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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 phase, providing automated noise-filtering of the ATL03 data. The terrain and top of canopy elevations are subsequently aggregated in 100 m segments using a series of iterative smoothing filters. We demonstrate improved estimates for both terrain and top of canopy elevations compared to the ATL08 100 m segment estimates. The neural network (NN) noise filtering reliably eliminated outlier top of canopy estimates caused by low clouds, and aggregated root mean square error (RMSE) decreased from 7.7 m for ATL08 to 3.7 m for NN prediction (18 test profiles aggregated). For terrain elevations, RMSE decreased from 5.2 m for ATL08 to 3.3 m for the NN prediction, compared to airborne lidar reference profiles.ICESat-2LidarPoint cloudNoise filtering
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Utilizing Machine Learning to Improve Searches for LIGO Sources
We develop methods to more efficiently differentiate between gravitational wave signals from binary mergers, and detector noise. We make use of the PyCBC detection pipeline to compile larger amounts of data, including signal and noise, into SNR density plots, and we modified them so that they could be easily interpreted by an image classifier. After selecting the parameters that demonstrated features in the density plots, we created a convolutional neural network to search for these patterns. We trained and tested the neural network over increasingly large and varied data sets.
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- Award ID(s):
- 1757303
- PAR ID:
- 10089821
- Date Published:
- Journal Name:
- LIGO Laboratory Summer 2018 Undergraduate Research
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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