POLINSAR DATA PROCESSING WITH RAT (RADAR TOOLS)
Maxim Neumann, Andreas Reigber, Stéphane Guillaso, Marc Jäger and Olaf Hellwich
Technical University Berlin, Department of Computer Vision and Remote Sensing Franklinstrasse 28/29, FR 3-1, D-10587 Berlin, Germany
Email: {maxn, anderl, stephane, jaeger, hellwich} @cs.tu-berlin.de
ABSTRACT
Polarimetric SAR Interferometry (PolInSAR) is an up–to–date research topic in SAR image data processing. This field undergoes extensive development and as of now no software is publicly available. This paper summarizes the first efforts taken to include PolInSAR data handling in the RAT (Radar Tools) framework. The main purpose of such extension is to provide basic PolInSAR capabilities in RAT, complementing the already existing polarimetric and interferometric modules.
RAT is a collection of tools for advanced image processing of SAR remote sensing data, originally started as a student’s project and currently under further development at the Department of Computer Vision and Remote Sensing of the Tech-nical University of Berlin. It is programmed in IDL (Interactive Data Language) and uses IDL widgets as graphical user interface. The purpose of this paper is also to give an overview of the current development status of RAT through ad-dressing the newest structural improvements in RAT as well as recently implemented methods for SAR polarimetry and interferometry.
1
INTRODUCTION
In recent years, the usage of synthetic aperture radar (SAR) data became more and more popular and is used in many scientific fields. Several new and promising algorithms are described in literature. However, remote sensing software like Erdas Image or ENVI include only some basic and very well-established SAR functionality. Advanced algorithms often have to be implemented by oneself, with the consequence that in many cases they’re almost exclusively used by their respective developer.
The main motivation for RAT is to offer some kind of experimental platform for advanced SAR image processing. This is achieved by providing a software with basic SAR data handling functionality, like data import and export, as well SAR specific preprocessing and display functions. The programming interface of RAT is kept simple and adding new function-ality is quite easy. Function templates and a step-by-step description of how to program a RAT module is included in the distribution. In this way, everybody who is interested might use RAT as framework for his own, possibly experimental, developments. In addition, RAT can be used to better promote own algorithms to the public. Even smaller developments, made for example in the frame of diploma thesis, can be implemented as a RAT module and easily distributed to a wider audience.
The second motivation of this software package is a better distribution of modern SAR algorithms to the base of non-expert users. Application oriented research laboratories are in many cases dependent on the limited offerings of com-mercial software packages. Additionally, complex multidimensional SAR data is often found to be hard to handle by non-SAR-experts. RAT tries to simplify the handling of such data by simple menu-driven functions and a visual feedback of all functions on the screen. This should allow also non-experts to benefit from RAT and from SAR data in general In [8] the basic concept of RAT is introduced and the initially implemented methods in single channel, polarimetric and interferometric modules are presented. Since then, the structure of RAT was in continuous development and the available modules were extended by different new methods. These improvements are presented in the following section.
The major milestone is the expansion of RAT by the new module to handle PolInSAR data. The combination of SAR polarimetry and SAR interferometry contains high potential for future research work. The newest module implements low–level and mid–level functions to handle PolInSAR datasets. These methods are described in section 3.
RAT can be downloaded and used free of charge [2]. Additionally, RAT is open-source software. This means that its complete source-code is available to everybody who is interested. The source code can be modified, corrected and even used for other projects (for details please read license). The terms of use and the installation requirements are mentioned in section 4. However, it is important to note that RAT is not a professional development. It is generally seen as an experimental project, so there will be always bugs and wrong or not working modules, and no guarantee can be given for any of its functions.
2
CURRENT STATE OF THE RAT FRAMEWORK
The concept of RAT is to provide a generic SAR image handling platform, which is easy to understand and to extend by own functions. Basically, the core module of RAT contains GUI routines and functions for data handling and optimized display for many typical SAR data type representations, like for example complex multichannel images, decompositions, segmentations, etc.
