A tutorial on synthetic aperture radar
By Alberto Moreira, P. Prats-Iraola, Marwan Younis, Gerhard Krieger, Irena Hajnsek, K. Papathanassiou. IEEE Geoscience and Remote Sensing Magazine, 2013
2,533 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/06c75f5d3186559551ff9989c3a3ff654f6dc8f1
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Yonina C. Eldar
- Jie Xu
- Fan Liu
- Christos Masouros
- Richard Bamler
- Xiaoxiang Zhu
- Yuanhao Cui
- Tony X. Han
In Environmental Engineering
- Van Nhan Nguyen
- Davide Roverso
- Zhihui Yuan
- Richard Bamler
- Robert Jenssen
- Yang Lan
- Feng Xu
- Hyongki Lee
Citations in the corpus
- Integrated Sensing and Communications: Toward Dual-Functional Wireless Networks for 6G and Beyond (2021, background): “Today, more than 15 spaceborne radar systems are operated for innumerous applications, ranging from environmental and Earth system monitoring, change detection, 4D mapping (space and time), and security-related applications to planetary exploration [60].”
- A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images (2013, background): “SYNTHETIC aperture radar (SAR) remote sensing [1] offers a number of advantages over optical remote sensing, mainly the all-day, all-weather acquisition capability.”
- Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, methodology): “Regarding feature extraction, most conventional methods rely on exploring physical scattering properties [56] and texture information [57]”
- A SAR Dataset of Ship Detection for Deep Learning under Complex Backgrounds (2019, background): “This is because the datasets for the pretrained model are natural images from ImageNet [22] or COCO [23], which are quite different from SAR images [7,8].”
- Automatic autonomous vision-based power line inspection: A review of current status and the potential role of deep learning (2018, background): “SAR images are usually acquired by SARs mounted on various platforms such as small aircrafts and satellites [13].”
- SAR Automatic Target Recognition Based on Multiview Deep Learning Framework (2018, background): “With its all-weather, day-andnight operation and high-resolution imaging capability, it has superior performance compared with other sensors, such as optical and infrared tools [1], [2].”
- Phase Unwrapping in InSAR : A Review (2019, background): “In fact, a drawback as well as fundamental challenge of InSAR is that the desired absolute phase information is ambiguous [5].”