Tropical Cyclone Parameters Derived from Synthetic Aperture Radar (SAR) Images

2006 ◽  
Author(s):  
A. Reppucci ◽  
S. Lehner ◽  
J. Schulz-Stellenfleth
Keyword(s):  
Tropical Cyclone ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Aperture Radar

scholarly journals A Novel Orthogonal Waveform Separation Scheme for Airborne MIMO-SAR Systems

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3580 ◽  
Author(s):  
Jie Wang ◽  
Ke-Hong Zhu ◽  
Li-Na Wang ◽  
Xing-Dong Liang ◽  
Long-Yong Chen
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Synthetic Aperture Radar ◽  
3D Imaging ◽  
Synthetic Aperture ◽  
Separation Scheme ◽  
Sar Images ◽  
Wide Swath ◽  
Aperture Radar

In recent years, multi-input multi-output (MIMO) synthetic aperture radar (SAR) systems, which can promote the performance of 3D imaging, high-resolution wide-swath remote sensing, and multi-baseline interferometry, have received considerable attention. Several papers on MIMO-SAR have been published, but the research of such systems is seriously limited. This is mainly because the superposed echoes of the multiple transmitted orthogonal waveforms cannot be separated perfectly. The imperfect separation will introduce ambiguous energy and degrade SAR images dramatically. In this paper, a novel orthogonal waveform separation scheme based on echo-compression is proposed for airborne MIMO-SAR systems. Specifically, apart from the simultaneous transmissions, the transmitters are required to radiate several times alone in a synthetic aperture to sense their private inner-aperture channels. Since the channel responses at the neighboring azimuth positions are relevant, the energy of the solely radiated orthogonal waveforms in the superposed echoes will be concentrated. To this end, the echoes of the multiple transmitted orthogonal waveforms can be separated by cancelling the peaks. In addition, the cleaned echoes, along with original superposed one, can be used to reconstruct the unambiguous echoes. The proposed scheme is validated by simulations.

scholarly journals Tropical Cyclone Winds Retrieved From Synthetic Aperture Radar

Oceanography ◽  
2013 ◽  
Vol 26 (2) ◽  
Author(s):  
Jochen Horstmann ◽  
Christopher Wackerman ◽  
Silvia Falchetti ◽  
Salvatore Maresca
Keyword(s):  
Tropical Cyclone ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Aperture Radar

Co-seismic landslide detection after M 7.4 earthquake on June 23, 2020, in Oaxaca, Mexico, based on rapid mapping method using high and medium resolution synthetic aperture radar (SAR) images

Landslides ◽  
2021 ◽  
Author(s):  
Norma Davila Hernandez ◽  
Alexander Ariza Pastrana ◽  
Lizeth Caballero Garcia ◽  
Juan Carlos Villagran de Leon ◽  
Antulio Zaragoza Alvarez ◽  
...  
Keyword(s):  
Synthetic Aperture Radar ◽  
Mapping Method ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Rapid Mapping ◽  
Medium Resolution ◽  
Seismic Landslide ◽  
Aperture Radar

scholarly journals Tropical Cyclone Winds Retrieved From C-Band Cross-Polarized Synthetic Aperture Radar

2015 ◽  
Vol 53 (5) ◽  
pp. 2887-2898 ◽  
Author(s):  
Jochen Horstmann ◽  
Silvia Falchetti ◽  
Christopher Wackerman ◽  
Salvatore Maresca ◽  
Michael J. Caruso ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture ◽  
Aperture Radar

scholarly journals RFI Artefacts Detection in Sentinel-1 Level-1 SLC Data Based On Image Processing Techniques

Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2919 ◽  
Author(s):  
Agnieszka Chojka ◽  
Piotr Artiemjew ◽  
Jacek Rapiński
Keyword(s):  
Image Processing ◽  
Synthetic Aperture Radar ◽  
Nearest Neighbor ◽  
Synthetic Aperture ◽  
Interferometric Synthetic Aperture Radar ◽  
Sar Images ◽  
Permanent Scatterers ◽  
Level 1 ◽  
Processing Techniques ◽  
Aperture Radar

Interferometric Synthetic Aperture Radar (InSAR) data are often contaminated by Radio-Frequency Interference (RFI) artefacts that make processing them more challenging. Therefore, easy to implement techniques for artefacts recognition have the potential to support the automatic Permanent Scatterers InSAR (PSInSAR) processing workflow during which faulty input data can lead to misinterpretation of the final outcomes. To address this issue, an efficient methodology was developed to mark images with RFI artefacts and as a consequence remove them from the stack of Synthetic Aperture Radar (SAR) images required in the PSInSAR processing workflow to calculate the ground displacements. Techniques presented in this paper for the purpose of RFI detection are based on image processing methods with the use of feature extraction involving pixel convolution, thresholding and nearest neighbor structure filtering. As the reference classifier, a convolutional neural network was used.

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