Forest Height Inversion by Convolutional Neural Networks Based on L-Band PolInSAR Data Without Prior Knowledge Dependency

Forest Height Inversion by Convolutional Neural Networks Based on L-Band PolInSAR Data Without Prior Knowledge Dependency

Forest height is a key forest parameter which is of great significance for monitoring forest resources, calculating forest biomass, and observing the global carbon cycle. Because the PolInSAR system could provide various object information including height, shape and direction sensitivity, and spati...

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Bibliographic Details
Main Authors: Dandan Li, Hailiang Lu, Chao Li, Linda Mohaisen, Weipeng Jing
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Artificial intelligence
forestry
neural networks
polarimetry
synthetic aperture radar (SAR)
Online Access:https://ieeexplore.ieee.org/document/10301544/

Internet

https://ieeexplore.ieee.org/document/10301544/

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