Politz, F.; Sester, M.: Exploring ALS and DIM data for semantic segmentation using CNNs. In: Jutzi, B.; Weinmann, M.; Hinz, S. (Eds.): ISPRS TC I Mid-term Symposium "Innovative Sensing – From Sensors to Methods and Applications". Katlenburg-Lindau : Copernicus Publications, 2018 (The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; 42-1), S. 347-354. DOI: https://doi.org/10.5194/isprs-archives-XLII-1-347-2018
Zusammenfassung: | |
Over the past years, the algorithms for dense image matching (DIM) to obtain point clouds from aerial images improved significantly. Consequently, DIM point clouds are now a good alternative to the established Airborne Laser Scanning (ALS) point clouds for remote sensing applications. In order to derive high-level applications such as digital terrain models or city models, each point within a point cloud must be assigned a class label. Usually, ALS and DIM are labelled with different classifiers due to their varying characteristics. In this work, we explore both point cloud types in a fully convolutional encoder-decoder network, which learns to classify ALS as well as DIM point clouds. As input, we project the point clouds onto a 2D image raster plane and calculate the minimal, average and maximal height values for each raster cell. The network then differentiates between the classes ground, non-ground, building and no data. We test our network in six training setups using only one point cloud type, both point clouds as well as several transfer-learning approaches. We quantitatively and qualitatively compare all results and discuss the advantages and disadvantages of all setups. The best network achieves an overall accuracy of 96% in an ALS and 83% in a DIM test set. | |
Lizenzbestimmungen: | CC BY 4.0 Unported |
Publikationstyp: | BookPart |
Publikationsstatus: | publishedVersion |
Erstveröffentlichung: | 2018 |
Die Publikation erscheint in Sammlung(en): | Fakultät für Bauingenieurwesen und Geodäsie |
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