{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T23:36:09Z","timestamp":1768347369351,"version":"3.49.0"},"reference-count":30,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,2,18]],"date-time":"2023-02-18T00:00:00Z","timestamp":1676678400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62192774"],"award-info":[{"award-number":["62192774"]}]},{"name":"National Natural Science Foundation of China","award":["2012YQ040164"],"award-info":[{"award-number":["2012YQ040164"]}]},{"DOI":"10.13039\/501100012149","name":"National Key Scientific Instrument and Equipment Development Projects of China","doi-asserted-by":"publisher","award":["62192774"],"award-info":[{"award-number":["62192774"]}],"id":[{"id":"10.13039\/501100012149","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012149","name":"National Key Scientific Instrument and Equipment Development Projects of China","doi-asserted-by":"publisher","award":["2012YQ040164"],"award-info":[{"award-number":["2012YQ040164"]}],"id":[{"id":"10.13039\/501100012149","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, a ground target extraction system for a novel LiDAR, airborne streak tube imaging LiDAR (ASTIL), is proposed. This system depends on only a single echo and a single data source, and can achieve fast ground target extraction. This system consists of two modules: Autofocus SSD (Single Shot MultiBox Detector) and post-processing. The Autofocus SSD proposed in this paper is used for object detection in the ASTIL echo signal, and its prediction speed exceeds that of the original SSD by a factor of three. In the post-processing module, we describe in detail how the echoes are processed into point clouds. The system was tested on a test set, and it can be seen from a visual perspective that satisfactory results were obtained for the extraction of buildings and trees. The system mAPIoU=0.5 is 0.812, and the FPS is greater than 34. The results prove that this ASTIL processing system can achieve fast ground target extraction based on a single echo and a single data source.<\/jats:p>","DOI":"10.3390\/rs15041128","type":"journal-article","created":{"date-parts":[[2023,2,20]],"date-time":"2023-02-20T01:36:37Z","timestamp":1676856997000},"page":"1128","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Airborne Streak Tube Imaging LiDAR Processing System: A Single Echo Fast Target Extraction Implementation"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3189-097X","authenticated-orcid":false,"given":"Yongji","family":"Yan","sequence":"first","affiliation":[{"name":"National Key Laboratory of Science and Technology on Tunable Laser, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Hongyuan","family":"Wang","sequence":"additional","affiliation":[{"name":"Research Center for Space Optical Engineering, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Boyi","family":"Song","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Tunable Laser, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Zhaodong","family":"Chen","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Tunable Laser, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Rongwei","family":"Fan","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Tunable Laser, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Deying","family":"Chen","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Tunable Laser, Harbin Institute of Technology, Harbin 150001, China"}]},{"given":"Zhiwei","family":"Dong","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Tunable Laser, Harbin Institute of Technology, Harbin 150001, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,2,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4939","DOI":"10.1109\/TGRS.2020.2969024","article-title":"Classification of Hyperspectral and LiDAR Data Using Coupled CNNs","volume":"58","author":"Hang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7355","DOI":"10.1109\/TGRS.2020.2982064","article-title":"Joint Classification of Hyperspectral and LiDAR Data Using Hierarchical Random Walk and Deep CNN Architecture","volume":"58","author":"Zhao","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhou, R.Q., and Jiang, W.S. (2020). A Ridgeline-Based Terrain Co-registration for Satellite LiDAR Point Clouds in Rough Areas. Remote Sens., 12.","DOI":"10.3390\/rs12132163"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Huang, J., Stoter, J., Peters, R., and Nan, L.L. (2022). City3D: Large-Scale Building Reconstruction from Airborne LiDAR Point Clouds. Remote Sens., 14.","DOI":"10.3390\/rs14092254"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Liu, X.J., Ning, X.G., Wang, H., Wang, C.G., Zhang, H.C., and Meng, J. (2019). A Rapid and Automated Urban Boundary Extraction Method Based on Nighttime Light Data in China. Remote Sens., 11.","DOI":"10.3390\/rs11091126"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100","DOI":"10.3832\/ifor0562-004","article-title":"Analysis of full-waveform LiDAR data for forestry applications: A review of investigations and methods","volume":"4","author":"Pirotti","year":"2011","journal-title":"iForest"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, X., Chen, W.Y., Sanesi, G., and Lafortezza, R. (2019). Remote Sensing in Urban Forestry: Recent Applications and Future Directions. Remote Sens., 11.","DOI":"10.3390\/rs11101144"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Guo, B., Li, Q.Q., Huang, X.F., and Wang, C.S. (2016). An Improved Method for Power-Line Reconstruction from Point Cloud Data. Remote Sens., 8.","DOI":"10.3390\/rs8010036"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"15605","DOI":"10.3390\/rs71115605","article-title":"Automatic Object Extraction from Electrical Substation Point Clouds","volume":"7","author":"Arastounia","year":"2015","journal-title":"Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"105694","DOI":"10.1016\/j.ocecoaman.2021.105694","article-title":"Towards the adaptability of coastal resilience: Vulnerability analysis of underground gas pipeline system after hurricanes using LiDAR data","volume":"209","author":"Huang","year":"2021","journal-title":"Ocean Coast. