A graph signal filtering-based approach for detection of differentedge types on airborne lidar data,

Date

2017-09-14

Author

Bayram, Eda
Vural, Elif
Alatan, Abdullah Aydın

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Airborne Laser Scanning is a well-known remote sensing technology, which provides a dense and highly accurate, yet unorganized point cloud of earth surface. During the last decade, extracting information from the data generated by airborne LiDAR systems has been addressed by many studies in geo-spatial analysis and urban monitoring applications. However, the processing of LiDAR point clouds is challenging due to their irregular structure and 3D geometry. In this study, we propose a novel framework for the detection of the boundaries of an object or scene captured by LiDAR. Our approach is motivated by edge detection techniques in vision research and it is established on graph signal filtering which is an exciting and promising field of signal processing for irregular data types. Due to the convenient applicability of graph signal processing tools on unstructured point clouds, we achieve the detection of the edge points directly on 3D data by using a graph representation that is constructed exclusively to answer the requirements of the application. Moreover, considering the elevation data as the (graph) signal, we leverage aerial characteristic of the airborne LiDAR data. The proposed method can be employed both for discovering the jump edges on a segmentation problem and for exploring the crease edges on a LiDAR object on a reconstruction/modeling problem, by only adjusting the filter characteristics.

URI

https://hdl.handle.net/11511/81426
https://www.spiedigitallibrary.org/conference-proceedings-of-spie/10429/2279146/A-graph-signal-filtering-based-approach-for-detection-of-different/10.1117/12.2279146.short?SSO=1

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Department of Electrical and Electronics Engineering, Article

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Citation Formats

E. Bayram, E. Vural, and A. A. Alatan, “A graph signal filtering-based approach for detection of differentedge types on airborne lidar data,” 2017, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/81426.