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Point Set Multi-Level Aggregation Feature Extraction Based on Multi-Scale Max Pooling and LDA for Point Cloud Classification

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College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
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Hebei Jiaotong Vocational and Technical College, Shijiazhuang 050035, China
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College of Civil Engineering, Nanjing Forestry University, Nanjing 210037, China
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Beijing Advanced Innovation Center for Imaging Theory and Technology, Capital Normal University, Beijing 100048, China
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Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2846; https://doi.org/10.3390/rs11232846
Received: 5 November 2019 / Revised: 28 November 2019 / Accepted: 28 November 2019 / Published: 29 November 2019
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’s 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—a mobile laser scanning (MLS) scene point cloud and a terrestrial laser scanning (TLS) scene point cloud—demonstrate 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. View Full-Text
Keywords: point cloud classification; multi-level point sets; multi-scale features; max pooling point cloud classification; multi-level point sets; multi-scale features; max pooling
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MDPI and ACS Style

Tong, G.; Li, Y.; Zhang, W.; Chen, D.; Zhang, Z.; Yang, J.; Zhang, J. Point Set Multi-Level Aggregation Feature Extraction Based on Multi-Scale Max Pooling and LDA for Point Cloud Classification. Remote Sens. 2019, 11, 2846.

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