{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T17:51:10Z","timestamp":1721584270358},"reference-count":46,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2019,11,29]],"date-time":"2019-11-29T00:00:00Z","timestamp":1574985600000},"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":["41971415","61175031"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Accurate and effective classification of lidar point clouds with discriminative features expression is a challenging task for scene understanding. In order to improve the accuracy and the robustness of point cloud classification based on single point features, we propose a novel point set multi-level aggregation features extraction and fusion method based on multi-scale max pooling and latent Dirichlet allocation (LDA). To this end, in the hierarchical point set feature extraction, point sets of different levels and sizes are first adaptively generated through multi-level clustering. Then, more effective sparse representation is implemented by locality-constrained linear coding (LLC) based on single point features, which contributes to the extraction of discriminative individual point set features. Next, the local point set features are extracted by combining the max pooling method and the multi-scale pyramid structure constructed by the point\u2019s coordinates within each point set. The global and the local features of the point sets are effectively expressed by the fusion of multi-scale max pooling features and global features constructed by the point set LLC-LDA model. The point clouds are classified by using the point set multi-level aggregation features. Our experiments on two scenes of airborne laser scanning (ALS) point clouds\u2014a mobile laser scanning (MLS) scene point cloud and a terrestrial laser scanning (TLS) scene point cloud\u2014demonstrate the effectiveness of the proposed point set multi-level aggregation features for point cloud classification, and the proposed method outperforms other related and compared algorithms.<\/jats:p>","DOI":"10.3390\/rs11232846","type":"journal-article","created":{"date-parts":[[2019,11,29]],"date-time":"2019-11-29T15:58:21Z","timestamp":1575043101000},"page":"2846","source":"Crossref","is-referenced-by-count":11,"title":["Point Set Multi-Level Aggregation Feature Extraction Based on Multi-Scale Max Pooling and LDA for Point Cloud Classification"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-0701-3010","authenticated-orcid":false,"given":"Guofeng","family":"Tong","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Northeastern University, Shenyang 110819, China"}]},{"given":"Yong","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Northeastern University, Shenyang 110819, China"}]},{"given":"Weilong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hebei Jiaotong Vocational and Technical College, Shijiazhuang 050035, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-8118-3889","authenticated-orcid":false,"given":"Dong","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0076-7311","authenticated-orcid":false,"given":"Zhenxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Advanced Innovation Center for Imaging Theory and Technology, Capital Normal University, Beijing 100048, China"}]},{"given":"Jingchao","family":"Yang","sequence":"additional","affiliation":[{"name":"Hebei Jiaotong Vocational and Technical College, Shijiazhuang 050035, China"}]},{"given":"Jianjun","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Northeastern University, Shenyang 110819, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Landrieu, L., and Simonovsky, M. 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