Deep learning and earth observation to support the sustainable development goals: Current approaches, open challenges, and future opportunities

C Persello, JD Wegner, R Hänsch… - … and Remote Sensing …, 2022 - ieeexplore.ieee.org
The synergistic combination of deep learning (DL) models and Earth observation (EO)
promises significant advances to support the Sustainable Development Goals (SDGs). New …

Image restoration for remote sensing: Overview and toolbox

B Rasti, Y Chang, E Dalsasso, L Denis… - IEEE Geoscience and …, 2021 - ieeexplore.ieee.org
Remote sensing provides valuable information about objects and areas from a distance in
either active (eg, radar and lidar) or passive (eg, multispectral and hyperspectral) modes …

[HTML][HTML] 面向SAR 图像解译的物理可解释深度学习技术进展与探讨

黄钟泠, 姚西文, 韩军伟 - 雷达学报, 2021 - radars.ac.cn
深度学习技术近年来在合成孔径雷达(SAR) 图像解译领域发展迅速, 但当前基于数据驱动的方法
通常忽视了SAR 潜在的物理特性, 预测结果高度依赖训练数据, 甚至违背了物理认知 …

A review of satellite synthetic aperture radar interferometry applications in permafrost regions: Current status, challenges, and trends

Z Zhang, H Lin, M Wang, X Liu, Q Chen… - … and Remote Sensing …, 2022 - ieeexplore.ieee.org
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 …

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 …

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 …

A CNN-based coherence-driven approach for InSAR phase unwrapping

F Sica, F Calvanese, G Scarpa… - IEEE Geoscience and …, 2020 - ieeexplore.ieee.org
Phase unwrapping (PU) is among the most critical tasks in synthetic aperture radar (SAR)
interferometry (InSAR). Due to the presence of noise, the interferogram usually presents …

[HTML][HTML] Deep learning for InSAR phase filtering: An optimized framework for phase unwrapping

G Murdaca, A Rucci, C Prati - Remote Sensing, 2022 - mdpi.com
Interferometric Synthetic Aperture Radar (InSAR) data processing applications, such as
deformation monitoring and topographic mapping, require an interferometric phase filtering …

PDNet: A lightweight deep convolutional neural network for InSAR phase denoising

H Yu, T Yang, L Zhou, Y Wang - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Interferometric phase denoising is a vital procedure for interferometric synthetic aperture
radar (InSAR)-based remote sensing techniques because it can improve the accuracy of the …

Automatic mapping of rice growth stages using the integration of sentinel-2, mod13q1, and sentinel-1

F Ramadhani, R Pullanagari, G Kereszturi, J Procter - Remote sensing, 2020 - mdpi.com
Rice (Oryza sativa L.) is a staple food crop for more than half of the world's population. Rice
production is facing a myriad of problems, including water shortage, climate, and land-use …