{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T20:18:16Z","timestamp":1778012296542,"version":"3.51.4"},"reference-count":84,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,2]],"date-time":"2022-08-02T00:00:00Z","timestamp":1659398400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002341","name":"Academy of Finland projects","doi-asserted-by":"publisher","award":["319011"],"award-info":[{"award-number":["319011"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland projects","doi-asserted-by":"publisher","award":["318437"],"award-info":[{"award-number":["318437"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Henry Ford foundation","award":["319011"],"award-info":[{"award-number":["319011"]}]},{"name":"Henry Ford foundation","award":["318437"],"award-info":[{"award-number":["318437"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Autonomous vehicle perception systems typically rely on single-wavelength lidar sensors to obtain three-dimensional information about the road environment. In contrast to cameras, lidars are unaffected by challenging illumination conditions, such as low light during night-time and various bidirectional effects changing the return reflectance. However, as many commercial lidars operate on a monochromatic basis, the ability to distinguish objects based on material spectral properties is limited. In this work, we describe the prototype hardware for a hyperspectral single photon lidar and demonstrate the feasibility of its use in an autonomous-driving-related object classification task. We also introduce a simple statistical model for estimating the reflectance measurement accuracy of single photon sensitive lidar devices. The single photon receiver frame was used to receive 30 12.3 nm spectral channels in the spectral band 1200\u20131570 nm, with a maximum channel-wise intensity of 32 photons. A varying number of frames were used to accumulate the signal photon count. Multiple objects covering 10 different categories of road environment, such as car, dry asphalt, gravel road, snowy asphalt, wet asphalt, wall, granite, grass, moss, and spruce tree, were included in the experiments. We test the influence of the number of spectral channels and the number of frames on the classification accuracy with random forest classifier and find that the spectral information increases the classification accuracy in the high-photon flux regime from 50% to 94% with 2 channels and 30 channels, respectively. In the low-photon flux regime, the classification accuracy increases from 30% to 38% with 2 channels and 6 channels, respectively. Additionally, we visualize the data with the t-SNE algorithm and show that the photon shot noise in the single photon sensitive hyperspectral data contributes the most to the separability of material specific spectral signatures. The results of this study provide support for the use of hyperspectral single photon lidar data on more advanced object detection and classification methods, and motivates the development of advanced single photon sensitive hyperspectral lidar devices for use in autonomous vehicles and in robotics.<\/jats:p>","DOI":"10.3390\/s22155759","type":"journal-article","created":{"date-parts":[[2022,8,3]],"date-time":"2022-08-03T00:15:26Z","timestamp":1659485726000},"page":"5759","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Feasibility of Hyperspectral Single Photon Lidar for Robust Autonomous Vehicle Perception"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3142-731X","authenticated-orcid":false,"given":"Josef","family":"Taher","sequence":"first","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, National Land Survey of Finland, 02150 Espoo, Finland"},{"name":"Department of Computer Science, Aalto University School of Science, 02150 Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5486-4582","authenticated-orcid":false,"given":"Teemu","family":"Hakala","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, National Land Survey of Finland, 02150 Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7592-3107","authenticated-orcid":false,"given":"Anttoni","family":"Jaakkola","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, National Land Survey of Finland, 02150 Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4664-6221","authenticated-orcid":false,"given":"Heikki","family":"Hyyti","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, National Land Survey of Finland, 02150 Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3841-6533","authenticated-orcid":false,"given":"Antero","family":"Kukko","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, National