ICESat-2 noise filtering using a point cloud neural network

作者：Mariya Velikova, Juan Carlos Fernández-Diaz, Craig Glennie · 发表于：ISPRS Open Journal of Photogrammetry and Remote Sensing · 年份：2023 · DOI：10.1016/j.ophoto.2023.100053 · 被引用次数：8 · 研究领域：Remote Sensing and LiDAR Applications、Cryospheric studies and observations、Plant Water Relations and Carbon Dynamics

The ATLAS sensor onboard the ICESat-2 satellite is a photon-counting lidar (PCL) with a primary mission to map Earth's ice sheets. A secondary goal of the mission is to provide vegetation and terrain elevations, which are essential for calculating the planet's biomass carbon reserves. A drawback of ATLAS is that the sensor does not provide reliable terrain height estimates in dense, high-closure forests because only a few photons reach the ground through the canopy and return to the detector. This low penetration translates into lower accuracy for the resultant terrain model. Tropical forest measurements with ATLAS have an additional problem estimating top of canopy because of frequent atmospheric phenomena such as fog and low clouds that can be misinterpreted as top of the canopy. To alleviate these issues, we propose using a ConvPoint neural network for 3D point clouds and high-density airborne lidar as training data to classify vegetation and terrain returns from ATLAS. The semantic segmentation network provides excellent results and could be used in parallel with the current ATL08 noise filtering algorithms, especially in areas with dense vegetation. We use high-density airborne lidar data acquired along ICESat-2 transects in Central American forests as a ground reference for training the neural network to distinguish between noise photons and photons lying between the terrain and the top of the canopy. Each photon event receives a label (noise or signal) in the test phas...