A Deep Learning Solution for Height Estimation on a Forested Area Based on Pol-TomoSAR Data-Reference-Cited by-同舟云学术

A Deep Learning Solution for Height Estimation on a Forested Area Based on Pol-TomoSAR Data

Published:2023 Issue: Volume:61 Page:1-14
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Yang Wenyu¹^ORCID,Vitale Sergio¹^ORCID,Aghababaei Hossein²^ORCID,Ferraioli Giampaolo³^ORCID,Pascazio Vito¹^ORCID,Schirinzi Gilda¹^ORCID

Affiliation:

1. Dipartimento di Ingegneria, Università degli Studi di Napoli “Parthenope,”, Naples, Italy

2. Department of Earth Observation Science, Faculty of Geo-Information Science and Earth Observation, University of Twente, Enschede, The Netherlands

3. Dipartimento di Scienze e Tecnologie, Università degli Studi di Napoli “Parthenope,”, Naples, Italy

Funder

Italian Space Agency (ASI) through “MEFISTO—Metodi di Elaborazione di dati SAR multi-Frequenza per Il monitoraggio del disseSTO idrogeologico” within the Framework of BANDO

“Studio di nuovi metodi e tecniche basati sull’utilizzo di dati sar multimissione/multifrequenza”

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10121647.pdf?arnumber=10121647

Reference41 articles.

1. Model-Based Target Scattering Decomposition of Polarimetric SAR Tomography

2. Phase Calibration Based on Phase Derivative Constrained Optimization in Multibaseline SAR Tomography

3. Compressive Sensing for Multibaseline Polarimetric SAR Tomography of Forested Areas

4. Imbalance: Oversampling algorithms for imbalanced classification in R

5. On the Separation of Ground and Canopy Scatterings Using Single Polarimetric Multi-Baseline SAR Tomography

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