Land Survey of Finland, 02150 Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1289-2811","authenticated-orcid":false,"given":"Petri","family":"Manninen","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, National Land Survey of Finland, 02150 Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6772-9611","authenticated-orcid":false,"given":"Jyri","family":"Maanp\u00e4\u00e4","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, National Land Survey of Finland, 02150 Espoo, Finland"},{"name":"Department of Computer Science, Aalto University School of Science, 02150 Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5360-4017","authenticated-orcid":false,"given":"Juha","family":"Hyypp\u00e4","sequence":"additional","affiliation":[{"name":"Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute FGI, National Land Survey of Finland, 02150 Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.trf.2014.04.009","article-title":"Intention to use a fully automated car: Attitudes and a priori acceptability","volume":"27","author":"Payre","year":"2014","journal-title":"Transp. Res. Part Traffic Psychol. Behav."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.ssci.2014.09.004","article-title":"Human\u2013machine cooperation in smart cars. An empirical investigation of the loss-of-control thesis","volume":"72","author":"Weyer","year":"2015","journal-title":"Saf. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.trpro.2015.01.002","article-title":"Automated vehicles and the rethinking of mobility and cities","volume":"5","author":"Alessandrini","year":"2015","journal-title":"Transp. Res. Procedia"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.trf.2004.02.001","article-title":"Behavioural adaptation to adaptive cruise control (ACC): Implications for preventive strategies","volume":"7","author":"Parker","year":"2004","journal-title":"Transp. Res. Part Traffic Psychol. Behav."},{"key":"ref_5","unstructured":"Shanker, R., Jonas, A., Devitt, S., Huberty, K., Flannery, S., Greene, W., Swinburne, B., Locraft, G., Wood, A., and Weiss, K. (2013). Autonomous cars: Self-driving the new auto industry paradigm. Morgan Stanley Blue Pap., 1\u2013109."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"205","DOI":"10.5194\/ars-3-205-2005","article-title":"Automotive radar and lidar systems for next generation driver assistance functions","volume":"3","author":"Rasshofer","year":"2005","journal-title":"Adv. Radio Sci."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/MSP.2020.2983772","article-title":"Advances in single-photon lidar for autonomous vehicles: Working principles, challenges, and recent advances","volume":"37","author":"Rapp","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"7021","DOI":"10.1109\/JSEN.2020.2977775","article-title":"Single-photon detectors modeling and selection criteria for high-background LiDAR","volume":"20","author":"Pasquinelli","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"6067","DOI":"10.1109\/TVT.2020.2984772","article-title":"Single-photon detection approach for autonomous vehicles sensing","volume":"69","author":"Du","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1109\/TCI.2021.3111572","article-title":"Robust and guided bayesian reconstruction of single-photon 3d lidar data: Application to multispectral and underwater imaging","volume":"7","author":"Halimi","year":"2021","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_11","first-page":"50","article-title":"Lidar for autonomous driving: The principles, challenges, and trends for automotive lidar and perception systems","volume":"37","author":"Li","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Takai, I., Matsubara, H., Soga, M., Ohta, M., Ogawa, M., and Yamashita, T. (2016). Single-photon avalanche diode with enhanced NIR-sensitivity for automotive LIDAR systems. Sensors, 16.","DOI":"10.3390\/s16040459"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Powers, M.A., and Davis, C.C. (2010, January 5\u20139). Spectral LADAR: Towards active 3D multispectral imaging. Proceedings of the Laser Radar Technology and Applications XV, International Society for Optics and Photonics, Saint Petersburg, Russia.","DOI":"10.1117\/12.850599"},{"key":"ref_14","unstructured":"Tabirian, A.M., Jenssen, H.P., Buchter, S., and Hoffman, H.J. (2003). Multi-Wavelengths Infrared Laser. (6,567,431), U.S. Patent."