Vision-Aided Hyperspectral Full-Waveform LiDAR System to Improve Detection Efficiency

The hyperspectral full-waveform LiDAR (HSL) system based on the supercontinuum laser can obtain spatial and spectral information of the target synchronously and outperform traditional LiDAR or imaging spectrometers in target classification and other applications. However, low detection efficiency caused by the detection of useless background points (ULBG) hinders its practical applications, especially when the target is small compared with the large field of view (FOV) of the HSL system. A novel vision-aided hyperspectral full-waveform LiDAR system (V-HSL) was proposed to solve the problem and improve detection efficiency. First, we established the framework and developed preliminary algorithms for the V-HSL system. Next, we experimentally compared the performance of the V-HSL system with the HSL system. The results revealed that the proposed V-HSL system could reduce the detection of ULBG points and improve detection efficiency with enhanced detection performance. The V-HSL system is a promising development direction, and the study results will help researchers and engineers develop and optimize their design of the HSL system and ensure high detection efficiency of spatial and spectral information of the target.

[1]  Zhenxin Zhang,et al.  3D LiDAR and multi-technology collaboration for preservation of built heritage in China: A review , 2023, Int. J. Appl. Earth Obs. Geoinformation.

[2]  Dongfeng Shi,et al.  Scanning ghost imaging , 2022, Optics Express.

[3]  Hongkai Zhao,et al.  Shipborne oceanic high-spectral-resolution lidar for accurate estimation of seawater depth-resolved optical properties , 2022, Light: Science & Applications.

[4]  D. Tang,et al.  Fringe projection profilometry method with high efficiency, precision, and convenience: theoretical analysis and development. , 2022, Optics express.

[5]  Yuhuan Ren,et al.  Automatic Extraction of Damaged Houses by Earthquake Based on Improved YOLOv5: A Case Study in Yangbi , 2022, Remote. Sens..

[6]  Ping Liu,et al.  A review of deep learning used in the hyperspectral image analysis for agriculture , 2021, Artificial Intelligence Review.

[7]  Dong Liu,et al.  Optical system design for a hyperspectral imaging lidar using supercontinuum laser and its preliminary performance. , 2021, Optics express.

[8]  Zhenzhu Wang,et al.  Optical properties and seasonal distribution of aerosol layers observed by lidar over Jinhua, southeast China , 2021, Atmospheric Environment.

[9]  Juha Hyyppä,et al.  Analysis and Radiometric Calibration for Backscatter Intensity of Hyperspectral LiDAR Caused by Incident Angle Effect , 2021, Sensors.

[10]  Doug,et al.  ultralytics/yolov5: v5.0 - YOLOv5-P6 1280 models, AWS, Supervise.ly and YouTube integrations , 2021 .

[11]  Jia Sun,et al.  Using HSI Color Space to Improve the Multispectral Lidar Classification Error Caused by Measurement Geometry , 2021, IEEE Transactions on Geoscience and Remote Sensing.

[12]  W. Gong,et al.  Analyzing the effect of incident angle on echo intensity acquired by hyperspectral lidar based on the Lambert-Beckman model. , 2021, Optics express.

[13]  Shuai Gao,et al.  Estimating Vertical Chlorophyll Concentrations in Maize in Different Health States Using Hyperspectral LiDAR , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Xiaofei Wang,et al.  Overview of Hyperspectral Image Classification , 2020, J. Sensors.

[15]  S. Kaasalainen,et al.  Potential of active multispectral lidar for detecting low reflectance targets. , 2020, Optics express.

[16]  Sanna Kaasalainen,et al.  Improved waveform reconstruction and parameter accuracy retrieval for hyperspectral lidar data. , 2019, Applied optics.

[17]  Santiago Royo,et al.  An Overview of Lidar Imaging Systems for Autonomous Vehicles , 2019, Applied Sciences.

