RADAR INTERFEROMETRY WITH PUBLIC DOMAIN TOOLS

Bert M. Kampes(1,2), Ramon F. Hanssen(2), Zbigniew Perski(3)

(1) German Aerospace Center (DLR) – Oberpfaffenhofen; 82234 Wessling, Germany; Bert.Kampes@dlr.de(2) Delft University of Technology; Kluyverweg 1, 2629 HS Delft, The Netherlands; Hanssen@geo.tudelft.nl

(3) University of Silesia; 41-200 Sosnowiec, Bedzinska 60, Poland; perski@us.edu.pl

ABSTRACTThe development of public-domain software in the scientific community has stimulated a fast and free disseminationof ideas. Here we present latest contributions to public-domain radar interferometry software to create interferometricproducts and to analyze and visualize these products. We present the feasibility of the Doris radar interferometric softwareof Delft University of Technology to create ENVISAT interferometric products such as DEMs and deformation maps. Astepwise description of the creation of an ENVISAT DEM of the Las Vegas area is presented, using besides Doris theESA BEAM toolbox, the statistical cost phase unwrapper SNAPHU (Stanford University), the Generic Mapping Tools(University of Hawaii), and the PROJ.4 package (USGS). Finally, the GRASS GIS software is used to drape a LandSATimage on top of the computed DEM in order to show the analysis and visualization capabilities of this free GIS package.These packages are also described briefly in this paper. Both advantages as well as drawbacks of these tools are discussed.It is the intention of this paper to demonstrate how a larger scientific community can benefit from freely available tools,and which contributions need to be solicited for.

1 INTRODUCTIONThe Delft object-oriented Radar Interferometric Software package “Doris” is been developed in the C++ since September1998 [5] [6]. In 1999, it was made available in the public domain to stimulate joint development and to take advantageof feedback by a larger user community. The availability of technical public domain software is important for a fastacceptance and integration of a technique in other disciplines, enabling scientists to gain experience with new techniquesat low cost. Currently, many scientists from various research groups are using Doris and querying the Doris website [12].These people are members of a user group that automatically receives notice of new releases and who can ask and answerquestions from other users. These questions and answers are archived using a free yahoo group email account [13], whichserves as a searchable FAQ (list of frequently asked questions).

Public domain software is generally distributed “as is”, and “any express or implied warranties, including, but notlimited to, the implied warranties of merchantability and fitness for a particular purpose are disclaimed”. Installationof public domain software is not always straightforward, mainly due to differences in configurations of users (differentplatform architectures and conventions, compilers, etc.). A disadvantage of public domain software versus commercialsoftware may thus be the lack of reliable support, concerning installation, running, and documentation. However, com-mercial software also does not always run smoothly and it is for a user almost impossible to repair, or even to gain insightin the problems. Since public domain software is often free of charge, it is often quickly updated after a report of aproblem. But the main advantage of the software discussed here is the availability of the source code, and thus completeopenness of implementation details and the possibility of adapting it to ones needs.

Doris is free for research purposes and has been installed on virtually all existing platforms. The GNU compilersuite [20] is used as default compiler, which is available on most platforms. Before installation a configure script is runthat figures out compiler and platform specifics (mainly big or small endian). Also, dedicated libraries are used whenavailable (the public domain FFTW library for Fourier transformations [15], LAPACK for Cholesky decomposition, andon HP systems, the commercial VECLIB library for fast matrix multiplications). If these libraries are not available, aninternal implementation is used. The memory (RAM) usage of Doris can be set with the input file. The user manual andtechnical documentation is extensive (over 100 pages), and approximately 20% of the source code are comments. A testdata set can be downloaded to get familiar with the software.

The basic interferometric tasks computed with Doris are described in section 2. During this processing, the basicinformation (height or displacement) is extracted from the phase data in the input Single Look Complex (SLC) images.Doris integrates other dedicated (public domain) programs where possible, particularly for the phase unwrapping, seesection 2.7. Further analysis and visualization of the results is then performed using PROJ.4, GMT, and GRASS, whichis described in sections 3 and 4. Finally, section 5 concludes with a review of the main advantages and disadvantages ofthe public domain software described in this paper.

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Proc. of FRINGE 2003 Workshop, Frascati, Italy,1 – 5 December 2003 (ESA SP-550, June 2004) 22_kampes

2 INSAR PROCESSINGDoris is capable of performing most common radar interferometric processing steps in a modular setup. The followingsub-sections describe these modules. In order to demonstrate the capabilities of the software suite, we compute a full-scene interferogram and corresponding DEM from ENVISAT ASAR data on a laptop. The total processing time was about1 hour, with a 900 MHz processor and approximately 100 MB of RAM available. The operating system was WindowsXP, while Cygwin was used as a shell. Cygwin is a Linux-like environment for Windows. Most Cygwin tools are releasedunder the GPL (GNU General Public License, i.e., free software, [20]), or are in the public domain. Installation of Cygwinis extremely straightforward using a setup script directly under Windows, see [11].