Since the first paper on RAT [8], many new routines were added and modified, not even counting the PolInSAR module. Furthermore, the RAT–specific file format has been extended. A small preview image is additionally saved with the data file for faster loading in future sessions. This allows to have a view at the content without the necessity to load the entire data file. For future compatibility, a few empty flags were also added. This RAT–file–format is naturally still compatible to former formats. New feature is also a toolbox, which allows comfortable view on single channels and custom combi-nation of channels. The handy data management tool helps to keep the overview over possibly big amount of different files. RAT can import the native formats of DLR’s E-SAR sensor and ENVISAT-ASAR data. Additionally, RAT supports im-port of POLSARPRO and RSI-ENVI format, generic binary, and generic pixmap formats (png, jpeg and tiff). Exim-port possibilities exist to RSI-ENVI, generic binary as well as png, jpg & tiff. With different files in the RAT specific format RAT can construct InSAR, PolSAR and PolInSAR dataset. Generic methods for any data representation type are: binary transformations; mirror transformations; image cutting, pre-summing and resizing; number of looks calculation; zoom; value measure and single channel extraction.
The single channel SAR module contains numerous functions for conducting speckle filtering and edge detection, as well as routines for the analysis of spectrum, texture, point– and distributed– targets. The SAR image can be projected from the slant range into ground range; and the weighting functions can be applied or removed in the spectrum.
The module for handling polarimetric data is already well developed. It has a big variety on decompositions and classi-fications. They are based on, among others, Entropy/Alpha/Anistropy, Moriyama, Wishart, Freeman–Durden, etc. This module also includes methods for speckle filtering, basis transformation, calibration, polarimetric CFAR edge detection, polarimetric target analysis.
The interferometric part of RAT is in continuously development and enhancement. For the moment it contains different functions for coregistration, filtering the spectral range and for removing the flat earth phase. Also the user can estimate the coherence, to filter phase noise, and to conduct phase unwrapping. As a matter of course different methods are implemented for every of these routines.
3
POLINSAR IN RAT
Polarimetric SAR interferometry (PolInSAR) is, as implied by the name, the combination of SAR polarimetry (PolSAR) and SAR interferometry (InSAR). The newly developed PolInSAR–module provides a framework for PolInSAR image data processing. This framework offers, on the one hand, the necessary basic functionality and, on the other hand, already implements some recently developed and very promising methods.
A PolInSAR dataset is constructed from two already coregistrated polarimetric data sets. The default presentation form of each position is a set of two scattering vectors k1and k2, which are equivalent to their Sinclair matrices. This set can also be considered a single polarimetric interferometric feature vector k = (kT1, kT
2)T. For the moment, only the most important Pauli decomposition is implemented. From the polin–vector, the covariance matrix C and the coherence matrix T can be
lexicographic pol.-in.
scattering vector scattering vectorpauli pol.-in. interferometric
covariance matrix coherence matrixinterferometric
Speckle Filter
- Boxcar - Lee - Refined Lee
Flat Earth Removal
- Linear - From file Spectral Filter** - Range standard - Range adaptive Coherence - Estimation by Boxcar
- Estimation by Region Growing - Optimization/Decomposition - ESPRIT Coherence Phases
Single Point Coherence Analysis
General
- Resize, Pre-summing - Cut out, Zoom
- Mirror horizontal/vertical - Transform to ampl./phase - Number of looks estimation
Extract
- Interferogram - SAR image - PolSAR image - InSAR image
* Reconstruction of scattering vector from matrix without the absolute phase **Before or instead of flat earth removal
* *
Figure 1: Diagram of possible program flow in PolInSAR–RAT.
constructed. The inverse transformation to the scattering vectors is only possible with the loss of the absolute phase. All other methods are based on these representation forms as shown in the diagram in Fig. 1.
These specific methods for image data processing in detail are:
Range Spectral Filter For spectral filtering there are two possibilities implemented. One way is the standard filtering of
the wavenumber shift. If a flat earth phase file is given, also an adaptive range spectral filter can be applied, which has a smaller loss of resolution [5, 7]. To note is that the adaptive spectral filtering should be applied before the flat earth removal. Optionally, the flat earth can be removed during the spectral filtering.
Flat Earth Removal A linear flat earth phase can be easily removed when processing satellite SAR images. For the
airborne case the linear flat earth removal is not sufficient and an additional possibility to remove the flat earth phase with a given (precomputed) file is possible.