[18]  Wei Gong,et al.  Hyperspectral lidar point cloud segmentation based on geometric and spectral information. , 2019, Optics express.

[19]  Zheng Niu,et al.  Estimating leaf chlorophyll and nitrogen contents using active hyperspectral LiDAR and partial least square regression method , 2019, Journal of Applied Remote Sensing.

[20]  Ning Wang,et al.  A 10-nm Spectral Resolution Hyperspectral LiDAR System Based on an Acousto-Optic Tunable Filter , 2019, Sensors.

[21]  Wei Gong,et al.  Wavelength selection of the multispectral lidar system for estimating leaf chlorophyll and water contents through the PROSPECT model , 2019, Agricultural and Forest Meteorology.

[22]  Wei Gong,et al.  A new waveform decomposition method for multispectral LiDAR , 2019, ISPRS Journal of Photogrammetry and Remote Sensing.

[23]  Yazhe Tang,et al.  Vision-Aided Multi-UAV Autonomous Flocking in GPS-Denied Environment , 2019, IEEE Transactions on Industrial Electronics.

[24]  Zhijie Wen,et al.  Feasibility Study of Ore Classification Using Active Hyperspectral LiDAR , 2018, IEEE Geoscience and Remote Sensing Letters.

[25]  Giorgos Mountrakis,et al.  A linearly approximated iterative Gaussian decomposition method for waveform LiDAR processing , 2017 .

[26]  Lin Du,et al.  Multispectral LiDAR Point Cloud Classification: A Two-Step Approach , 2017, Remote. Sens..

[27]  Mingquan Wu,et al.  Deriving backscatter reflective factors from 32-channel full-waveform LiDAR data for the estimation of leaf biochemical contents. , 2016, Optics express.

[28]  Bo Zhu,et al.  Investigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification , 2015, Sensors.

[29]  Fan Zhang,et al.  Intensity Correction of Terrestrial Laser Scanning Data by Estimating Laser Transmission Function , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[30]  Sebastian Bauer,et al.  Spectral and geometric aspects of mineral identification by means of hyperspectral fluorescence imaging , 2015 .

[31]  Yongxiang Hu,et al.  Ocean subsurface studies with the CALIPSO spaceborne lidar , 2014 .

[32]  D. Roberts,et al.  Urban tree species mapping using hyperspectral and lidar data fusion , 2014 .

[33]  Kyungeun Cho,et al.  Calibration between Color Camera and 3D LIDAR Instruments with a Polygonal Planar Board , 2014, Sensors.

[34]  Rachel Gaulton,et al.  The potential of dual-wavelength laser scanning for estimating vegetation moisture content , 2013 .

[35]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[36]  Michael A. Powers,et al.  Spectral LADAR: active range-resolved three-dimensional imaging spectroscopy. , 2012, Applied optics.

[37]  Gong Wei,et al.  Multi-wavelength canopy LiDAR for remote sensing of vegetation: Design and system performance , 2012 .

[38]  J. Suomalainen,et al.  Full waveform hyperspectral LiDAR for terrestrial laser scanning. , 2012, Optics express.

[39]  Teemu Hakala,et al.  Demonstration of a virtual active hyperspectral LiDAR in automated point cloud classification , 2011 .

[40]  Yuwei Chen,et al.  Two-channel Hyperspectral LiDAR with a Supercontinuum Laser Source , 2010, Sensors.

[41]  Lorenzo Bruzzone,et al.  Fusion of Hyperspectral and LIDAR Remote Sensing Data for Classification of Complex Forest Areas , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[42]  W. Wagner,et al.  Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner , 2006 .

[43]  Patrick L. Thompson,et al.  CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) on MRO (Mars Reconnaissance Orbiter) , 2004, SPIE Asia-Pacific Remote Sensing.

[44]  Ram M. Narayanan,et al.  A multiwavelength airborne polarimetric lidar for vegetation remote sensing: instrumentation and preliminary test results , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[45]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..