MULTIBASELINE POLINSAR MODULE FOR SAR DATA PROCESSING AND ANALYSIS IN RAT (RADAR TOOLS)

MULTIBASELINE POLINSAR MODULE FOR SAR DATA PROCESSING AND ANALYSIS IN RAT (RADAR TOOLS) Maxim Neumann 2,1, Andreas Reigber 1, Marc Jäger 1, Stéphane Guillaso 1 and Olaf Hellwich 1 1 Berlin University of Technology, Computer Vision and Remote Sensing Group, Franklinstrasse 28/29, Sekretariat FR3-1, D-10587 Berlin, Germany 2 IETR Laboratory, UMR CNRS 6164, University of Rennes 1 Campus de Beaulieu Bâtiment 11D 263 Avenue Général Leclerc, CS 74205 35042 Rennes Cedex, France Email: {maxn, anderl, jaeger, stephane, hellwich}@cs.tu-berlin.de Abstract The combination of SAR Polarimetry (POL- SAR) and SAR Interferometry (InSAR) into Polarimetric SAR Interferometry (POLInSAR) has shown great potential for information extraction from SAR data. Applications have been developed and validated theoretically for POLInSAR data. But due to different reasons these methods are difficult to apply on real data. The SAR observables have to be increased, and the utilization of multiple baselines (MB) is one of the possibilities. There will be a need for data processing and analysis methods and tools to work effectively with multibaseline datasets. In this paper we present the newly developed module for the software package RAT (Radar Tools), which provides these abilities for multibaseline polarimetric interferometric SAR data. It is the first available package of tools for working with MBSAR data. RAT (RAdar Tools [1], [2]) 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 Technical 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 addressing the newest structural improvements in RAT as well as recently implemented methods for SAR polarimetry and interferometry. I. INTRODUCTION In recent years, the usage of synthetic aperture radar (SAR) data, including polarimetric and interferometric 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 as SAR specific preprocessing and display functions. The programming interface of RAT is kept simple and adding new functionality 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 commercial 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. POLSARPro [3] is another free and excellent program, delivering advanced SAR data processing and analysis tools. However, the major difference between RAT and POLSARPro, not considering the technical and framework design details, is that RAT is more experimental and, thus, is easier extendable and can provide more cutting edge algorithms. At the same time this can be also seen as a small drawback, since being on the cutting edge implies less stability. In [1] and [2] the basic concepts of RAT are introduced and the initially implemented methods in single channel, polarimetric, interferometric and SB POLInSAR 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 topic of this paper is the expansion of RAT by the new module to handle multibaselines PolInSAR data. The combination of SAR polarimetry and multibaseline SAR interferometry contains high potential for future research work. The newest module implements low level and mid level functions for MB PolInSAR data processing and data analysis. RAT can be downloaded and used free of charge [4]. Additionally, most of the source code of RAT is freely

Fig. 1. Diagram of possible data processing steps in MB PolInSAR RAT. available. 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 IV. 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. II. 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. The rat specific data files (.rat) are saved in a flexible and extensible data file format. It allows to save multi dimensional data with data specific information and a small preview. The small preview image is saved 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 backwards compatible to former formats. A new feature is the possibility to save data specific parameters. This information is saved separately in an additional text file (.rit: RAT Information Text file). Saving this information as a text file provides the possibility to edit and extend the parameters with a simple text editor of choice without the necessity of using RAT for it, although this property is also provided. For the moment, among others, the following parameters can be set and used: number of polarizations, number of tracks, wavelength polarization basis: ellipticity and orientation resolution in azimuth, ground and slant range number of sub apertures in azimuth and range file paths for vertical wavenumber files, baseline files, incidence angle files, etc. The motivation to provide a framework for additional parameters is to allow the development of complex algorithms in the field of DEM generation and parameter inversion. Additionally, the history of data processing (called RAT file evolution) is automatically updated and saved with every step. Saved is the processing step description with the most important parameters and a time stamp. A real example is given below: 1) Construct multibaseline polarimetic SAR data with 3 tracks and 3 polarizations from 405.rat, 404.rat, 407.rat. TIMESTAMP: Tue Sep 5 23:15:29 2006 2) Multibaseline adaptive range spectral filtering. Cut 267 pixels im spectrum (from 1280). TIMESTAMP: Tue Sep 5 23:20:07 2006 3) Vector to matrix transform. presumming: 5 10 TIMESTAMP: Tue Sep 5 23:29:15 2006 4) Transform: C T TIMESTAMP: Tue Sep 5 23:43:33 2006 5) Speckle filtering (Polarimetric RefLee): boxsize3 looks: 30.0000 Threshold: 0.778151 Method: 0

Fig. 2. Multibaseline dataset after refined Lee speckle filtering. Fig. 3. Interferogram generation of the multibaseline dataset. TIMESTAMP: Mon Sep 11 11:48:23 2006 RAT can import the native formats of the E SAR, EMISAR, RAMSES, PI SAR, CONVAIR, ENVISAT ASAR, ALOS PALSAR, and RADARSAT2 sensors. Additionally, RAT supports import of POLSARPRO and RSI- ENVI format, generic binary, and generic pixmap formats (png, jpeg and tiff). Export possibilities exist to RSI-ENVI, generic binary as well as png, jpg & tiff. Additionally, RAT provides the possibility to import system information parameters from configuration files of E SAR and RAMSES sensors. With different files in the RAT specific format RAT can construct InSAR, POLSAR, SB POLInSAR, and MB POLInSAR datasets. The toolbox provides a comfortable view on single channels and custom combination of channels. The handy data management tool helps to keep the overview over possibly big amount of different files. 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; spectrum analysis; channel statistics 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 classifications. 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. III. MB POLINSAR IN RAT Multibaseline Polarimetric SAR interferometry (MB PolInSAR) is, as implied by the name, the combination of SAR polarimetry (PolSAR) and multibaseline SAR interferometry (MB InSAR). The presented module is the extension of the single baseline POLInSAR module, which has been presented in [2]. It provides a framework for MB PolInSAR image data processing and analysis. 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 MB PolInSAR dataset is constructed from multiple already coregistrated coherent polarimetric datasets. The default presentation form of each position is a set of

