{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T04:48:42Z","timestamp":1769575722974,"version":"3.49.0"},"reference-count":50,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T00:00:00Z","timestamp":1706832000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62331019"],"award-info":[{"award-number":["62331019"]}]},{"name":"National Natural Science Foundation of China","award":["62201438"],"award-info":[{"award-number":["62201438"]}]},{"name":"National Natural Science Foundation of China","award":["12005169"],"award-info":[{"award-number":["12005169"]}]},{"name":"National Natural Science Foundation of China","award":["2021JC-23"],"award-info":[{"award-number":["2021JC-23"]}]},{"name":"National Natural Science Foundation of China","award":["SXLK2022-02-8"],"award-info":[{"award-number":["SXLK2022-02-8"]}]},{"name":"Basic Research Program of Natural Sciences of Shaanxi Province","award":["62331019"],"award-info":[{"award-number":["62331019"]}]},{"name":"Basic Research Program of Natural Sciences of Shaanxi Province","award":["62201438"],"award-info":[{"award-number":["62201438"]}]},{"name":"Basic Research Program of Natural Sciences of Shaanxi Province","award":["12005169"],"award-info":[{"award-number":["12005169"]}]},{"name":"Basic Research Program of Natural Sciences of Shaanxi Province","award":["2021JC-23"],"award-info":[{"award-number":["2021JC-23"]}]},{"name":"Basic Research Program of Natural Sciences of Shaanxi Province","award":["SXLK2022-02-8"],"award-info":[{"award-number":["SXLK2022-02-8"]}]},{"name":"Shaanxi Forestry Science and Technology Innovation Key Project","award":["62331019"],"award-info":[{"award-number":["62331019"]}]},{"name":"Shaanxi Forestry Science and Technology Innovation Key Project","award":["62201438"],"award-info":[{"award-number":["62201438"]}]},{"name":"Shaanxi Forestry Science and Technology Innovation Key Project","award":["12005169"],"award-info":[{"award-number":["12005169"]}]},{"name":"Shaanxi Forestry Science and Technology Innovation Key Project","award":["2021JC-23"],"award-info":[{"award-number":["2021JC-23"]}]},{"name":"Shaanxi Forestry Science and Technology Innovation Key Project","award":["SXLK2022-02-8"],"award-info":[{"award-number":["SXLK2022-02-8"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>With the development and popularization of LiDAR technology, point clouds are becoming widely used in multiple fields. Point cloud classification plays an important role in segmentation, geometric analysis, and vegetation description. However, existing point cloud classification algorithms have problems such as high computational complexity, a lack of feature optimization, and low classification accuracy. This paper proposes an efficient point cloud classification algorithm based on dynamic spatial\u2013spectral feature optimization. It can eliminate redundant features, optimize features, reduce computational costs, and improve classification accuracy. It achieves feature optimization through three key steps. First, the proposed method extracts spatial, geometric, spectral, and other features from point cloud data. Then, the Gini index and Fisher score are used to calculate the importance and relevance of features, and redundant features are filtered. Finally, feature importance factors are used to dynamically enhance the discriminative power of highly distinguishable features to strengthen their contribution to point cloud classification. Four real-scene datasets from STPLS3D are utilized for experimentation. Compared to the other five algorithms, the proposed algorithm achieves at least a 37.97% improvement in mean intersection over union (mIoU). Meanwhile, the results indicate that the proposed algorithm can achieve high-precision point cloud classification with low computational complexity.<\/jats:p>","DOI":"10.3390\/rs16030575","type":"journal-article","created":{"date-parts":[[2024,2,2]],"date-time":"2024-02-02T09:42:32Z","timestamp":1706866952000},"page":"575","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Dynamic Spatial\u2013Spectral Feature Optimization-Based Point Cloud Classification"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1237-5910","authenticated-orcid":false,"given":"Yali","family":"Zhang","sequence":"first","affiliation":[{"name":"The Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"},{"name":"Xi\u2019an Key Laboratory of Advanced Remote Sensing, Xi\u2019an 710071, China"},{"name":"Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Wei","family":"Feng","sequence":"additional","affiliation":[{"name":"The Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"},{"name":"Xi\u2019an Key Laboratory of Advanced Remote Sensing, Xi\u2019an 710071, China"},{"name":"Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Yinghui","family":"Quan","sequence":"additional","affiliation":[{"name":"The Department of Remote Sensing Science and Technology, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"},{"name":"Xi\u2019an Key Laboratory of Advanced Remote Sensing, Xi\u2019an 710071, China"},{"name":"Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Guangqiang","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Aerospace Engineering, Air Force Engineering University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0677-6702","authenticated-orcid":false,"given":"Gabriel","family":"Dauphin","sequence":"additional","affiliation":[{"name":"The Laboratory of Information Processing and Transmission, L2TI, Institut Galil\u00e9, University Paris XIII, 93430 Villetaneuse, France"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.isprsjprs.2011.12.003","article-title":"Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data","volume":"67","author":"Hollaus","year":"2012","journal-title":"Isprs J. 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