Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification |
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| Institution: | 1. University of Twente, Faculty of Geo-Information Science and Earth Observation (ITC), Department of Earth Observation Science, P.O. Box 217, 7500AE Enschede, The Netherlands;2. Wuhan University, National Engineering Center for Multimedia Software, School of Computer, Hubei 430079, PR China;1. State Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, China;2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China;3. The First Institute of Oceanography, SOA, Qingdao, China;1. Department of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China;2. School of Geographical Sciences, Guangzhou University, Guangzhou 510006, China;3. Guangdong Key Laboratory for Urbanization and Geo-simulation, Guangzhou 510275, China;1. Computer Engineering College, Jimei University, Xiamen, China;2. Fujian Key Laboratory of Sensing and Computing for Smart Cities, Department of Computer Science, Xiamen University, Xiamen FJ 361005, China;3. Department of Geography & Environmental Management, University of Waterloo, Waterloo, ON N2L 3G1, Canada |
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| Abstract: | Automatic urban object detection from airborne remote sensing data is essential to process and efficiently interpret the vast amount of airborne imagery and Laserscanning (ALS) data available today. This paper combines ALS data and airborne imagery to exploit both: the good geometric quality of ALS and the spectral image information to detect the four classes buildings, trees, vegetated ground and sealed ground. A new segmentation approach is introduced which also makes use of geometric and spectral data during classification entity definition. Geometric, textural, low level and mid level image features are assigned to laser points which are quantified into voxels. The segment information is transferred to the voxels and those clusters of voxels form the entity to be classified. Two classification strategies are pursued: a supervised method, using Random Trees and an unsupervised approach, embedded in a Markov Random Field framework and using graph-cuts for energy optimization. A further contribution of this paper concerns the image-based point densification for building roofs which aims to mitigate the accuracy problems related to large ALS point spacing.Results for the ISPRS benchmark test data show that to rely on color information to separate vegetation from non-vegetation areas does mostly lead to good results, but in particular in shadow areas a confusion between classes might occur. The unsupervised classification strategy is especially sensitive in this respect. As far as the point cloud densification is concerned, we observe similar sensitivity with respect to color which makes some planes to be missed out, or false detections still remain. For planes where the densification is successful we see the expected enhancement of the outline. |
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| Keywords: | Visibility Segmentation Supervised Unsupervised Classification |
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