},{"key":"ref_15","unstructured":"Buchter, S.C., Ludvigsen, H.E., and Kaivola, M. (2011). Method of Generating Supercontinuum Optical Radiation, Supercontinuum Optical Radiation Source, and Use Thereof. (8,000,574), U.S. Patent."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1109\/LGRS.2006.888848","article-title":"Toward hyperspectral lidar: Measurement of spectral backscatter intensity with a supercontinuum laser source","volume":"4","author":"Kaasalainen","year":"2007","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7057","DOI":"10.3390\/s100707057","article-title":"Two-channel hyperspectral LiDAR with a supercontinuum laser source","volume":"10","author":"Chen","year":"2010","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7119","DOI":"10.1364\/OE.20.007119","article-title":"Full waveform hyperspectral LiDAR for terrestrial laser scanning","volume":"20","author":"Hakala","year":"2012","journal-title":"Opt. Express"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1109\/LGRS.2012.2232278","article-title":"Classification of spruce and pine trees using active hyperspectral LiDAR","volume":"10","author":"Vauhkonen","year":"2013","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_20","first-page":"136","article-title":"Estimation of rice leaf nitrogen contents based on hyperspectral LIDAR","volume":"44","author":"Du","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.agrformet.2014.08.018","article-title":"Fast and nondestructive method for leaf level chlorophyll estimation using hyperspectral LiDAR","volume":"198","author":"Nevalainen","year":"2014","journal-title":"Agric. For. Meteorol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"9667","DOI":"10.1109\/JSTARS.2021.3111295","article-title":"Application of Hyperspectral LiDAR on 3-D Chlorophyll-Nitrogen Mapping of Rohdea Japonica in Laboratory","volume":"14","author":"Du","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2018.04.024","article-title":"Estimating leaf chlorophyll status using hyperspectral lidar measurements by PROSPECT model inversion","volume":"212","author":"Sun","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Shao, H., Chen, Y., Yang, Z., Jiang, C., Li, W., Wu, H., Wang, S., Yang, F., Chen, J., and Puttonen, E. (2019). Feasibility study on hyperspectral LiDAR for ancient Huizhou-style architecture preservation. Remote Sens., 12.","DOI":"10.3390\/rs12010088"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1785","DOI":"10.1109\/LGRS.2018.2854358","article-title":"Feasibility study of ore classification using active hyperspectral LiDAR","volume":"15","author":"Chen","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"013105","DOI":"10.1117\/1.OE.54.1.013105","article-title":"Artificial target detection with a hyperspectral LiDAR over 26-h measurement","volume":"54","author":"Puttonen","year":"2015","journal-title":"Opt. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1016\/j.isprsjprs.2011.04.002","article-title":"Demonstration of a virtual active hyperspectral LiDAR in automated point cloud classification","volume":"66","author":"Suomalainen","year":"2011","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"24043","DOI":"10.1364\/OE.27.024043","article-title":"Hyperspectral lidar point cloud segmentation based on geometric and spectral information","volume":"27","author":"Chen","year":"2019","journal-title":"Opt. Express"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Jiang, C., Chen, Y., Wu, H., Li, W., Zhou, H., Bo, Y., Shao, H., Song, S., Puttonen, E., and Hyypp\u00e4, J. (2019). Study of a high spectral resolution hyperspectral LiDAR in vegetation red edge parameters extraction. Remote Sens., 11.","DOI":"10.3390\/rs11172007"},{"key":"ref_30","unstructured":"Evans, B.J., and Mitra, P. (2005). Multi-spectral LADAR. (6,882,409), U.S. Patent."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1109\/JSSC.2009.2021920","article-title":"Single-photon synchronous detection","volume":"44","author":"Niclass","year":"2009","journal-title":"IEEE J. Solid-State Circuits"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1364\/OPTICA.408657","article-title":"Single-photon imaging over 200 km","volume":"8","author":"Li","year":"2021","journal-title":"Optica"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"11919","DOI":"10.1364\/OE.25.011919","article-title":"Single-photon three-dimensional imaging at up to 10 km range","volume":"25","author":"Pawlikowska","year":"2017","journal-title":"Opt. Express"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"782","DOI":"10.1109\/TITS.2015.2482601","article-title":"Automotive three-dimensional vision through a single-photon counting SPAD camera","volume":"17","author":"Bronzi","year":"2015","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"083112","DOI":"10.1063\/1.2001672","article-title":"Multiple wavelength time-of-flight sensor based on time-correlated single-photon counting","volume":"76","author":"Buller","year":"2005","journal-title":"Rev. Sci. Instrum."