[HTML][HTML] Deep learning in multimodal remote sensing data fusion: A comprehensive review

J Li, D Hong, L Gao, J Yao, K Zheng, B Zhang… - International Journal of …, 2022 - Elsevier
With the extremely rapid advances in remote sensing (RS) technology, a great quantity of
Earth observation (EO) data featuring considerable and complicated heterogeneity are …

Integrated sensing and communications: Toward dual-functional wireless networks for 6G and beyond

F Liu, Y Cui, C Masouros, J Xu, TX Han… - IEEE journal on …, 2022 - ieeexplore.ieee.org
As the standardization of 5G solidifies, researchers are speculating what 6G will be. The
integration of sensing functionality is emerging as a key feature of the 6G Radio Access …

Sparse synthetic aperture radar imaging from compressed sensing and machine learning: Theories, applications, and trends

G Xu, B Zhang, H Yu, J Chen, M Xing… - IEEE Geoscience and …, 2022 - ieeexplore.ieee.org
Synthetic aperture radar (SAR) image formation can be treated as a class of ill-posed linear
inverse problems, and the resolution is limited by the data bandwidth for traditional imaging …

BIM, machine learning and computer vision techniques in underground construction: Current status and future perspectives

MQ Huang, J Ninić, QB Zhang - Tunnelling and Underground Space …, 2021 - Elsevier
The architecture, engineering and construction (AEC) industry is experiencing a
technological revolution driven by booming digitisation and automation. Advances in …

Hyperspectral and SAR image classification via multiscale interactive fusion network

J Wang, W Li, Y Gao, M Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Due to the limitations of single-source data, joint classification using multisource remote
sensing data has received increasing attention. However, existing methods still have certain …

Measuring, modelling and projecting coastal land subsidence

M Shirzaei, J Freymueller, TE Törnqvist… - Nature Reviews Earth & …, 2021 - nature.com
Coastal subsidence contributes to relative sea-level rise and exacerbates flooding hazards,
with the at-risk population expected to triple by 2070. Natural processes of vertical land …

A SAR dataset of ship detection for deep learning under complex backgrounds

Y Wang, C Wang, H Zhang, Y Dong, S Wei - remote sensing, 2019 - mdpi.com
With the launch of space-borne satellites, more synthetic aperture radar (SAR) images are
available than ever before, thus making dynamic ship monitoring possible. Object detectors …

Deep learning meets SAR: Concepts, models, pitfalls, and perspectives

XX Zhu, S Montazeri, M Ali, Y Hua… - … and Remote Sensing …, 2021 - ieeexplore.ieee.org
Deep learning in remote sensing has received considerable international hype, but it is
mostly limited to the evaluation of optical data. Although deep learning has been introduced …

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 …

Motion compensation/autofocus in airborne synthetic aperture radar: A review

J Chen, M Xing, H Yu, B Liang, J Peng… - IEEE Geoscience and …, 2021 - ieeexplore.ieee.org
Air-and spaceborne synthetic aperture radar (SAR) can provide a large number of high-
resolution images for microwave remote sensing applications, such as geoscience and …