{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T14:57:08Z","timestamp":1782313028942,"version":"3.54.5"},"reference-count":55,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,3,21]],"date-time":"2020-03-21T00:00:00Z","timestamp":1584748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41771374"],"award-info":[{"award-number":["41771374"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Key Research and Development Programs for Global Change and Adaptation","award":["2016YFA0600202"],"award-info":[{"award-number":["2016YFA0600202"]}]},{"name":"the State Key Laboratory of Soil &amp; Sustainable Agriculture Research Fund","award":["Study on the applications of 3-D forest structural parameters automatic retrieval model"],"award-info":[{"award-number":["Study on the applications of 3-D forest structural parameters automatic retrieval model"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Separating foliage and woody components can effectively improve the accuracy of simulating the forest eco-hydrological processes. It is still challenging to use deep learning models to classify canopy components from the point cloud data collected in forests by terrestrial laser scanning (TLS). In this study, we developed a convolution neural network (CNN)-based model to separate foliage and woody components (FWCNN) by combing the geometrical and laser return intensity (LRI) information of local point sets in TLS datasets. Meanwhile, we corrected the LRI information and proposed a contribution score evaluation method to objectively determine hyper-parameters (learning rate, batch size, and validation split rate) in the FWCNN model. Our results show that: (1) Correcting the LRI information could improve the overall classification accuracy (OA) of foliage and woody points in tested broadleaf (from 95.05% to 96.20%) and coniferous (from 93.46% to 94.98%) TLS datasets (Kappa \u2265 0.86). (2) Optimizing hyper-parameters was essential to enhance the running efficiency of the FWCNN model, and the determined hyper-parameter set was suitable to classify all tested TLS data. (3) The FWCNN model has great potential to classify TLS data in mixed forests with OA &gt; 84.26% (Kappa \u2265 0.67). This work provides a foundation for retrieving the structural features of woody materials within the forest canopy.<\/jats:p>","DOI":"10.3390\/rs12061010","type":"journal-article","created":{"date-parts":[[2020,3,24]],"date-time":"2020-03-24T07:16:08Z","timestamp":1585034168000},"page":"1010","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["An Improved Convolution Neural Network-Based Model for Classifying Foliage and Woody Components from Terrestrial Laser Scanning Data"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8577-507X","authenticated-orcid":false,"given":"Bingxiao","family":"Wu","sequence":"first","affiliation":[{"name":"International Institute for Earth System Science, Nanjing University, Nanjing 210023, China"},{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guang","family":"Zheng","sequence":"additional","affiliation":[{"name":"International Institute for Earth System Science, Nanjing University, Nanjing 210023, China"},{"name":"Jiangsu Provincial Key Laboratory of Geographic Information Science, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yang","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.jhydrol.2018.04.003","article-title":"Effects of forest structure on hydrological processes in China","volume":"561","author":"Sun","year":"2018","journal-title":"J. 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