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Altmann, Y., Maccarone, A., McCarthy, A., Buller, G., and McLaughlin, S. (September, January 29). Joint spectral clustering and range estimation for 3D scene reconstruction using multispectral Lidar waveforms. Proceedings of the 2016 24th European Signal Processing Conference (EUSIPCO), Budapest, Hungary.","DOI":"10.1109\/EUSIPCO.2016.7760301"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"658","DOI":"10.1109\/TCI.2017.2703144","article-title":"Robust spectral unmixing of sparse multispectral lidar waveforms using gamma Markov random fields","volume":"3","author":"Altmann","year":"2017","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.isprsjprs.2020.04.021","article-title":"Combining single photon and multispectral airborne laser scanning for land cover classification","volume":"164","author":"Matikainen","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Morsy, S., Shaker, A., and El-Rabbany, A. (2018). Using multispectral airborne LiDAR data for land\/water discrimination: A case study at Lake Ontario, Canada. Appl. Sci., 8.","DOI":"10.3390\/app8030349"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"4942","DOI":"10.1109\/TGRS.2013.2285942","article-title":"Design and evaluation of multispectral lidar for the recovery of arboreal parameters","volume":"52","author":"Wallace","year":"2013","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","unstructured":"Johnson, K., Vaidyanathan, M., Xue, S., Tennant, W.E., Kozlowski, L.J., Hughes, G.W., and Smith, D.D. (2001, January 17\u201319). Adaptive LaDAR receiver for multispectral imaging. Proceedings of the Laser Radar Technology and Applications VI, SPIE, Orlando, FL, USA."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1364\/OE.24.001873","article-title":"Computational multi-depth single-photon imaging","volume":"24","author":"Shin","year":"2016","journal-title":"Opt. Express"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1109\/TCI.2019.2945204","article-title":"Bayesian 3D reconstruction of subsampled multispectral single-photon Lidar signals","volume":"6","author":"Tachella","year":"2019","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41467-019-12943-7","article-title":"Real-time 3D reconstruction from single-photon lidar data using plug-and-play point cloud denoisers","volume":"10","author":"Tachella","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"A468","DOI":"10.1364\/OE.27.00A468","article-title":"Portable hyperspectral lidar utilizing 5 GHz multichannel full waveform digitization","volume":"27","author":"Kaasalainen","year":"2019","journal-title":"Opt. Express"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chen, Y., Li, W., Hyypp\u00e4, J., Wang, N., Jiang, C., Meng, F., Tang, L., Puttonen, E., and Li, C. (2019). A 10-nm spectral resolution hyperspectral LiDAR system based on an acousto-optic tunable filter. Sensors, 19.","DOI":"10.3390\/s19071620"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"30146","DOI":"10.1364\/OE.26.030146","article-title":"Wavelength-time coding for multispectral 3D imaging using single-photon LiDAR","volume":"26","author":"Ren","year":"2018","journal-title":"Opt. Express"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"e202000505","DOI":"10.1002\/jbio.202000505","article-title":"Simultaneous multi-spectral, single-photon fluorescence imaging using a plasmonic colour filter array","volume":"14","author":"Connolly","year":"2021","journal-title":"J. Biophotonics"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTQE.2018.2867439","article-title":"A 512\u00d7 512 SPAD image sensor with integrated gating for widefield FLIM","volume":"25","author":"Ulku","year":"2018","journal-title":"IEEE J. Sel. Top. Quantum Electron."