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F-SAR Data Formats

Operational F-SAR processing chains produce a variety of different data products. The most common product types include:

RGI Focused SAR data in radar geometry
GTC Geocoded SAR data
INF Co-registered image stacks for interferometry

Other data products include SAR imagery from circular acquisitions, polarimetric change detection results, geocoded amplitude mosaics and more.

The structure and contents of these data products is described in the F-SAR Product Description document.

The binary data in F-SAR products is generally stored in the so-called RAT format. In addition, most RAT files are accompanied by .hdr files, which allow the data to be opened in ENVI. The widely used GDAL software library also supports the ENVI format, such that F-SAR data can also be opened using free alternatives such as QGis. Any of these programs can then be used to export F-SAR data to various other file formats, such as GeoTiff.

Native RAT/XML Interface

Researchers developing their own SAR signal processing techniques on the basis of F-SAR data may find it more convenient to read the binary in F-SAR products directly. A direct interface to the data may even be required when the application under consideration involves working with complex data or sensor tracks, as neither data type is well supported by GIS software.

A native interface to F-SAR data is available as part of the PyRAT radar signal processing framework. Researchers may want to consider using PyRAT as a basis for implementing new signal processing algorithms. Alternatively, the F-SAR RAT and XML interface can be downloaded and used separately.

The following basic usage examples assume that ste_io.py has been downloaded and that the python interpreter has been launched in the directory containing the downloaded package.

The rrat function reads the contents of RAT files into NumPy arrays:

	
			Python 3.4.1 (default, May 23 2014, 17:48:28) [GCC] on linux

			Type "help", "copyright", "credits" or "license" for more information.
		
			>>> from ste_io import rrat 
			# import the rrat function
		
			>>> path_to_slc = '...' 
			# provide path to a slc*.rat file here!
		
			>>> slc = rrat(path_to_slc)
			# read the slc
		
			>>> type(slc)
			# contents are returned as a numpy array
		
			<class 'numpy.ndarray'>
			 
			>>> slc.dtype
			# slc data are complex
		
			dtype('complex64')
			 
			>>> slc.shape
			# approx. 20k rows (azimuth), 4k columns (range)
		
			(19968, 3772)

The rrat function also supports reading blocks of data. The following example reads a block of data spanning rows 1000 to 1199 and columns 500 to 799 of the full array on disk:

	
			>>> from ste_io import rrat
			 
			>>> path_to_slc = '...'
			# provide path to a slc*.rat file here!
		
			>>> slc = rrat(path_to_slc, block=[1000,1200,500,800])
		
			>>> slc.shape
			 
			(200, 300)

The RatFormat object can be used to access the metadata in the header of RAT files. The structure of the RAT header is detailed in appendix 2 of the F-SAR Product Description. The following example extracts some of the metadata related to the UTM grid used for a geocoded amplitude image:

	
			>>> from ste_io import RatFile
			 
			>>> path_to_ampgeo = '...'
			# provide path to an ampgeo*.rat file here!
		
			>>> header = RatFile(path_to_ampgeo).Header
		
			>>> header.Geo.zone
			# UTM zone
		
			32
			 
			>>> header.Geo.min_east
			# minimum easting coordinate
		
			563199.9585834567
			 
			>>> header.Geo.ps_east
			# pixel spacing in easting
		
			2.0

In addition, the ste_io.py package defines the Xml2Py class, which facilitates object-like access to parameters stored in the pp*.xml files of the RDP product components. The following example reads a pp file from the RGI-RDP product component and illustrates how parameter values are conveniently accessed as python object properties:

	
			>>> from ste_io import Xml2Py
			 
			>>> path_to_pp = '...'
			# provide path of a pp*.xml file here!
		
			>>> pp = Xml2Py(path_to_pp)
			# read the XML parameter file
		
			>>> print('Sensor velocity: %f m/s'%(pp.v0))
		
			Sensor velocity: 102.897640 m/s
			 
			>>> type(pp.v0)
			 
			<class 'numpy.float64'>
			 
			>>> print('First 5 elements of range vector:', pp.r[:5])
		
			First 5 elements of range vector:

			[ 6445.1437 6446.3425 6447.5414 6448.7403 6449.9391]
		
			>>> type(pp.r)
			 
			<class 'numpy.ndarray'>

Multimodal Algorithms


E-SAR und F-SAR Bildergalerie


Flyer F-SAR (current sensor, in operation)

Flyer E-SAR (out of operation)

F-SAR Data Format Description

Performance of 3-D Surface Deformation Estimation for Simultaneous Squinted SAR Acquisitions

Strukturmechanik

Kommunikationstechnik und Radar