The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W3, 119–122, 2017
https://doi.org/10.5194/isprs-archives-XLII-3-W3-119-2017
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W3, 119–122, 2017
https://doi.org/10.5194/isprs-archives-XLII-3-W3-119-2017

  19 Oct 2017

19 Oct 2017

FEASIBILITY OF MULTISPECTRAL AIRBORNE LASER SCANNING FOR LAND COVER CLASSIFICATION, ROAD MAPPING AND MAP UPDATING

L. Matikainen, K. Karila, J. Hyyppä, E. Puttonen, P. Litkey, and E. Ahokas L. Matikainen et al.
  • Finnish Geospatial Research Institute FGI at the National Land Survey of Finland, Centre of Excellence in Laser Scanning Research, Geodeetinrinne 2, 02430 Masala, Finland

Keywords: Laser scanning, lidar, multispectral, land cover, road, building, updating, change detection

Abstract. This article summarises our first results and experiences on the use of multispectral airborne laser scanner (ALS) data. Optech Titan multispectral ALS data over a large suburban area in Finland were acquired on three different dates in 2015–2016. We investigated the feasibility of the data from the first date for land cover classification and road mapping. Object-based analyses with segmentation and random forests classification were used. The potential of the data for change detection of buildings and roads was also demonstrated. The overall accuracy of land cover classification results with six classes was 96 % compared with validation points. The data also showed high potential for road detection, road surface classification and change detection. The multispectral intensity information appeared to be very important for automated classifications. Compared to passive aerial images, the intensity images have interesting advantages, such as the lack of shadows. Currently, we focus on analyses and applications with the multitemporal multispectral data. Important questions include, for example, the potential and challenges of the multitemporal data for change detection.