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1364\/OPTICA.386574","article-title":"Megapixel time-gated SPAD image sensor for 2D and 3D imaging applications","volume":"7","author":"Morimoto","year":"2020","journal-title":"Optica"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Fox, M. (2006). Quantum Optics: An Introduction, Oxford University Press.","DOI":"10.1093\/oso\/9780198566724.001.0001"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/TCI.2015.2453093","article-title":"Photon-efficient computational 3-D and reflectivity imaging with single-photon detectors","volume":"1","author":"Shin","year":"2015","journal-title":"IEEE Trans. Comput. Imaging"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1109\/TIP.2011.2179306","article-title":"Bits from photons: Oversampled image acquisition using binary poisson statistics","volume":"21","author":"Yang","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Buchner, A., Hadrath, S., Burkard, R., Kolb, F.M., Ruskowski, J., Ligges, M., and Grabmaier, A. (2021). Analytical Evaluation of Signal-to-Noise Ratios for Avalanche-and Single-Photon Avalanche Diodes. Sensors, 21.","DOI":"10.3390\/s21082887"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"5113","DOI":"10.1002\/sim.8354","article-title":"A more intuitive and modern way to compute a small-sample confidence interval for the mean of a Poisson distribution","volume":"38","author":"Hanley","year":"2019","journal-title":"Stat. Med."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1093\/treephys\/tpn022","article-title":"Estimating forest LAI profiles and structural parameters using a ground-based laser called \u2018Echidna\u00ae","volume":"29","author":"Jupp","year":"2009","journal-title":"Tree Physiol."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Okhrimenko, M., Coburn, C., and Hopkinson, C. (2019). Multi-spectral lidar: Radiometric calibration, canopy spectral reflectance, and vegetation vertical SVI profiles. Remote Sens., 11.","DOI":"10.3390\/rs11131556"},{"key":"ref_58","first-page":"21","article-title":"Long-range 3D single-photon imaging lidar system","volume":"Volume 9250","author":"Pawlikowska","year":"2014","journal-title":"Proceedings of the Electro-Optical Remote Sensing, Photonic Technologies, and Applications VIII; and Military Applications in Hyperspectral Imaging and High Spatial Resolution Sensing II"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"100057","DOI":"10.1016\/j.array.2021.100057","article-title":"Deep learning for object detection and scene perception in self-driving cars: Survey, challenges, and open issues","volume":"10","author":"Gupta","year":"2021","journal-title":"Array"},{"key":"ref_60","unstructured":"Van der Maaten, L., and Hinton, G. (2008). Visualizing data using t-SNE. J. Mach. Learn. Res., 9."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Wattenberg, M., Vi\u00e9gas, F., and Johnson, I. (2016). How to Use t-SNE Effectively. Distill.","DOI":"10.23915\/distill.00002"},{"key":"ref_62","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1109\/ICDAR.1995.598994","article-title":"Random decision forests","volume":"Volume 1","author":"Ho","year":"1995","journal-title":"Proceedings of the Proceedings of 3rd International Conference on Document Analysis and Recognition"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Oshiro, T.M., Perez, P.S., and Baranauskas, J.A. (2012, January 13\u201320). How many trees in a random forest?. Proceedings of the International Workshop on Machine Learning and Data Mining in Pattern Recognition, Berlin, Germany.","DOI":"10.1007\/978-3-642-31537-4_13"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"4765","DOI":"10.1109\/JLT.2020.2994654","article-title":"Counting statistics of actively quenched SPADs under continuous illumination","volume":"38","author":"Straka","year":"2020","journal-title":"J. Light. Technol."},{"key":"ref_66","unstructured":"Kindt, W., Van Zeijl, H., and Middelhoek, S. (1998, January 8\u201310). Optical cross talk in geiger mode avalanche photodiode arrays: Modeling, prevention and measurement. Proceedings of the 28th European Solid-State Device Research Conference, Bordeaux, France."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"8381","DOI":"10.1364\/OE.16.008381","article-title":"Optical crosstalk in single photon avalanche diode arrays: A new complete model","volume":"16","author":"Rech","year":"2008","journal-title":"Opt. Express"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Xu, H., Braga, L.H., Stoppa, D., and Pancheri, L. (2015, January 3\u20135). Characterization of single-photon avalanche diode arrays in 150nm CMOS technology. Proceedings of the 2015 XVIII AISEM Annual Conference, Trento, Italy.","DOI":"10.1109\/AISEM.2015.7066818"},{"key":"ref_69","first-page":"618001","article-title":"Silicon photon counting detector optical cross-talk effect","volume":"Volume 6180","author":"Prochazka","year":"2006","journal-title":"Proceedings of the Photonics, Devices, and Systems III"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/ncomms14080","article-title":"Multiplexed single-mode wavelength-to-time mapping of multimode light","volume":"8","author":"Chandrasekharan","year":"2017","journal-title":"Nat. Commun."