Fusion of airborne laserscanning point clouds and images for supervised and unsupervised scene classification

Markus Gerke, Jing Xiao

Research output: Contribution to journalArticleAcademicpeer-review

105 Citations (Scopus)
7 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)78-92
JournalISPRS journal of photogrammetry and remote sensing
Publication statusPublished - 2013


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