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

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% mIoU, 94.55% OA, 84.46% F1, and 0.92 Kappa 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% mIoU, 95.62% OA, 88.42% F1, and 0.94 Kappa 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.