The development of deep learning in synthetic aperture radar imagery
Open accessAbstract
The usage of remote sensing to observe environments necessitates interdisciplinary approaches to derive effective, impactful research. One remote sensing technique, Synthetic Aperture Radar, has shown significant benefits over traditional remote sensing techniques but comes at the price of additional complexities. To adequately cope with these, researchers have begun to employ advanced machine learning techniques known as deep learning to Synthetic Aperture Radar data. Deep learning represents the next stage in the evolution of machine intelligence which places the onus of identifying salient features on the network rather than researcher. This paper will outline machine learning techniques as it has been used previously on SAR; what is deep learning and where it fits in compared to traditional machine learning; what benefits can be derived by applying it to Synthetic Aperture Radar imagery; and finally describe some obstacles that still need to be overcome in order to provide constient and long term results from deep learning in SAR.
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SAR has shown significant benefits over traditional remote sensing techniques but comes with additional complexities.
“Although SAR has shown significant benefits over traditional remote sensing techniques, it comes with additional complexities [147].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [147]
Cites
No references could be resolved yet.
Research works printed but not linked: 14
- schwegmann, 2017, Subsidence feature discrimination using deep convolutional neural networks in Synthetic Aperture Radar Imagery, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1
- 10.1109/LGRS.2016.2618840 · DOI
- bentes, 2016, Ship-Iceberg Discrimination with Convolutional Neural Networks in High Resolution SAR Images, European Conference on Synthetic Aperture Radar (EUSAR), 1
- 10.1109/JSTARS.2017.2670083 · DOI
- schwegmann, 2017, Synthetic Aperture Radar ship discrimination, generation and latent variable extraction using information maximizing generative adversarial networks, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 1
- 10.1109/JSTARS.2015.2417756 · DOI
- 10.1109/IGARSS.2013.6723076 · DOI
- 10.1109/LGRS.2016.2631638 · DOI
- 10.1109/36.285193 · DOI
- 10.1109/IGARSS.2016.7729017 · DOI
- goodfellow, 2016, Deep Learning
- 10.1109/LGRS.2015.2478256 · DOI
- crisp, 2004, The State-of-the-Art in Ship Detection in Synthetic Aperture Radar Imagery, DSTO Information Sciences Laboratory
- 10.1109/TGRS.2016.2551720 · DOI
Cited by
Sorted by citations
Applications and Challenges of Artificial Intelligence in Space Missions
Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68 · see citing works
“…color and textural variations and the limitations of traditional remote sensing using optical sensors, Synthetic Aperture Radar (SAR) imaging, an alternate form of remote sensing, permits the observation of large target areas during day or night under almost any weather condition [145], [146].”
Background
- Authors
- Schwegmann, C. P., Kleynhans, W., & Salmon, B. P.
- Affiliations
- Council for Scientific and Industrial Research (ZA)
- University of Tasmania (AU)
- Venue
- 2017 International Workshop on Remote Sensing with Intelligent Processing (RSIP)
- Publisher
- IEEE
- Published
- 2017-05
- Pages
- 1-2
- OpenAlex
- W2703516764
- Cited by
- 10
- References
- 0
- Citations by year
- 2025: 2 · 2023: 1 · 2021: 2 · 2020: 1 · 2019: 2 · 2018: 2
- FWCI
- 2.39
- Type
- conference paper
- Language
- en
- Open access
- green
- figshare.com
- Licence
- cc-by
- Locations
- International Workshop on Remote Sensing with Intelligent Processing (RSIP) (publishedVersion)
- eCite Digital Repository (University of Tasmania) (submittedVersion)
- UTAS Research Repository (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Advanced SAR Imaging Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Remote-Sensing Image Classification
- Physical Sciences › Earth and Planetary Sciences › Geophysics › Synthetic Aperture Radar (SAR) Applications and Techniques
- Keywords
- synthetic aperture radar, synthetic aperture radar images, deep learning, remote sensing, machine learning, feature extraction, SAR image analysis
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