Print Email Facebook Twitter LiDAR-guided dense matching for detecting changes and updating of buildings in Airborne LiDAR data Title LiDAR-guided dense matching for detecting changes and updating of buildings in Airborne LiDAR data Author Zhou, K. (TU Delft Optical and Laser Remote Sensing) Lindenbergh, R.C. (TU Delft Optical and Laser Remote Sensing) Gorte, Ben (University of New South Wales) Zlatanova, S. (University of New South Wales) Date 2020 Abstract Change detection is essential to keep 3D city models up-to-date. LiDAR data with high accuracy are used to create 3D city models. However, updating LiDAR data at state or nation level often takes around a decade. Very high resolution (VHR) stereo images, with often yearly updating rate and dense 3D information, provide an option for validating and updating LiDAR data. However, the 3D information in both data sources has quality problems. LiDAR point clouds are sparse and irregularly spaced, and have mixed returns near building edges, while 3D information extracted from stereo images are affected by shadow and low texture. This research proposes LiDAR-guided dense matching to address these problems explicitly for detecting accurate building changes. Data sparsity and irregular spacing is addressed by densifying LiDAR points in a form of a digital surface model (DSM). Instead of applying interpolation with associated edge problems due to mixed returns, three candidate DSMs are created by linking each DSM pixel to up to three planes as identified in segmented and triangulated LiDAR data. The candidate DSMs limit the disparity search space for dense matching, addressing low texture and shadow problems in images. Through edge-aware dense matching, the detailed building edge information in stereo pairs determine the optimal heights to address LiDAR edge problem. Changes are detected where corresponding pixels from dense matching have large color differences. Due to homogeneous surroundings and shadows, only partial changes are initially detected. A second hierarchical dense matching step is employed to complete changes and update 3D information by propagating initial partial changes iteratively. The proposed method is applied on data from two cities, Amersfoort and Assen, the Netherlands, with around 1200 existing buildings. In both areas, the method successfully verifies unchanged buildings while detecting minimum changes of 2×2×2m3. New and removed building detection in Amersfoort both have a F1 score of over 0.8, both in pixel and object evaluation, while F1 scores in Assen are over 0.9 for both categories. The experiments also show that the proposed method outperforms two well-known change detection methods in terms of verifying unchanged buildings and detecting small changes simultaneously. Subject 3D city modelBuildingChange detectionDense matchingLiDAR dataVHR images To reference this document use: http://resolver.tudelft.nl/uuid:96f3645d-7bd5-4a3b-bfa5-b58ec0f02f40 DOI https://doi.org/10.1016/j.isprsjprs.2020.02.005 Embargo date 2022-03-04 ISSN 0924-2716 Source ISPRS Journal of Photogrammetry and Remote Sensing, 162, 200-213 Bibliographical note Accepted Author Manuscript Part of collection Institutional Repository Document type journal article Rights © 2020 K. Zhou, R.C. Lindenbergh, Ben Gorte, S. Zlatanova Files PDF LiDAR_guided_dense_matchi ... R_data.pdf 9.98 MB Close viewer /islandora/object/uuid:96f3645d-7bd5-4a3b-bfa5-b58ec0f02f40/datastream/OBJ/view