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"5163","DOI":"10.1364\/OE.19.005163","article-title":"Group-velocity-dispersion measurements of atmospheric and combustion-related gases using an ultrabroadband-laser source","volume":"19","author":"Wrzesinski","year":"2011","journal-title":"Opt. Express"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Tontini, A., Gasparini, L., and Perenzoni, M. (2020). Numerical model of spad-based direct time-of-flight flash lidar CMOS image sensors. Sensors, 20.","DOI":"10.3390\/s20185203"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Incoronato, A., Locatelli, M., and Zappa, F. (2021, January 21\u201325). Statistical Model for SPAD-based Time-of-Flight systems and photons pile-up correction. Proceedings of the The European Conference on Lasers and Electro-Optics. Optical Society of America, Munich, Germany.","DOI":"10.1109\/CLEO\/Europe-EQEC52157.2021.9541645"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"44","DOI":"10.4090\/juee.2011.v5n1.044056","article-title":"Development and utilization of urban spectral library for remote sensing of urban environment","volume":"5","author":"Nasarudin","year":"2011","journal-title":"J. Urban Environ. Eng."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Wu, B., Wan, A., Yue, X., and Keutzer, K. (2018, January 21\u201325). Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud. Proceedings of the 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, Australia.","DOI":"10.1109\/ICRA.2018.8462926"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Maanp\u00e4\u00e4, J., Taher, J., Manninen, P., Pakola, L., Melekhov, I., and Hyypp\u00e4, J. (2020, January 10\u201315). Multimodal end-to-end learning for autonomous steering in adverse road and weather conditions. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9413109"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Ghallabi, F., Nashashibi, F., El-Haj-Shhade, G., and Mittet, M.A. (2018, January 4\u20137). Lidar-based lane marking detection for vehicle positioning in an hd map. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569951"},{"key":"ref_78","unstructured":"Biasutti, P., Lepetit, V., Br\u00e9dif, M., Aujol, J.F., and Bugeau, A. (2019, January 27\u201328). LU-Net: A Simple Approach to 3D LiDAR Point Cloud Semantic Segmentation. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_79","unstructured":"Ouster, I. (2022, June 01). Webinar: Introducing the L2X chip\u2014Up to 2X the Data Output to power Ouster\u2019s Most Reliable and Rugged Sensors. Available online: https:\/\/ouster.com\/resources\/webinars\/l2x-lidar-chip\/."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Villa, F., Severini, F., Madonini, F., and Zappa, F. (2021). SPADs and sipms arrays for long-range high-speed light detection and ranging (LiDAR). Sensors, 21.","DOI":"10.3390\/s21113839"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"4705","DOI":"10.1364\/AO.43.004705","article-title":"Gated viewing and high-accuracy three-dimensional laser radar","volume":"43","author":"Busck","year":"2004","journal-title":"Appl. Opt."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"116001","DOI":"10.1117\/1.2127895","article-title":"Underwater 3-D optical imaging with a gated viewing laser radar","volume":"44","author":"Busck","year":"2005","journal-title":"Opt. Eng."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"034301","DOI":"10.1117\/1.2183668","article-title":"Long-range three-dimensional imaging using range-gated laser radar images","volume":"45","author":"Andersson","year":"2006","journal-title":"Opt. Eng."},{"key":"ref_84","first-page":"29","article-title":"Linear LIDAR versus Geiger-mode LIDAR: Impact on data properties and data quality","volume":"Volume 9832","author":"Ullrich","year":"2016","journal-title":"Proceedings of the Laser Radar Technology and Applications XXI"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5759\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:01:06Z","timestamp":1760140866000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/15\/5759"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,8,2]]},"references-count":84,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2022,8]]}},"alternative-id":["s22155759"],"URL":"https:\/\/doi.org\/10.3390\/s22155759","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,8,2]]}}}