Ice thickness from deep learning and conditional random fields: application to ice-penetrating radar data with radiometric validation
M Liu-Schiaffini, G Ng, C Grima… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Identifying the location of the ice–bedrock interface of glaciers and ice sheets is crucial for a
wide range of geophysical applications, such as searching for liquid water in basal regions …
wide range of geophysical applications, such as searching for liquid water in basal regions …
Feature tracing in radio-echo sounding products of terrestrial ice sheets and planetary bodies
Radio-echo sounding (RES) is a useful technique for measuring the subsurface properties
of ice sheets and glaciers. One of the most important and unique outcomes is the mapping of …
of ice sheets and glaciers. One of the most important and unique outcomes is the mapping of …
A weakly supervised transfer learning approach for radar sounder data segmentation
Airborne radar sounders (RSs) are active sensors that acquire subsurface data for Earth
observation. RS data (radargrams) provide information on buried geology by imaging …
observation. RS data (radargrams) provide information on buried geology by imaging …
Skip-WaveNet: A Wavelet based Multi-scale Architecture to Trace Firn Layers in Radar Echograms
Echograms created from airborne radar sensors capture the profile of firn layers present on
top of an ice sheet. Accurate tracking of these layers is essential to calculate the snow …
top of an ice sheet. Accurate tracking of these layers is essential to calculate the snow …
Super-Resolution of Radargrams with a Generative Deep Learning Model
Radar sounder (RS) profiles are essential for imaging the subsurface of planetary bodies
and the Earth as they provide valuable geological insights. However, the limited availability …
and the Earth as they provide valuable geological insights. However, the limited availability …
Crossed Siamese Vision Graph Neural Network for Remote Sensing Image Change Detection
The development of deep learning in remote-sensing (RS) visual tasks has led to
remarkable progress in RS image change detection (CD). However, RS bi-temporal images …
remarkable progress in RS image change detection (CD). However, RS bi-temporal images …
An Approach to Semantic Segmentation of Radar Sounder Data Based on Unsupervised Random Walks and User-Guided Label Propagation
J Dal Corso, L Bruzzone - IEEE Transactions on Geoscience …, 2024 - ieeexplore.ieee.org
Radar sounders (RSs) are utilized for the analysis of subsurface of Earth and other planets.
Data acquired from RS can be processed to obtain radargrams, which are 2-D arrays …
Data acquired from RS can be processed to obtain radargrams, which are 2-D arrays …
URS: An Unsupervised Radargram Segmentation Network based on Self-supervised ViT with Contrastive Feature Learning Framework
Radar sounders are air and space-borne nadir-looking sensors operating in high-frequency
(HF) or very high-frequency (VHF) bands and collect subsurface backscattered returns by …
(HF) or very high-frequency (VHF) bands and collect subsurface backscattered returns by …
Deep learning for unsupervised denoising of radar sounder data
E Donini, A Zuech, L Bruzzone… - IGARSS 2023-2023 …, 2023 - ieeexplore.ieee.org
Analyzing radar sounder (RS) profiles allows the retrieval of critical information on
subsurface geology. However, radar-grams suffer from several noise contributions …
subsurface geology. However, radar-grams suffer from several noise contributions …
A hybrid CNN-transformer architecture for semantic segmentation of radar sounder data
Radar Sounders (RSs) are space-borne and airborne sensors operating on the nadir-
looking geometry to collect sub-surface information by transmitting linearly modulated …
looking geometry to collect sub-surface information by transmitting linearly modulated …