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, Q.R., Ruan, C.Q., Guo, J.T., Li, J., Lian, X.H., Yin, Z.H., Fu, D., and Zhong, S. (2020). Storm Surge Hazard Assessment of the Levee of a Rapidly Developing City-Based on LiDAR and Numerical Models. Remote Sens., 12.","DOI":"10.3390\/rs12223723"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.isprsjprs.2015.05.012","article-title":"Fusion of waveform LiDAR data and hyperspectral imagery for land cover classification","volume":"108","author":"Wang","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.isprsjprs.2012.09.009","article-title":"LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy","volume":"74","author":"Singh","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/MGRS.2019.2927260","article-title":"Urban Impervious Surface Detection From Remote Sensing Images A review of the methods and challenges","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nevis, A.J. (2003, January 11). Automated processing for Streak Tube Imaging Lidar data. Proceedings of the Society of Photo-Optical Instrumentation Engineers, Orlando, FL, USA.","DOI":"10.1117\/12.501566"},{"key":"ref_16","first-page":"110","article-title":"DEM Generation from Laser Scanner Data Using Adaptive TIN Models","volume":"33","author":"Axelsson","year":"2000","journal-title":"Int. Arch. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"16509","DOI":"10.1117\/1.JRS.15.016509","article-title":"Ground target extraction using airborne streak tube imaging LiDAR","volume":"15","author":"Dong","year":"2021","journal-title":"J. Appl. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"111488","DOI":"10.1016\/j.measurement.2022.111488","article-title":"Extracting suburban residential building zone from airborne streak tube imaging LiDAR data","volume":"199","author":"Yan","year":"2022","journal-title":"Measurement"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"03122005","DOI":"10.1061\/(ASCE)CO.1943-7862.0002347","article-title":"Implementing Remote-Sensing Methodologies for Construction Research: An Unoccupied Airborne System Perspective","volume":"148","author":"Zhang","year":"2022","journal-title":"J. Constr. Eng. Manag."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 8\u201316). SSD: Single Shot MultiBox Detector. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, Z., Wang, Y.C., Zhang, N., Zhang, Y.X., Zhao, Z.K., Xu, D.D., Ben, G.L., and Gao, Y.X. (2022). Deep Learning-Based Object Detection Techniques for Remote Sensing Images: A Survey. Remote Sens., 14.","DOI":"10.3390\/rs14102385"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1109\/TGRS.2019.2942103","article-title":"Object Detection in High-Resolution Panchromatic Images Using Deep Models and Spatial Template Matching","volume":"58","author":"Hou","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Fan, Q.C., Chen, F., Cheng, M., Lou, S.L., Xiao, R.L., Zhang, B., Wang, C., and Li, J. (2019). Ship Detection Using a Fully Convolutional Network with Compact Polarimetric SAR Images. Remote Sens., 11.","DOI":"10.3390\/rs11182171"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Alganci, U., Soydas, M., and Sertel, E. (2020). Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images. Remote Sens., 12.","DOI":"10.3390\/rs12030458"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.neucom.2022.01.022","article-title":"Object recognition datasets and challenges: A review","volume":"495","author":"Salari","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"104471","DOI":"10.1016\/j.imavis.2022.104471","article-title":"Deep learning-based detection from the perspective of small or tiny objects: A survey","volume":"123","author":"Tong","year":"2022","journal-title":"Image Vis. Comput."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"38297","DOI":"10.1007\/s11042-022-13153-y","article-title":"Tools, techniques, datasets and application areas for object detection in an image: A review","volume":"81","author":"Kaur","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A.G., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201323). MobileNetV2: Inverted Residuals and Linear Bottlenecks. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_30","unstructured":"Liang, Y., Ge, C., Tong, Z., Song, Y., Wang, J., and Xie, P. (2022, February 01). Not All Patches are What You Need: Expediting Vision Transformers via Token Reorganizations. Available online: https:\/\/ui.adsabs.harvard.edu\/abs\/2022arXiv220207800L."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1128\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:36:12Z","timestamp":1760121372000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/4\/1128"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,18]]},"references-count":30,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15041128"],"URL":"https:\/\/doi.org\/10.3390\/rs15041128","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,18]]}}}