2.1 Doris’ philosophyDoris’ philosophy is to use simple format definitions, and a single main program that keeps track of the processing. Thereare 3 small ASCII files that are used to store relevant parameters on the master image, the slave, and on the products.Each processing step can read and write to these files, updating them with the latest available results. Typical parametersare for example the name of a binary data file and the number of lines it contains, the Doppler centroid frequency, orbitaldata points, etc. The data products themselves are stored in simple binary data streams (i.e., files), without a header.

Doris uses other public domain software to perform dedicated tasks that can be handled well by these programs. Thisincludes getorb to obtain precise orbital data records for the ERS satellites [8] [17], SNAPHU for phase unwrapping[27], GMT for general plotting and gridding [19], PROJ.4 for coordinate transformations [26], and GRASS GIS forpowerful analysis and data fusion with other layers of information [21]. Furthermore, it can handle the publicly availableGTOPO30 global height model of the USGS to remove the topographic phase [22], or the GLOBE DEMs [18]. Althoughthe resolution of this DEM is rather limited, in the near future the SRTM DEMs should become available for (almost) thewhole earth [28].

A shell script has been written that generates template configuration files and can be used to run Doris. For eachstep, the output can be automatically visualized by generating SUNraster quicklook files of the intermediate products.There also is a quicklook processing option build in the shell script that performs an automated processing to quicklyobtain a lower quality interferogram and coherence image. The speed-up comes from skipping filtering steps, usinglarge multilooking factors, and using simple interpolation kernels. People not used to the UNIX environment can havea disadvantage running the Doris software at start (and the other described public domain software). But the concept ofusing ASCII input files and a pipeline of programs instead of a graphical user interface (GUI) is quite powerful, and withthe provided documentation and examples it is easy to apply.

2.2 Reading the ASAR parametersDoris was originally written to handle ERS SLC data. Adding an option to Doris to be able to process data from a newsatellite mainly means adding the ability to read a new data format, since the other modules are not affected. For exam-ple, for ENVISAT, the data is no longer distributed in the CEOS format, as was used for the ERS and JERS satellites.Nevertheless, the main processing steps to create the interferogram are hardly affected, as long as the data and the param-eters can be read from the CD-ROM in the distributed format. The number of parameters required for interferometry issmall, but they still need to be read from the distributed data formats. For this purpose, ESA has made the Basic ERS &Envisat (A)ATSR and Meris Toolbox (BEAM) freely available, see [14], making the task of adapting the software a lotsimpler. With the library functions in this toolbox, a simple stand-alone program reads all available information from theASAR SLC distributed file, and dumps it to a temporary ASCII file. Simple UNIX commands are then used to extract theparameters that are required, and to write them to the Doris parameter file in the correct format and units. The reading,extracting and converting the parameters only takes a few seconds of CPU time.

Details on the processed scenes can be found in tables 1 and 2. The temporal baseline is only 35 days, resulting inminimal temporal decorrelation for this dry area. The interferometric amplitude of the scene is shown in Fig. 2 (top left).The city of Las Vegas is located at the bottom of this image, whereas the top part consists of mountains. The maximumtopographic difference in the scene is about 3000 meter, see also Fig. 4.

2.3 Cropping (ASAR) data to hard diskUsing the ESA BEAM toolbox, we implemented a program that is able to read the data in the distributed format, andwrites an area of interest to hard disk. This program is included in the Doris distribution. We read and write the data perline to keep the implementation simple and memory requirements low. In Doris a system call is made to this stand-alone

Table 1: Image parameters for the processed master and slave ASAR SLC. These parameters are read from the ENVISATdata file using a small program written with the freely available ESA BEAM library.

Image parameter Master SlaveFrame number 12 11Orbit number 4418 3917Acquisition date 03-JAN-2003 29-NOV-2002Acquisition time [UTC] 17:52 17:52Number of lines 26894 26894Number of range pixels 5195 5195Doppler centroid frequency [Hz] 230 185

Table 2: Product parameters for the interferogram. Most of these parameters have been computed by Doris.Product parameter valueScene identification Las VegasScene size [km2] 100 × 100Scene center longitude [deg] -115.007Scene center latitude [deg] 36.4614Perpendicular baseline [m] 100.9Parallel baseline [m] -14.7Height ambiguity [m] 81.4Temporal baseline [d] -35

program. The only requirement for this to work is that the executable program is in the search path. The (wall clock)time required by this program was about 7 minutes for a full scene (650 MB of complex short integer data, including byteswapping), performed for the master and slave image.

