Abstract
In Airborne Light Detection and Ranging (LiDAR) data acquisition practice, discrepancies exist between adjacent strips even though careful system calibrations have been performed. A strip adjustment method using planar features acquired by the Minimum Hausdorff Distance (MHD) is proposed to eliminate these discrepancies. First, semi-suppressed fuzzy C-means and restricted region growing algorithms are used to extract buildings. Second, a binary image is generated from the minimum bounding rectangle that covers overlapping regions. Then, connected components labeling algorithm is applied to process the binary image to extract individual buildings. After that, building matching is performed based on MHD. Third, a coarse-to-fine approach is used to segment building roof planes. Then, plane matching is conducted under the constraints of MHD and normal vectors similarity. The last step is the calculation of the parameters based on Euclidean distance minimization between matched planes. Two different types of datasets, one of which was acquired by a dual-channel LiDAR system Trimble AX80, were selected to verify the proposed method. Experimental results show that the corresponding planar features that meet adjustment requirements can be successfully detected without any manual operations or auxiliary data or transformation of raw data, while the discrepancies between strips can be effectively eliminated. Although adjustment results of the proposed method slightly outperform the comparison alternative, the proposed method also has the advantage of processing the adjustment in a more automatic manner than the comparison method.
Highlights
Airborne Light Detection and Ranging (LiDAR) technology has been becoming an indispensable tool regarding three-dimensional (3D) geospatial data acquisition for urban applications [1], such as road detection [2], building extraction [3] and 3D reconstruction [4], population estimation [5], 3D change detection [6], assessment of post-disaster building damage [7], and many others [8].In practice, LiDAR data are collected by parallel flight strips where the region of a single strip is generally much less than the entire region being surveyed
LiDAR and the other which was acquired by a dual-channel system, were selected to validate the proposed method, which was implemented by C++ and the results derived from it were displayed by LiDAR_Suite, an airborne LiDAR data processing software developed by the Research and Development (R&D) group of the authors
Pixel of the Building segmentation is one of the key steps in the proposed method. It is based the Building segmentation is one of the key steps in the proposed method. It is based on the Building segmentation isof one ofofBinary the key steps ininthe the proposed method
Summary
Airborne LiDAR technology has been becoming an indispensable tool regarding three-dimensional (3D) geospatial data acquisition for urban applications [1], such as road detection [2], building extraction [3] and 3D reconstruction [4], population estimation [5], 3D change detection [6], assessment of post-disaster building damage [7], and many others [8].In practice, LiDAR data are collected by parallel flight strips where the region of a single strip is generally much less than the entire region being surveyed. Multiple strips are required and should be stitched together so that the whole region can be covered. This operating fashion is very similar to the conventional aero-survey by photogrammetric technique, and partial overlapping. Sensors 2019, 19, x FOR PEER REVIEW between adjacentstrips stripsisisrequired requiredtoto mosaic data from multiple strips an integrated dataset. Between adjacent mosaic data from multiple strips intointo an integrated dataset. The. The lateral overlap can vary from to. 30%, depending on the geomorphological characteristics lateral overlap can vary from 10% to 30%, depending on the geomorphological characteristics of the of the region being surveyed [9,10].
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