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A network called PointMM is developed in this study to enhance the accuracy of point cloud semantic segmentation in complex scenes. The main contribution of PointMM involves two aspects: (1) Multi-spatial feature encoding. We leverage a novel feature encoding module to learn multi-spatial features from the neighborhood point set obtained by k-nearest neighbors (KNN) in the feature space. This enhances the network\u2019s ability to learn the spatial structures of various samples more finely and completely. (2) Multi-head attention pooling. We leverage a multi-head attention pooling module to address the limitations of symmetric function-based pooling, such as maximum and average pooling, in terms of losing detailed feature information. This is achieved by aggregating multi-spatial and attribute features of point clouds, thereby enhancing the network\u2019s ability to transmit information more comprehensively and accurately. Experiments on publicly available point cloud datasets S3DIS and ISPRS 3D Vaihingen demonstrate that PointMM effectively learns features at different levels, while improving the semantic segmentation accuracy of various objects. Compared to 12 state-of-the-art methods reported in the literature, PointMM outperforms the runner-up by 2.3% in OA on the ISPRS 3D Vaihingen dataset, and achieves the third best performance in both OA and MioU on the S3DIS dataset. Both achieve a satisfactory balance between OA, F1, and MioU.<\/jats:p>","DOI":"10.3390\/rs16071246","type":"journal-article","created":{"date-parts":[[2024,3,31]],"date-time":"2024-03-31T13:28:00Z","timestamp":1711891680000},"page":"1246","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["PointMM: Point Cloud Semantic Segmentation CNN under Multi-Spatial Feature Encoding and Multi-Head Attention Pooling"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3531-9276","authenticated-orcid":false,"given":"Ruixing","family":"Chen","sequence":"first","affiliation":[{"name":"School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Xu","sequence":"additional","affiliation":[{"name":"Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, J., Xie, H., Zhang, L., and Lu, Z. 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