Recurrent Residual Dual Attention Network for Airborne Laser Scanning Point Cloud Semantic Segmentation

Kernel point convolution (KPConv) can effectively represent the point features of point cloud data. However, KPConv-based methods just consider the local information of each point, which is very difficult to characterize the intrinsic properties of airborne laser scanning (ALS) point clouds for comp...

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Veröffentlicht in:	IEEE transactions on geoscience and remote sensing Jg. 61; S. 1 - 14
Hauptverfasser:	Zeng, Tao, Luo, Fulin, Guo, Tan, Gong, Xiuwen, Xue, Jingyun, Li, Hanshan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Aggregation Airborne lasers Attention mechanism Coders Convolution Decoding Design Distribution encoder-decoder structure Feature extraction Image segmentation Kernel kernel point convolution (KPConv) Kernels Laser applications Lasers Methods Modules Normal distribution Point cloud compression point cloud semantic segmentation recurrent residual structure Scaling Scaling factors Scanning Semantic segmentation Semantics Three-dimensional displays
ISSN:	0196-2892, 1558-0644
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Abstract	Kernel point convolution (KPConv) can effectively represent the point features of point cloud data. However, KPConv-based methods just consider the local information of each point, which is very difficult to characterize the intrinsic properties of airborne laser scanning (ALS) point clouds for complex laser scanning conditions. Therefore, we rethink KPConv and propose a recurrent residual dual attention network (RRDAN) based on the encoder-decoder structure for the semantic segmentation of ALS point cloud data. In the encoder stage, we design an attention KPConv (AKPConv) block by using a scaling factor of batch normalization to highlight the significant channel information. Then, we use the AKPConv block to develop a recurrent residual kernel attention (RRKA) module to iteratively aggregate the local neighborhood features. In the decoder stage, we design a global and local channel attention (GLCA) module with global connection and local 1-D convolution to interact the global and local information after fusing the upsampled high-level representations and the skip-connected low-level features. In addition, to reduce the influence of the long-tailed distribution of reflection intensity, we apply gamma transformation to correct the data as a normal distribution. The proposed RRDAN can achieve diversified feature aggregation to implement the refined semantic segmentation of ALS point clouds. We evaluate our method on three ALS datasets (i.e., ISPRS, DCF2019, and LASDU) to demonstrate its performance compared to a few advanced methods. The code is available at https://github.com/SC-shendazt/RRDAN .
AbstractList	Kernel point convolution (KPConv) can effectively represent the point features of point cloud data. However, KPConv-based methods just consider the local information of each point, which is very difficult to characterize the intrinsic properties of airborne laser scanning (ALS) point clouds for complex laser scanning conditions. Therefore, we rethink KPConv and propose a recurrent residual dual attention network (RRDAN) based on the encoder–decoder structure for the semantic segmentation of ALS point cloud data. In the encoder stage, we design an attention KPConv (AKPConv) block by using a scaling factor of batch normalization to highlight the significant channel information. Then, we use the AKPConv block to develop a recurrent residual kernel attention (RRKA) module to iteratively aggregate the local neighborhood features. In the decoder stage, we design a global and local channel attention (GLCA) module with global connection and local 1-D convolution to interact the global and local information after fusing the upsampled high-level representations and the skip-connected low-level features. In addition, to reduce the influence of the long-tailed distribution of reflection intensity, we apply gamma transformation to correct the data as a normal distribution. The proposed RRDAN can achieve diversified feature aggregation to implement the refined semantic segmentation of ALS point clouds. We evaluate our method on three ALS datasets (i.e., ISPRS, DCF2019, and LASDU) to demonstrate its performance compared to a few advanced methods. The code is available at https://github.com/SC-shendazt/RRDAN .
Author	Zeng, Tao Gong, Xiuwen Li, Hanshan Luo, Fulin Guo, Tan Xue, Jingyun
Author_xml	– sequence: 1 givenname: Tao surname: Zeng fullname: Zeng, Tao email: sczt21@163.com organization: School of Mechatronic Engineering, Xi'an Technological University, Xi'an, China – sequence: 2 givenname: Fulin orcidid: 0000-0002-7696-0775 surname: Luo fullname: Luo, Fulin email: luoflyn@163.com organization: College of Computer Science, Chongqing University, Chongqing, China – sequence: 3 givenname: Tan orcidid: 0000-0001-9523-8094 surname: Guo fullname: Guo, Tan email: guot@cqupt.edu.cn organization: School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China – sequence: 4 givenname: Xiuwen orcidid: 0000-0002-1078-1571 surname: Gong fullname: Gong, Xiuwen email: xiuwen.gong@sydney.edu.au organization: Faculty of Engineering, The University of Sydney, Sydney, NSW, Australia – sequence: 5 givenname: Jingyun surname: Xue fullname: Xue, Jingyun organization: School of Mechatronic Engineering, Xi'an Technological University, Xi'an, China – sequence: 6 givenname: Hanshan surname: Li fullname: Li, Hanshan organization: School of Mechatronic Engineering, Xi'an Technological University, Xi'an, China
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Snippet	Kernel point convolution (KPConv) can effectively represent the point features of point cloud data. However, KPConv-based methods just consider the local...
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SubjectTerms	Aggregation Airborne lasers Attention mechanism Coders Convolution Decoding Design Distribution encoder-decoder structure Feature extraction Image segmentation Kernel kernel point convolution (KPConv) Kernels Laser applications Lasers Methods Modules Normal distribution Point cloud compression point cloud semantic segmentation recurrent residual structure Scaling Scaling factors Scanning Semantic segmentation Semantics Three-dimensional displays
Title	Recurrent Residual Dual Attention Network for Airborne Laser Scanning Point Cloud Semantic Segmentation
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