2.4 CoregistrationMaybe the most important step in interferometry is the alignment of the slave on the master image . The determination ofthe coregistration polynomial that describes the transformation of the slave to the master is performed in 4 steps in Doris.

First the orbital data of master and slave are used to compute a single coarse offset on pixel level between master andslave image. The estimate for this offset is then improved using a cross correlation performed on the intensity data ofmaster and slave on a small number of relatively large patches. The initial offset is taken from the previous computedcoarse offset based on the orbits. In the third step, the fine coregistration, the offset vectors between master and slave arecomputed at a large number of small patches (64 by 64 pixels), also using a cross correlation of the intensity data. Fig. 1shows the computed offset vectors at these patches, which project the slave image on top of the master. Only vectors areplotted that have a correlation larger than 0.4. Above the image, an overview of the estimated offsets is given in a table.GMT is used to plot these data. This plot is generated by Doris, while optionally the magnitude of the master image canbe used as background.

Finally, when we have obtained these offsets at a number of positions in the image, a threshold is chosen to selectpatches that contain useful information, and a 2D-polynomial of degree 2 (by default) is fitted through these offset vectors.A least squares fit weighted by the coherence is used. In general, not all selected offset vectors fit well within the model.We use a manual or automated iterative process to improve the estimation of the transformation polynomial. Each timeafter a polynomial is fitted, we can detect outliers and exclude them from a next polynomial fit. This gives a large controlto the user, who can check the coregistration in a detailed manner. Plots of the least squares residuals (2D vector plot,histograms and an xy-plot) are also generated automatically by Doris. The maximum least squares residual was about 0.2pixels for this dataset and selected offset vectors (computed offset versus model). Using the estimated polynomial, theslave image is resampled to the master grid. We account for the Doppler centroid frequency of the slave image, and canuse several interpolation kernels, ranging from nearest neighbor to cubic convolution, to a 16 point truncated sinc. Defaultwe use a 6 point cubic convolution kernel, see [4]. The actual resampling is by far the most time-consuming procedure,about 24 minutes of CPU time in this case. The output format was chosen to be 4 byte float values, making the size of thedata file about 1.3 GB.

GMT 2003 Aug 19 17:31:29

Offset_vectors_(Corr_>_0.4)

3842

7684

11526

15368

19210

23052

26894

Azi

mut

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742 1484 2226 2968 3710 4452 5194Range

correlation1.00.5

(-649.44;2.25) - 1 (-649.81;2.12) - 2 (-650.06;2.00) - 3 (-650.38;1.88) - 4 (-650.75;1.69) - 5 (-651.00;1.62) - 6(-651.19;1.44) - 7 (-651.50;1.31) - 8 (-651.88;1.19) - 9 (-652.06;1.06) - 10 (-652.25;1.00) - 11 (-649.44;2.31) - 12(-649.88;2.12) - 13 (-650.12;2.00) - 14 (-650.38;1.88) - 15 (-650.75;1.75) - 16 (-651.00;1.62) - 17 (-651.19;1.44) - 18(-651.44;1.25) - 19 (-651.81;1.12) - 20 (-652.06;1.06) - 21 (-652.31;1.00) - 22 (-649.50;2.25) - 23 (-649.88;2.12) - 24(-650.12;2.00) - 25 (-650.38;1.88) - 26 (-650.75;1.81) - 27 (-651.00;1.62) - 28 (-651.25;1.44) - 29 (-651.69;1.25) - 30(-651.88;1.19) - 31 (-652.06;1.06) - 32 (-652.25;1.00) - 33 (-649.50;2.25) - 34 (-649.88;2.12) - 35 (-650.12;2.00) - 36(-650.44;1.88) - 37 (-650.75;1.81) - 38 (-651.00;1.62) - 39 (-651.25;1.44) - 40 (-651.69;1.25) - 41 (-651.81;1.12) - 42(-652.06;1.06) - 43 (-652.31;1.00) - 44 (-649.50;2.31) - 45 (-649.88;2.12) - 46 (-650.12;2.00) - 47 (-650.44;1.94) - 48(-650.81;1.81) - 49 (-651.00;1.62) - 50 (-651.25;1.38) - 51 (-651.62;1.25) - 52 (-651.94;1.12) - 53 (-652.00;1.00) - 54(-652.31;1.00) - 55 (-649.44;2.25) - 56 (-649.94;2.12) - 57 (-650.12;2.00) - 58 (-650.50;1.94) -

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Figure 1: Offset vectors between master and slave image computed with Doris and plotted with the GMT. This is standardoutput during the coregistration with Doris.

