Comparison of interferometric SAR data with a digital elevation map over large relief terrain

1993 ◽  
Author(s):  
Qian Lin ◽  
John F. Vesecky ◽  
Howard A. Zebker
Keyword(s):  
Digital Elevation ◽  
Digital Elevation Map ◽  
Sar Data ◽  
Interferometric Sar

Extraction of linear objects from interferometric SAR data

2002 ◽  
Vol 23 (3) ◽  
pp. 461-475 ◽  
Author(s):  
Olaf Hellwich ◽  
Ivan Laptev ◽  
Helmut Mayer
Keyword(s):  
Sar Data ◽  
Interferometric Sar

Get HyP3! SAR processing for everyone

2021 ◽  
Author(s):  
Joseph H. Kennedy ◽  
Krik Hogenson ◽  
Andrew Johnston ◽  
Heidi Kristenson ◽  
Alex Lewandowski ◽  
...  
Keyword(s):  
Primary Data ◽  
The Public ◽  
Processing Pipeline ◽  
Area Of Interest ◽  
Global Coverage ◽  
Sar Data ◽  
New Processing ◽  
Interferometric Sar ◽  
Sar Processing ◽  
Detection Processing

<p>Synthetic Aperture Radar (SAR), with its capability of imaging day or night, ability to penetrate dense cloud cover, and suitability for interferometry, is a robust dataset for event/change monitoring. SAR data can be used to inform decision makers dealing with natural and anthropogenic hazards such as floods, earthquakes, deforestation and glacier movement. However, SAR data has only recently become freely available with global coverage, and requires complex processing with specialized software to generate analysis-ready datasets. Furthermore, processing SAR is often resource-intensive, in terms of computing power and memory, and the sheer volume of data available for processing can be overwhelming. For example, ESA's Sentinel-1 has produced ~10PB of data since launch in 2014. Even subsetting the data to a small scientific area of interest can result in many thousands of scenes, which must be processed into an analysis-ready format.</p><p>The Alaska Satellite Facility (ASF) Hybrid Pluggable Processing Pipeline (HyP3), which is now out of beta and available to the public, provides custom, on-demand processing of Sentinel-1 SAR data at no cost to users. HyP3 is integrated directly into Vertex, ASF's primary data discovery tool, so users can easily select an area of interest on the Earth, find available SAR products, and click a button to send them (individually, or as a batch) to HyP3 for Radiometric Terrain Correction (RTC), Interferometric SAR (InSAR), or Change Detection processing. Processing leverages AWS cloud computing and is done in parallel for rapid product generation. Each process provides options to customize the processing and final output products, and provides metadata-rich, analysis-ready final products to users.</p><p>In addition to the Vertex user interface, HyP3 provides a RESTful API and a python software developers kit (SDK) to allow programmatic access and the ability to build HyP3 into user workflows. HyP3 is open source and designed to allow users to develop new processing plugins or stand up their own custom processing pipeline.</p><p>We will present an overview of using HyP3, both inside Vertex and programmatically, and the available output products. We will demonstrate using HyP3 to investigate the consequences of natural hazards and very briefly discuss the technologies and software design principles used in the development of HyP3 and how users could contribute new plugins, or stand up their own custom processing pipeline.</p>

scholarly journals Evaluation of Atmospheric Effects on Interferograms Using DEM Errors of Fixed Ground Points

Sensors ◽  
2018 ◽  
Vol 18 (7) ◽  
pp. 2336 ◽  
Author(s):  
Takashi Nonaka ◽  
Tomohito Asaka ◽  
Keishi Iwashita
Keyword(s):  
Phase Noise ◽  
Atmospheric Effects ◽  
Sar Images ◽  
Single Pass ◽  
Digital Elevation ◽  
Total Phase ◽  
Disaster Monitoring ◽  
The Difference ◽  
Elevation Model ◽  
Interferometric Sar

High-resolution synthetic aperture radar (SAR) data are widely used for disaster monitoring. To extract damaged areas automatically, it is essential to understand the relationships among the sensor specifications, acquisition conditions, and land cover. Our previous studies developed a method for estimating the phase noise of interferograms using several pairs of TerraSAR-X series (TerraSAR-X and TanDEM-X) datasets. Atmospheric disturbance data are also necessary to interpret the interferograms; therefore, the purpose of this study is to estimate the atmospheric effects by focusing on the difference in digital elevation model (DEM) errors between repeat-pass (two interferometric SAR images acquired at different times) and single-pass (two interferometric SAR images acquired simultaneously) interferometry. Single-pass DEM errors are reduced due to the lack of temporal decorrelation and atmospheric disturbances. At a study site in the city of Tsukuba, a quantitative analysis of DEM errors at fixed ground objects shows that the atmospheric effects are estimated to contribute 75% to 80% of the total phase noise in interferograms.

PolSARpro v2.0: The Versatile Polarimetric and Interferometric SAR Data Processing Toolbox

2006 ◽  
Author(s):  
E. Pottier ◽  
L. Ferro-Famil ◽  
S. Cloude ◽  
I. Hajnsek ◽  
K. Papathanassiou ◽  
...  
Keyword(s):  
Data Processing ◽  
Sar Data ◽  
Interferometric Sar
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