scholarly journals Use of Synthetic Aperture Radar in Finescale Surface Analysis of Synoptic-Scale Fronts at Sea

2005 ◽  
Vol 20 (3) ◽  
pp. 311-327 ◽  
Author(s):  
G. S. Young ◽  
T. N. Sikora ◽  
N. S. Winstead
Keyword(s):  
North America ◽  
Synthetic Aperture Radar ◽  
East Coast ◽  
Gulf Of Alaska ◽  
Data Sources ◽  
Synthetic Aperture ◽  
Synoptic Scale ◽  
Sar Images ◽  
Cold Fronts ◽  
Aperture Radar

Abstract The viability of synthetic aperture radar (SAR) as a tool for finescale marine meteorological surface analyses of synoptic-scale fronts is demonstrated. In particular, it is shown that SAR can reveal the presence of, and the mesoscale and microscale substructures associated with, synoptic-scale cold fronts, warm fronts, occluded fronts, and secluded fronts. The basis for these findings is the analysis of some 6000 RADARSAT-1 SAR images from the Gulf of Alaska and from off the east coast of North America. This analysis yielded 158 cases of well-defined frontal signatures: 22 warm fronts, 37 cold fronts, 3 stationary fronts, 32 occluded fronts, and 64 secluded fronts. The potential synergies between SAR and a range of other data sources are discussed for representative fronts of each type.

scholarly journals On the Origin of Atmospheric Frontal Lines off the East Coast of Taiwan Observed on Spaceborne Synthetic Aperture Radar Images

2010 ◽  
Vol 138 (2) ◽  
pp. 475-496 ◽  
Author(s):  
Werner Alpers ◽  
Jen-Ping Chen ◽  
Chia-Jung Pi ◽  
I-I. Lin
Keyword(s):  
Synthetic Aperture Radar ◽  
East Coast ◽  
Physical Mechanism ◽  
Mesoscale Model ◽  
Synthetic Aperture ◽  
Katabatic Wind ◽  
Mountain Range ◽  
Sar Images ◽  
Radar Images ◽  
Aperture Radar

Abstract Frontal lines having offshore distances typically between 40 and 80 km are often visible on synthetic aperture radar (SAR) images acquired over the east coast of Taiwan by the European Remote Sensing Satellites 1 and 2 (ERS-1 and ERS-2) and Envisat. In a previous paper the authors showed that they are of atmospheric and not of oceanic origin; however, in that paper they did not give a definite answer to the question of which physical mechanism causes them. In this paper the authors present simulations carried out with the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model, which shows that the frontal lines are associated with a quasi-stationary low-level convergence zone generated by the dynamic interaction of onshore airflow of the synoptic-scale wind with the coastal mountain range of the island of Taiwan. Reversed airflow collides with the onshore-flowing air leading to an uplift of air, which is often accompanied by the formation of bands of increased cloud density and of rainbands. The physical mechanism causing the generation of the frontal lines is similar to the one responsible for the formation of cloud bands off the Island of Hawaii as described by Smolarkiewicz et al. Four SAR images are shown, one acquired by ERS-2 and three by Envisat, showing frontal lines at the east coast of Taiwan caused by this generation mechanism. For these events the recirculation pattern, as well as the frontal (or convective) lines observed, were reproduced quite well with the meteorological model. So, it is argued that the observed frontal lines are not seaward boundaries of (classical) barrier jets or of katabatic wind fields, which have characteristics that are quite different from the flow patterns around the east coast of Taiwan as indicated by the SAR images.

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.

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 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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