Impact of optimization of ALS point cloud on classification

• Autorzy: Błaszczak-Bąk W. , Sobieraj A.
• Języki publikacji: EN

Abstrakty

EN

Airborne laser scanning (ALS) is one of the LIDAR technologies (Light Detection and Ranging). It provides information about the terrain in form of a point cloud. During measurement is acquired: spatial data (object’s coordinates X, Y, Z) and collateral data such as intensity of reflected signal. The obtained point cloud is typically applied for generating a digital terrain model (DTM) and a digital surface model (DSM). For DTM and DSM generation it is necessary to apply filtration or classification algorithms. They allow to divide a point cloud into object groups (e.g.: terrain points, vegetation, etc.). In this study classification is conducted with one extra parameter–intensity. The obtained point groups were used for digital spatial model generation. Classification is a time and work consuming process, therefore there is a need to reduce the time of ALS point cloud processing. Optimization algorithm enables to decrease the number of points in a dataset. In this study the main goal was to test the impact of optimization on the results of a classification. Studies were conducted in two variants. Variant 1 includes classification of the original point cloud where points are divided in the groups: roofs, asphalt road, tree/bushes, grass. On variant 2 before classification, an optimization algorithm was performed in the original point cloud. Obtained from these two variants object groups were used to generate a spatial model, which was then statistically analyzed.

Słowa kluczowe

EN

optimization; classification; intensity

• Wydawca: Wydawnictwo Uniwersytetu Warmińsko-Mazurskiego w Olsztynie
• Czasopismo: Technical Sciences / University of Warmia and Mazury in Olsztyn
• Rocznik: 2013
• Tom: nr 16(2)
• Strony: 147--164
• Opis fizyczny: Bibliogr. 30 poz., rys., tab.

Twórcy

autor

Błaszczak-Bąk W.

• Institute of Geodesy, University of Warmia and Mazury in Olsztyn
• Chair of Geodesy Gdansk University of Technology

autor

Sobieraj A.

