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Application of LiDAR Remote Sensing and Mapping

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 21 February 2025 | Viewed by 801

Special Issue Editors

Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: LiDAR remote sensing; point cloud processing; forest mapping and monitoring; LiDAR applications in forestry and ecology
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing 100029, China
Interests: data analysis statistical analysis multiple linear regression lidar remote sensing geology
Laboratory of Digital Earth Sciences, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Interests: lidar remote sensing; vegetation structure; ICESat; forest mapping; biomass estimation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

LiDAR (Light detection and ranging) technology has revolutionized the field of remote sensing and mapping by providing high-resolution, three-dimensional data of the Earth's surface. Its applications span various domains, including forestry, agriculture, urban planning, geology, and environmental monitoring. This Special Issue aims to gather cutting-edge research and advancements in the application of LiDAR remote sensing and mapping. Researchers and practitioners are invited to submit original research articles, comprehensive reviews, and detailed case studies that highlight the transformative impact of LiDAR technology. 

Dr. Sheng Nie
Dr. Xiaoxiao Zhu
Dr. Cheng Wang
Guest Editors

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Keywords

  • LiDAR remote sensing
  • point cloud processing
  • LiDAR applications in forestry and ecology
  • forest height and mapping
  • 3D scene reconstruction
  • space-borne LiDAR
  • building height retrieval and mapping

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Published Papers (1 paper)

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Research

15 pages, 2064 KiB  
Article
Research on the Depth Image Reconstruction Algorithm Using the Two-Dimensional Kaniadakis Entropy Threshold
by Xianhui Yang, Jianfeng Sun, Le Ma, Xin Zhou, Wei Lu and Sining Li
Sensors 2024, 24(18), 5950; https://doi.org/10.3390/s24185950 - 13 Sep 2024
Viewed by 553
Abstract
The photon-counting light laser detection and ranging (LiDAR), especially the Geiger mode avalanche photon diode (Gm-APD) LiDAR, can obtain three-dimensional images of the scene, with the characteristics of single-photon sensitivity, but the background noise limits the imaging quality of the laser radar. In [...] Read more.
The photon-counting light laser detection and ranging (LiDAR), especially the Geiger mode avalanche photon diode (Gm-APD) LiDAR, can obtain three-dimensional images of the scene, with the characteristics of single-photon sensitivity, but the background noise limits the imaging quality of the laser radar. In order to solve this problem, a depth image estimation method based on a two-dimensional (2D) Kaniadakis entropy thresholding method is proposed which transforms a weak signal extraction problem into a denoising problem for point cloud data. The characteristics of signal peak aggregation in the data and the spatio-temporal correlation features between target image elements in the point cloud-intensity data are exploited. Through adequate simulations and outdoor target-imaging experiments under different signal-to-background ratios (SBRs), the effectiveness of the method under low signal-to-background ratio conditions is demonstrated. When the SBR is 0.025, the proposed method reaches a target recovery rate of 91.7%, which is better than the existing typical methods, such as the Peak-picking method, Cross-Correlation method, and the sparse Poisson intensity reconstruction algorithm (SPIRAL), which achieve a target recovery rate of 15.7%, 7.0%, and 18.4%, respectively. Additionally, comparing with the SPIRAL, the reconstruction recovery ratio is improved by 73.3%. The proposed method greatly improves the integrity of the target under high-background-noise environments and finally provides a basis for feature extraction and target recognition. Full article
(This article belongs to the Special Issue Application of LiDAR Remote Sensing and Mapping)
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