Fig. 4. Coherence estimation after speckle filtering with region growing methods. scattering vectors k1, k2,..., kn, which are equivalent to their Sinclair matrices. This set can also be considered as a single polarimetric interferometric feature vector k = (kt1, kt2,..., ktn )T. The covariance(c) and coherency(t) matrices of MB scattering vectors are obtained by multi looking, which can be done by different kinds of speckle filters or by presuming. " T11... T1n #...... T = kk = (1)... T 1n... Tnn The data can be represented in lexicographic or in Pauli matrix basis and can be transformed to any arbitrary polarization basis, given by the ellipticity and orientation angles of polarization ellipses. All methods of the MB RAT module are based on these representations, as shown in the diagram in Fig. 1. These specific methods for image data processing in detail are: a) 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], [6]. 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. Furthermore, dealing with multiple baselines, this filter is applied equally to all baselines and datasets, cutting out the same amount of the spectra. Fig. 5. Simultaneous coherence optimization over multiple baselines. b) 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 given (precomputed) files is possible. c) Topography Removal: Basic utility to obtain topography independent interferograms. d) Speckle Filters: Implemented are the basic fundamental speckle filters, including the boxcar filter, the Lee filter and the refined Lee filter (Fig. 2). Speckle filters built on advanced concepts like simulated annealing or region growing methods are also available for MB POLInSAR data. e) Interferogram: Generation of a polarimetric interferogram. (Fig. 3) f) Coherence Estimation: The polarimetric complex coherence can be easily and quickly estimated by boxcar. Another implemented approach is based on region growing simultaneously along all polarimetric and interferometric channels (Fig. 4). g) Coherence Optimization: Different methods are implemented to optimize multibaseline coherences. They can be divided into two groups: single baseline and simultaneous multibaseline coherence optimization [7], [8]. For both groups exist methods for optimizing coherences using multiple scattering mechanisms at baseline ends [9], as well as using equal scattering mechanisms [10]. h) Polarimetric Decomposition: Various decompositions are provided for application to multibaseline polari-

Fig. 6. Analysis tool for multibaseline coherence sets and optimization techniques. metric data for characterization and analysis of scattering behavior. Included are the entropy alpha anisotropy decomposition, the sphere helix diplane decomposition, the Freeman Durden decomposition, as well as the decomposition into the four angles α, β, δ and γ of the eigenvalue decomposition. i) Classification: The Wishart classifier is provided which can be applied on multibaseline coherency matrices with various choices for initializations. Up to now, two analysis tools have been implemented specifically for multibaseline data. These are: j) Multibaseline Coherence Analysis: This is a tool to analyze the polarimetric complex coherences over multiple baselines simultaneously. Compare Fig. 6. The motivation to develop this tool was the need to observe the coherence set [10], [11] behavior and change by applying different averaging windows, and moving from one pixel to the other. The graphical windows present the coherence sets for selected baselines. Zoom possibilities are provided. Random coherences, the shapes of the coherence sets [12], as well as lexicographic and Pauli coherences can be visualized. Furthermore, various multibaseline optimization techniques have been implemented for analysis and evaluation of the given methods. This tool has been very useful for evaluation and validation during the development of multibaseline coherence optimization techniques, which are described in [8]. One can move the analyzed position pixel by pixel using provided arrow buttons, or one can just click in the main image to get the coherence set for this position. k) Multibaseline Polarimetry Analysis: This tool provides the possibility to observe with one view different polarimetric properties of the scatterers in multiple datasets. See Fig. 7. First, one can utilize pre defined polarimetric decompositions for visualizing the polarimetric properties. As an alternative, one can choose an x axis and an y axis from a wide range of polarimetric parameters and observe the dependency. As in the coherence analysis tool, the variety is given by the choice of datasets, as well as averaging windows and position. Furthermore, general functions can be applied to MB POLInSAR datasets as described in [1], [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 an interferometric, or even a SB POLInSAR dataset can be extracted from the MB PolInSAR dataset. IV. TERMS OF USE RAT is available on the web and distributed under a free software license [4]. The distribution includes most of the source-code, as well as a binary version, which can be executed using the free IDL virtual machine [13] 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

Fig. 7. Simultaneous polarimetric analysis of multiple datasets. fine. However, testing is done on Linux and there might be some problems left when running RAT under Windows. RAT is free 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 D-10587 Berlin, Germany 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. V. 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 implement and test new SAR methods, and, if desired, provide them to the scientific community. The MB POLInSAR module provides a basic framework for multibaseline POLInSAR data processing and data analysis. A possible future development goal might be the implementation of parameter inversion techniques for MB POLInSAR data. Parallel to the development of the polarimetric multibaseline module, the infrastructure of RAT has been continuously extended. One of the recent developments is the possibility to work on multi-file datasets. For the moment, this feature is available only to multiple singlepol channels, but it is going to be extended to polarimetric datasets. Comfortable wizard modes have been implemented to speed up processing steps. Ongoing work concerns also so-called parameter files, which include more system acquisition information that is necessary for many advanced methods. Another interesting topic, for which plans for implementation exists, are image recognition techniques. This includes shape and object based methods, as well as advanced image clustering and segmentation technique ACKNOWLEDGMENTS We would like to thank all the people 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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