PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data-Reference-Cited by-同舟云学术

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PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data

Published:2022-04 Issue: Volume:186 Page:123-139
ISSN:0924-2716
Container-title:ISPRS Journal of Photogrammetry and Remote Sensing
language:en
Short-container-title:ISPRS Journal of Photogrammetry and Remote Sensing

Author:

Zhang Qi,Ge Linlin,Hensley Scott,Isabel Metternicht Graciela,Liu Chang,Zhang Ruiheng

Publisher

Elsevier BV

Subject

Computers in Earth Sciences,Computer Science Applications,Engineering (miscellaneous),Atomic and Molecular Physics, and Optics

Reference71 articles.

1. Forest SAR tomography: principles and applications;Aghababaei;IEEE Geoscience and remote sensing magazine,2020

2. A survey of temporal decorrelation from spaceborne L-Band repeat-pass InSAR;Ahmed;Remote Sensing of Environment,2011

3. The use of waveform lidar to measure northern temperate mixed conifer and deciduous forest structure in New Hampshire;Anderson;Remote Sensing of Environment,2006

4. Forest biomass, carbon neutrality and climate change mitigation;Berndes;From science to policy,2016

5. Super-resolution-guided progressive pansharpening based on a deep convolutional neural network;Cai;IEEE transactions on Geoscience and Remote Sensing,2021

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1. A Multi-Baseline Forest Height Estimation Method Combining Analytic and Geometric Expression of the RVoG Model;Forests;2024-08-27

2. A higher accuracy forest cover product of China by fusing heterogeneous forest-related datasets using Dempster-Shafer theory;International Journal of Digital Earth;2024-06-25

3. Integrating multi-source remote sensing data for mapping boreal forest canopy height and species in interior Alaska in support of radar modeling;Environmental Research Letters;2024-06-17

4. The SAR2Height framework for urban height map reconstruction from single SAR intensity images;ISPRS Journal of Photogrammetry and Remote Sensing;2024-05

5. Mapping Coniferous Forest Distribution in a Semi-Arid Area Based on Multi-Classifier Fusion and Google Earth Engine Combining Gaofen-1 and Sentinel-1 Data: A Case Study in Northwestern Liaoning, China;Forests;2024-02-02

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