Synthetic Aperture Radar (SAR) Coordinate Processing System

1981 ◽  
Author(s):  
David E. Rogers ◽  
Thomas H. Heimburger
Keyword(s):  
Synthetic Aperture Radar ◽  
Processing System ◽  
Synthetic Aperture ◽  
Coordinate Processing ◽  
Aperture Radar

Synthetic aperture radar processing system for search and rescue

1997 ◽  
Author(s):  
Barton D. Huxtable ◽  
Christopher R. Jackson ◽  
Arthur W. Mansfield ◽  
Houra Rais
Keyword(s):  
Synthetic Aperture Radar ◽  
Processing System ◽  
Search And Rescue ◽  
Synthetic Aperture ◽  
Aperture Radar

scholarly journals Système de traitement automatisé du gouvernement canadien pour la détection des variations et l'analyse des déformations du sol à partir des données de radar à synthèse d'ouverture de RADARSAT-2 et de la mission de la Constellation RADARSAT : description et guide de l'utilisateur

2021 ◽  
Author(s):  
J P Dudley ◽  
S V Samsonov
Keyword(s):  
Synthetic Aperture Radar ◽  
Ground Deformation ◽  
Processing System ◽  
Ground Movement ◽  
Automated System ◽  
Anthropogenic Sources ◽  
Synthetic Aperture ◽  
Advanced Analysis ◽  
Sar Data ◽  
Aperture Radar

Remote sensing using Synthetic Aperture Radar (SAR) offers powerful methods for monitoring ground deformation from both natural and anthropogenic sources. Advanced analysis techniques such as Differential Interferometric Synthetic Aperture Radar (DInSAR), change detection, and Speckle Offset Tracking (SPO) provide sensitive measures of ground movement. With both the RADARSAT-2 and RADARSAT Constellation Mission (RCM) SAR satellites, Canada has access to a significant catalogue of SAR data. To make use of this data, the Canada Centre for Mapping and Earth Observation (CCMEO) has developed an automated system for generating standard and advanced deformation products from SAR data using both DInSAR and SPO methods. This document provides a user guide for this automated processing system.

scholarly journals Oil Slick Characterization Using a Statistical Region-Based Classifier Applied to UAVSAR Data

2019 ◽  
Vol 7 (2) ◽  
pp. 36
Author(s):  
Patrícia Genovez ◽  
Cathleen Jones ◽  
Sidnei Sant’Anna ◽  
Corina Freitas
Keyword(s):  
Synthetic Aperture Radar ◽  
Oil Recovery ◽  
Oil Spills ◽  
Single Channel ◽  
Processing System ◽  
Synthetic Aperture ◽  
Source Image ◽  
Oil Slick ◽  
Convergence Zones ◽  
Aperture Radar

During emergency responses to oil spills on the sea surface, quick detection and characterization of an oil slick is essential. The use of Synthetic Aperture Radar (SAR) in general and polarimetric SAR (PolSAR) in particular to detect and discriminate mineral oils from look-alikes is known. However, research exploring its potential to detect oil slick characteristics, e.g., thickness variations, is relatively new. Here a Multi-Source Image Processing System capable of processing optical, SAR and PolSAR data with proper statistical models was tested for the first time for oil slick characterization. An oil seep detected by NASA`s Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) in the Gulf of Mexico was used as a study case. This classifier uses a supervised approach to compare stochastic distances between different statistical distributions (fx) and hypothesis tests to associate confidence levels to the classification results. The classifier was able to detect zoning regions within the slick with high global accuracies and low uncertainties. Two different classes, likely associated with the thicker and thinner oil layers, were recognized. The best results, statistically equivalent, were obtained using different data formats: polarimetric, intensity pair and intensity single-channel. The presence of oceanic features in the form of oceanic fronts and internal waves created convergence zones that defined the shape, spreading and concentration of the thickest layers of oil. The statistical classifier was able to detect the thicker oil layers accumulated along these features. Identification of the relative thickness of spilled oils can increase the oil recovery efficiency, allowing better positioning of barriers and skimmers over the thickest layers. Decision makers can use this information to guide aerial surveillance, in situ oil samples collection and clean-up operations in order to minimize environmental impacts.

Synthetic Aperture Radar: From Signal to Image

2005 ◽  
Vol 277-279 ◽  
pp. 799-804
Author(s):  
Sunghee Kwak ◽  
Youngran Lee ◽  
Dong Seok Shin ◽  
Won Kyu Park
Keyword(s):  
Synthetic Aperture Radar ◽  
Optical Sensors ◽  
Processing System ◽  
Synthetic Aperture ◽  
Data Sets ◽  
Essential Requirement ◽  
Ground Station ◽  
Ground System ◽  
Sar Processing ◽  
Aperture Radar

Synthetic aperture radar (SAR) provides information different from those gathered by optical sensors and it acquires data day or night regardless of cloud cover through the microwave spectrum. The SAR processing system is an essential requirement in every ground station in order to properly interpret and fully utilize the information contained in these data sets. This paper describes the processing algorithms that generate images from the SAR signal and proposes the application of SAR ground station development to satisfy such requirements. A SAR ground system is implemented using those algorithms. It generates SAR image products according to processing levels. Experiments conducted in this paper show results comparable to the commercial SAR processing software.

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