• Chair of Geodesy Gdansk University of Technology

Bibliografia

• ANTONORAKIS A.S., RICHARDS K.S., BRASINGTON J. 2008. Object-based land cover classification using airborne LiDAR. Remote Sensing of Environment, 112: 2988–2998.
• AXELSSON P. 2000. DEM generation from laser scanner data using adaptive TIN models. International Archives of Photogrammetry and Remote Sensing, XXXIII/4B.
• BŁASZCZAK W. 2006 Optimization of large measurement results sets for building data base of spatial information system. Doctors thesis. University of Warmia and Mazury in Olsztyn.
• BŁASZCZAK-BĄK W., JANOWSKI A., KAMIŃSKI W., RAPIŃSKI J. 2010a. Modification of Lidar Point Cloud Processing Methodology. FIG Congress. Facing the Challenges – Building the Capacity Sydney, Australia, 11–16 April.
• BŁASZCZAK-BĄK W., JANOWSKI A., KAMIŃSKI W., RAPIŃSKI J. 2010a. Modyfied methodology for analyzing ALS observations. Reports on Geodesy, pp. 7–17.
• BŁASZCZAK-BĄK W., JANOWSKI A., KAMIŃSKI W., RAPIŃSKI J. 2010b. Proposition of modification of aerial laser survey point cloud processing methodology. Archives of Geomatics “New technology and instruments in survey”, pp. 7–17.
• BŁASZCZAK-BĄK W., JANOWSKI A., KAMIŃSKI W., RAPIŃSKI J. 2011a. Optimization algorithm and filtration using the adaptive TIN model at the stage of initial processing of the ALS point cloud. Canadian Journal of Remote Sensing, 37(6): 583–589.
• BORKOWSKI A., JÓŹKÓW G. 2007. Correctness evaluation of the flakes based filtering method of airborne laser scanning data. Archives of Photogrammetry, Cartography and Remote Sensing, 17a.
• CHARANIYA A., MANDUCHI R., LODHA S. 2004. Supervised parametric classification of aerial lidar data. Proceedings of the Conference on Computer Vision and Pattern Recognition Workshop (CVPRW’04), 3: 30.
• CSANYI N., TOTH C., GREJNER-BRZEZINSKA D., RAY J. 2005. Improving LiDAR Data Accuracy Using LiDAR Specific Ground Targets. ASPRS Annual Conference, Baltimore, MD, March 7–11, CD-ROM.
• ELMQVIST M. 2002. Ground surface estimation from airborne laser scanner data using active shape models. ISPRS, Commission III, Symposium Photogrammetric Computer Vision, September 9–13, Graz, pp. 114–118.
• GREINER-BRZEZINSKA D., TOTH C., PASKA E. 2004 Airborne remote sensing supporting traffic flow estimates. Proc. of 3rd International Symposium on Mobile Mapping Technology, Kunming, C 29–31, CD-ROM.
• HANSEN W., VÖGTLE T. 1999. Extraktion der Geländeoberfläche aus flugzeuggetragenen Laserscanner-Aufnahmen. Photogrammetrie Fernerkkundung Geoinformation, pp. 229–236.
• HEJMANOWSKA B., DRZEWIECKI W., KULESZA Ł. 2008. The quality of Digital Terrain Models. Archives of Photogrammetry and Remote Sensing, 18: 163–175.
• IZDEBSKI W. 2008. Wykłady z przedmiotu SIT/ MAPA zasadnicza. Politechnika Warszawska.
• KASS M., WITKIN A., TERZOPOULOS D. 1988, Snakes: Active contour models. International Journal of Computer Vision, 1(4): 321–331.
• KATZENBEISSER R. 2003. Toposys gmbh technical note, on line: http://www.toposys.de/pdfext/.
• KRAUS K., PFEIFER N. 1998. Determination of terrain models in wooded areas with airborne laser scanner. ISPRS Journal of Photogrammetry and Remote Sensing, 53: 193–203.
• OKSANEN J., SARJAKOSKI T. 2005. Error propagation of DEM-based surface derivatives. Computers and Geosciences, 31: 1015–1027.
• ROGGERO M. 2002. Object segmentation with region growing and principal component analysis. International Archives of Photogrammetry and Remote Sensing, XXXIV/3A.
• SCHUT G.H. 1976. Review of interpolation methods for digital terrain models. XIIth Congress of the International Society for Photogrammetry, Helsinki.
• SITHOLE G., VOSSELMAN G. 2004. Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 59(1–2): 85–101.
• SITHOLE G., VOSSELMAN G. 2005. Filtering of airborne laser scanner data based on segmented point clouds. Geo-Information Science, 66–71.
• SONG J., HAN S., KIM Y. 2002. Assessing the possibility of land-cover classification using lidar intensity data. International Archives of Photogrammetry and Remote Sensing, Graz, XXXIV/3B: 259–262.
• TOTH C., GREJNER-BRZEZINSKA D. 2004. Vehicle classification from LiDAR data to support traffic flow estimates. Proc. of 3rd International Symposium on Mobile Mapping Technology, Kunming, C 29–31, CD-ROM.
• TOVARI D., PFEIFER N. 2005. Segmentation based robust interpolation-a new approach to laser data filtering. IAPRSSIS, 363W19: 79–84.
• VISVALINGAM M., WHYATT J.D. 1992. Line generalization by repeated elimination of point. Cartographic Information Systems Research Group, University of Hull.
• VOSSELMAN G., MAAS H.G. 2001. Adjustment and filtering of raw laser altimetry data. Workshop on Airborne Laserscanning and Interferometric SAR.
• WANG C., LU Y. 2009. Potential of ILRIS3D intensity data for planar surfaces segmentation. Sensors, 9: 5770–5782.
• ZHANG K., CHEN S., WHITMANN D., SHYU M., YAN J., ZHANG C. 2002. A progressive morphological filter for removing non-ground measurements from airborne LIDAR data. Journal of Latex Class Files, 1(8).