonesia’s National Carbon Accounting System: Methodology and Products, Version 1. Jakarta, Indonesia.

Liu, Z. J., Wang, J., & Liu, W. P. (2005). Building extraction from high resolution imagery based on multi-scale object oriented classification and probabilistic Hough transform. In Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS ’05. (Vol. 4, pp. 2250–2253). IEEE. https://doi.org/10.1109/IGARSS.2005.1525421

Myint, S. W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q. (2011). Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery. Remote Sensing of Environment, 115(5), 1145–1161. https://doi.org/10.1016/j.rse.2010.12.017

Nyaruhuma, A. P., Gerke, M., Vosselman, G., & Mtalo, E. G. (2012). Verification of 2D building outlines using oblique airborne images. ISPRS Journal of Photogrammetry and Remote Sensing, 71, 62–75. https://doi.org/10.1016/j.isprsjprs.2012.04.007

Pandya, S. V, & Brotas, L. (2014). Tall Buildings and the Urban Microclimate in the City of London. In 30th International PLEA Conference (pp. 1–8). CEPT University, Ahmedabad.

Pfeifer, N., Rutzinger, M., Rottensteiner, F., Muecke, W., & Hollaus, M. (2007). Extraction of building footprints from airborne laser scanning: Comparison and validation techniques. 2007 Urban Remote Sensing Joint Event, URS. https://doi.org/10.1109/URS.2007.371854

Prerna, R., & Singh, C. K. (2015). Evaluation of LiDAR and image segmentation based classification techniques for automatic building footprint extraction for a segment of Atlantic County, New Jersey. Geocarto International, 6049(October), 1–20. https://doi.org/10.1080/10106049.2015.1076060

Sebari, I., & He, D. C. (2013). Automatic fuzzy object-based analysis of VHSR images for urban objects extraction. ISPRS Journal of Photogrammetry and Remote Sensing, 79, 171–184. https://doi.org/10.1016/j.isprsjprs.2013.02.006

Shaker, I. F., Abd-Elrahman, A., Abdel-Gawad, A. K., & Sherief, M. A. (2011). Building extraction from high resolution space images in high density residential areas in the Great Cairo region. Remote Sensing, 3(4), 781–791. https://doi.org/10.3390/rs3040781

Sirmacek, B., & Unsalan, C. (2010). Urban area detection using local feature points and spatial voting. IEEE Geoscience and Remote Sensing Letters, 7(1), 146–150. https://doi.org/10.1109/LGRS.2009.2028744

Song, J., Wu, J., & Jiang, Y. (2015). Extraction and reconstruction of curved surface buildings by contour clustering using airborne LiDAR data. Optik, 126(5), 513–521. https://doi.org/10.1016/j.ijleo.2015.01.011

Tomljenovic, I., Höfle, B., Tiede, D., & Blaschke, T. (2015). Building Extraction from Airborne Laser Scanning Data: An Analysis of the State of the Art. Remote Sensing, 7(4), 3826–3862. https://doi.org/10.3390/rs70403826

Trimble. (2007). eCognition® Developer 7 reference book. Definiens AG, München, 21–24.

Trimble. (2014). eCognition ® Developer Reference Book. Trimble. Germany.

Turker, M., & Koc-San, D. (2015). Building extraction from high-resolution optical spaceborne images using the integration of support vector machine (SVM) classification, Hough transformation and perceptual grouping. International Journal of Applied Earth Observation and Geoinformation, 34(1), 58–69. https://doi.org/10.1016/j.jag.2014.06.016

Wald, L., Ranchin, T., & Mangolini, M. (1997). Fusion of Satellite Images of Different Spatial Resolution: Assessing the Quality of Resulting Images. Photogrammetric Engineering & Remote Sensing, 63(6), 691–699.

Whiteside, T. G., Maier, S. W., & Boggs, G. S. (2014). Area-based and location-based validation of classified image objects. International Journal of Applied Earth Observation and Geoinformation, 28(1), 117–130. https://doi.org/10.1016/j.jag.2013.11.009

Wilson, J. P., & Gallant, J. C. (2000). Terrain Analysis: Principles and Applications. Wiley.

Zevenbergen, L. W., & Thorne, C. R. (1987). Quantitative analysis of land surface topography. Earth Surf. Process. Landforms. https://doi.org/10.1002/esp.3290120107

Zhan, Q., Molenaar, M., Tempfli, K., & Shi, W. (2005). Quality assessment for geo-spatial objects derived from remotely sensed data. International Journal of Remote Sensing, 26(14), 2953–2974. https://doi.org/10.1080/01431160500057764

Zylshal, Danoedoro, P., & Haryono, E. (2013). An object based image analysis approach to semi-automated karst morphology extraction. 34th Asian Conference on Remote Sensing 2013, ACRS 2013, 1, 927–934. Retrieved from http://a-a-r-s.org/acrs/administrator/components/com_jresearch/files/publications/SC02-0308_Full_Paper_ACRS2013_Zylshal.pdf

Zylshal, Yulianto, F., Pasaribu, J. M. J. M., & Prasasti, I. (2015). Landuse / Landcover Extraction From Spot 6 Imagery Using Object Based Image Analysis Approach : a Case Study of Jakarta ,. In Proceedings of ACRS 2015. Quenzhon City, Metro Manila, Philippines.

Refbacks

  • There are currently no refbacks.