Improved Semisupervised UNet Deep Learning Model for Forest Height Mapping With Satellite SAR and Optical Data

Improved Semisupervised UNet Deep Learning Model for Forest Height Mapping With Satellite SAR and Optical Data

In this study, we introduce an improved semisupervised deep learning approach, and demonstrate its suitability for modeling the relationship between forest structural parameters and satellite remote sensing imagery and producing forest maps. The improved approach is based on a popular UNet model, mo...

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Bibliographic Details
Main Authors: Shaojia Ge, Hong Gu, Weimin Su, Jaan Praks, Oleg Antropov
Format: Article
Language:English
Published: IEEE 2022-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Deep learning (DL)
regression
semisupervised
Sentinel-1
Sentinel-2
synthetic aperture radar (SAR)
Online Access:https://ieeexplore.ieee.org/document/9814844/

Internet

https://ieeexplore.ieee.org/document/9814844/

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