Deep learning and earth observation to support the sustainable development goals: Current approaches, open challenges, and future opportunities
The synergistic combination of deep learning (DL) models and Earth observation (EO)
promises significant advances to support the Sustainable Development Goals (SDGs). New …
promises significant advances to support the Sustainable Development Goals (SDGs). New …
On the use of deep learning for phase recovery
Phase recovery (PR) refers to calculating the phase of the light field from its intensity
measurements. As exemplified from quantitative phase imaging and coherent diffraction …
measurements. As exemplified from quantitative phase imaging and coherent diffraction …
Deep learning-based branch-cut method for InSAR two-dimensional phase unwrapping
Two-dimensional (2-D) phase unwrapping (PU) is a critical processing step for many
synthetic aperture radar (SAR) interferometry (InSAR) applications. As is well known, the …
synthetic aperture radar (SAR) interferometry (InSAR) applications. As is well known, the …
PU-GAN: A one-step 2-D InSAR phase unwrapping based on conditional generative adversarial network
Two-dimensional phase unwrapping (PU) is a classical ill-posed problem in synthetic
aperture radar interferometry (InSAR). The traditional algorithmic model-based 2-D PU …
aperture radar interferometry (InSAR). The traditional algorithmic model-based 2-D PU …
A review of satellite synthetic aperture radar interferometry applications in permafrost regions: Current status, challenges, and trends
With climate change and the increment of human activities, global permafrost is undergoing
degradation, threatening the stability of engineering and the ecological environment of the …
degradation, threatening the stability of engineering and the ecological environment of the …
Artificial intelligence in interferometric synthetic aperture radar phase unwrapping: A review
L Zhou, H Yu, Y Lan - IEEE Geoscience and Remote Sensing …, 2021 - ieeexplore.ieee.org
Interferometric synthetic aperture radar (InSAR) is a radar technique widely used in geodesy
and remote sensing applications, eg, topography reconstruction and subsidence estimation …
and remote sensing applications, eg, topography reconstruction and subsidence estimation …
InSAR spatial-heterogeneity tropospheric delay correction in steep mountainous areas based on deep learning for landslides monitoring
H Zhou, K Dai, S Pirasteh, R Li… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Synthetic aperture radar interferometry (InSAR) technology has been widely used for
landslide monitoring in mountainous areas. The troposphere in steep mountainous areas is …
landslide monitoring in mountainous areas. The troposphere in steep mountainous areas is …
Deep learning for the detection and phase unwrapping of mining-induced deformation in large-scale interferograms
This article proposes deep convolutional neural networks to detect and map localized, rapid
subsidence caused by mining activities using time-series Sentinel-1 synthetic aperture radar …
subsidence caused by mining activities using time-series Sentinel-1 synthetic aperture radar …
CANet: An unsupervised deep convolutional neural network for efficient cluster-analysis-based multibaseline InSAR phase unwrapping
L Zhou, H Yu, Y Lan, S Gong… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Multibaseline (MB) phase unwrapping (PU) is a vital processing procedure for MB synthetic
aperture radar interferometry (InSAR) signal processing and can improve the traditional …
aperture radar interferometry (InSAR) signal processing and can improve the traditional …
Deep-learning-based phase discontinuity prediction for 2-D phase unwrapping of SAR interferograms
Phase unwrapping is a critical step of interferometric synthetic aperture radar processing,
and its accuracy directly determines the reliability of subsequent applications. Many phase …
and its accuracy directly determines the reliability of subsequent applications. Many phase …