NDLI: Introducing Improved Transformer to Land Cover Classification Using Multispectral LiDAR Point Clouds

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Introducing Improved Transformer to Land Cover Classification Using Multispectral LiDAR Point Clouds

Content Provider	MDPI
Author	Zhang, Zhiwen Li, Teng Tang, Xuebin Lei, Xiangda Peng, Yuanxi
Copyright Year	2022
Abstract	The use of Transformer-based networks has been proposed for the processing of general point clouds. However, there has been little research related to multispectral LiDAR point clouds that contain both spatial coordinate information and multi-wavelength intensity information. In this paper, we propose networks for multispectral LiDAR point cloud point-by-point classification based on an improved Transformer. Specifically, considering the sparseness of different regions of multispectral LiDAR point clouds, we add a bias to the Transformer to improve its ability to capture local information and construct an easy-to-implement multispectral LiDAR point cloud Transformer (MPT) classification network. The MPT network achieves 78.49% $m I o U$ , 94.55% $O A$ , 84.46% $F_{1}$ , and 0.92 $K a p p a$ on the multispectral LiDAR point cloud testing dataset. To further extract the topological relationships between points, we present a standardization set abstraction (SSA) module, which includes the global point information while considering the relationships among the local points. Based on the SSA module, we propose an advanced version called MPT+ for the point-by-point classification of multispectral LiDAR point clouds. The MPT+ network achieves 82.94% $m I o U$ , 95.62% $O A$ , 88.42% $F_{1}$ , and 0.94 $K a p p a$ on the same testing dataset. Compared with seven point-based deep learning algorithms, our proposed MPT+ achieves state-of-the-art results for several evaluation metrics.
Starting Page	3808
e-ISSN	20724292
DOI	10.3390/rs14153808
Journal	Remote Sensing
Issue Number	15
Volume Number	14
Language	English
Publisher	MDPI
Publisher Date	2022-08-07
Access Restriction	Open
Subject Keyword	Remote Sensing Imaging Science Biasformer Standardization Set Abstraction Multispectral Lidar Point Clouds Land Cover Classification
Content Type	Text
Resource Type	Article

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