URS: An Unsupervised Radargram Segmentation Network Based on Self-Supervised ViT With Contrastive Feature Learning Framework-Reference-Cited by-同舟云学术

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URS: An Unsupervised Radargram Segmentation Network Based on Self-Supervised ViT With Contrastive Feature Learning Framework

Published:2024 Issue: Volume:17 Page:15512-15524
ISSN:1939-1404
Container-title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
language:
Short-container-title:IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

Author:

Ghosh Raktim¹,Bovolo Francesca¹^ORCID

Affiliation:

1. Center of Digital Society, Fondazione Bruno Kessler, Trento, Italy

Funder

Italian Space Agency

Missione JUICE - Attivitã dei team scientifici dei Payload per Lancio

Agenzia Spaziale Italian-Istituto Nazionale di Astrofisica

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/4609443/10330207/10646483.pdf?arnumber=10646483

Reference44 articles.

1. An Approach to the Generation and Analysis of Databases of Simulated Radar Sounder Data for Performance Prediction and Target Interpretation

2. Distributed Radar Sounder: A Novel Concept for Subsurface Investigations Using Sensors in Formation Flight

3. A technique for the automatic estimation of ice thickness and bedrock properties from radar sounder data acquired at Antarctica

4. A Reconciled Estimate of Ice-Sheet Mass Balance

5. Surface Expression of Basal and Englacial Features, Properties, and Processes of the Greenland Ice Sheet

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