AGFP-Net: Attentive geometric feature pyramid network for land cover classification using airborne multispectral LiDAR data

Abstract

Accurate land cover (LC) classification plays an important role in ecosystem protection, climate changes, and urban planning. The airborne multispectral LiDAR data are increasingly used for high-resolution and accurate LC classification tasks. However, most of the existing methods lack of the comprehensive extraction of the spatial geometric structure features, and ignore the fusion of multi-scale extracted features. In this paper, a point-wise deep learning-based method is proposed for LC classification based on airborne multispectral LiDAR data. We present a novel convolution operator to efficiently extract the spatial geometric structure features, called attentive graph geometric moments convolution (AGGM Convolution). Besides, to fuse the extracted multi-scale features, we propose a feature up-sampling module and construct a feature pyramid to integrate the features with different scales. The proposed method was evaluated using multispectral LiDAR data acquired with an airborne Teledyne Optech Titan system. In comparison with the previously developed state-of-the-art point cloud segmentation models, the proposed method behaves superiorly with an overall accuracy of 96.9% and a Kappa index of 0.950 on the test scenes. The quantitative assessments demonstrate that the proposed method performs effectively and efficiently in land cover classification tasks.