Speckle Filters Implemented are three basic speckle filters: Boxcar, Lee filter and Lee refined filter [6]. (Fig. 2.a) Interferogram Generation of a polarimetric interferogram. (Fig. 2.b)
Coherence Estimation The polarimetric complex coherence can be easily and quickly estimated by boxcar. Another
im-plemented approach is based on region growing simultaneously along all polarimetric and interferometric channels [10]. This method delivers better results, but requires more computational work. (Compare Fig. 3.a and 3.b)
Coherence Optimization Different methods are implemented to optimize or rather decompose the coherence. The first
and most popular, the two–scattering–mechanisms method, optimizes the amplitudes of the complex coherence [3]. Not really coherence optimiztion, but optimization of the phases of the complex coherence (equivalent to the interferometric phase) is the next approach. This method is based on ESPRIT [9] and returns the topographic phases of dominant scatterers [11].
Single Point Coherence Analysis This is a tool to analyze the polarimetric complex coherence for one single position.
Different types of coherences can be drawn into the complex coherence circle (random, lexicographic, Pauli, 2– mech. scat. optimization, ESPRIT and custom). The boundary shape of the coherence cloud can also be ap-proximately determined with the algorithm from [4]. Through modification of smoothing kind (boxcar, quadratic,
(a) Refined Lee Speckle filter (b) Polarimetric Interferogram
Figure 2: Basic methods in RAT–PolInSAR module.
cube), smoothing box-size (1–100 pixels) and some other coherence type specific parameters one can observe the change in positions and shapes of coherences and random coherence clouds. (Fig.3.c). It is also possible to limit the projection vectors for random coherence generation to study basic shapes of the complex coherences, which are defined in the covariance and the coherence matrices (Fig. 3.d). Another feature is the possibility to fit a line through coherences of any type. Some more options for visualization complete this tool.
Furthermore, general functions can be applied to PolInSAR–datasets as described in [8] and in section 2, for example, to estimate the number of looks, to conduct pre-summing, etc. Finally, to be able to use any specific method from other modules, a single channel SAR image, or a polarimetric, or even an interferometric dataset can be extracted from the multi–dimensional PolInSAR–dataset.
4
TERMS OF USE
RAT is available on the web and distributed under a free software license [2]. The distribution includes its complete source-code, as well as a binary version, which can be executed using the free IDL virtual machine [1] under various operating systems, including Linux, UNIX and MacOS-X. The main development platform is Linux. It is tried to support Windows, too. This is quite easy using IDL and in general RAT on Windows should work fine. However, testing is done on Linux and there might be some problems left when running RAT under Windows.
RAT is open-source software and can be used free of charge. However, in order to get some feedback about its user base, it is distributed as “postcard–ware”. This means that one may copy and use it as much as one likes, but that it is required to register each copy of the software by simply sending a postcard to the RAT team (local motives preferred). The address is:
RAT Team, Technical University of Berlin Dep. of Computer Vision and Remote Sensing Franklinstrasse 28/29, FR 3–1
(a) Boxcar coherence estimation (b) Region growing coherence estimation
(c) Single point complex coherence analysis applied to a single (multi–look) point in the forest.
(d) Random coherences
For details of the license, particularly concerning redistribution and usage of the RAT source-code in commercial software projects, please have a look at the complete license agreement at the RAT web site.
Generally, RAT is planned as an open project, and external contributions are welcome. Anybody, who likes to promote his own special algorithms by adding it to RAT, is encouraged to contact the authors for more information.
5
SUMMARY & OUTLOOK
RAT is a free tool for advanced SAR image processing, trying to include many modern algorithms, typically not available in commercial remote sensing software. It is mainly intended to be an experimental platform, where anybody can imple-ment and test new SAR methods, and, if desired, provided them to the scientific community.
The current implementation of PolInSAR capabilities is still very basic. Up to now, the main work was put in establishing the principle infrastructure for handling PolInSAR data. Still completely missing are model based inversion techniques, as well as PolInSAR classification and decomposition techniques. In future, the core development team at the Technical University Berlin will therefore concentrate in further improving and testing the RAT framework for PolInSAR process-ing. Additionally, it is planned to develop new functionality in the frame of student projects, graduation and PhD theses. Another interesting topic, for which plans to address it in RAT exists, are image recognition techniques. This includes shape and object based methods, as well as advanced image clustering and segmentation techniques.
Finally, we would like to thank our students who contributed up to now to the development of RAT, namely Thomas Weser, Guido Bethke, Andre Lehmann, Bert Wolf, Nicole Bouvier, Mathias Weller and Oliver Bach. This work was also supported by the German Research Foundation (DFG) under project number RE 1698/2.
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