End-to-End Full-Waveform Echo Decomposition Based on Self-Attention Classification and U-Net Decomposition
Author:
Affiliation:
1. School of Optics and Photonics, Beijing Institute of Technology, Beijing, China
Funder
National Natural Science Foundation of China
Fundamental Research Funds for the Central Universities
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
http://xplorestaging.ieee.org/ielx7/4609443/9656571/09870804.pdf?arnumber=9870804
Reference46 articles.
1. Full-waveform LiDAR echo decomposition based on dense and residual neural networks
2. Terrestrial LiDAR data classification based on raw waveform samples versus online waveform attributes;pashaei;IEEE Trans Geosci Remote Sens,2021
3. An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing
4. Deep Learning for Unmanned Aerial Vehicle-Based Object Detection and Tracking: A Survey
5. Vehicle detection of multi-source remote sensing data using active fine-tuning network
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