2.5 Spectral filteringSpectral filtering in range and azimuth can optionally be applied to increase the signal to noise ratio. For the azimuth filterthe non-overlapping spectrum is removed based on the given Doppler centroid frequencies (read from the annotation ofthe SLC data, see section 2.2). The range filtering can either be performed based on the orbital data (thus disregardinglocal terrain slopes due to topography), or the non-overlapping spectrum can be locally estimated after resampling of theslave image. For this demonstration we did not apply any spectral filtering, mainly because we expected only a verysmall amount of decorellation due to the small perpendicular (and temporal) baseline, and the small difference in Dopplercentroid frequencies (table 1).

2.6 Interferogram generationThe interferogram is computed using a multilook factor of 5 in azimuth and 1 in range. It is simply the dot product of themaster and complex conjugated slave, i.e., it contains the product of the amplitudes and the difference of the phases ofmaster and (aligned) slave. The phase difference contains information on topography, possible deformation, and possiblyatmosphere.The interferogram generation took less than 2 minutes.

The interferometric phase is corrected for the phase of a reference body. In this case the WGS84 ellipsoid was usedto compute the reference phase, but an external DEM can also be used as input in Doris. This DEM could be in a varietyof input formats, but it must be on a regular grid in the WGS84 datum. The reference phase corrected interferogram ismultilooked again, now by a factor of 4 in both directions. The pixel size of the product is thus about 80 by 80 meters. Themultilooking reduces the size of the data files considerably, and also reduces memory requirements for the unwrapping.Fig. 2 shows the quicklook output of Doris for the corrected phase.

Doris can also compute the coherence image, that can be used as input for the cost function computations in theunwrapping program. Phase filtering can be applied using different methods. We normally use a simple pre-definedspatial averaging kernel, but optionally 2D convolution kernels can be read from ASCII input files, or the Goldstein filtercan be used [3], [1]. In this case we did not perform phase filtering because of the already high coherence of these data.

Figure 2: Overview of interferometric processing with Doris. The interferometric amplitude and phase are shown (toppanels), as well as their overlay (bottom left). The phase is corrected for the reference phase of the WGS84 ellipsoid. Theunwrapped phase is shown in the bottom right panel. These images are automatically generated by Doris. Original dataare copyright of ESA.

2.7 Phase unwrapping with SNAPHUThe SNAPHU phase unwrapping software is described in [2]. It stands for Statistical-Cost, Network-Flow Algorithmfor Phase Unwrapping. It is a modern, sophisticated phase unwrapping program that uses information on the expectedsmoothness of the unwrapped phase, and can use the interferometric amplitude and/or coherence image to compute thecost functions. It is called fully automatically from within Doris when it is installed on the system. The RAM useage ofSNAPHU cannot be controlled by Doris, except by using larger multilooking factors, thus reducing the dimensions of theinterferogram. SNAPHU can be found at the internet pages of the Stanford university, [27]. Fig. 2 shows the unwrappedphase in the bottom right. SNAPHU took 7 minutes of wall clock time to unwrap the interferogram of size 1297× 1310

pixels.

2.8 Phase to height conversion and geo-referencingThe unwrapped phase is converted to a height, and the (azimuth, range) coordinates are geo-referenced. The algorithmdescribed in [9] was used for the phase to height conversion, and took about 5 seconds. Two other implementations canalso be used to perform this step. The geo-referencing took about 1 minute, and was done by constraining each pixel to thepreviously computed height, to be perpendicular to the master orbit, and to be at the range given by its range coordinate.

The output of this step is the height for a large number of pixels at an irregular grid of (longitude, latitude) pairs,which in itself is not that useful. These output matrices however are gridded using the GMT tools, while with PROJ.4 wecan perform coordinate transformations to obtain the DEM in the desired gridding and coordinate system. Alternatively,GRASS can perform the gridding. Note that also other images, such as the coherence image, are thus automaticallygeo-referenced in this way.

GMT 2003 Aug 24 16:37:31

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Figure 3: DEM (shaded relief) of the Las Vegas area computed with Doris and other public domain tools. The data gapsin high-relief areas are caused by shadow.

3 COORDINATE TRANSFORMATIONS AND GRIDDINGThe PROJ.4 package [26] is a “Cartographic Projections library” that can perform respective forward and inverse trans-formation of cartographic data to or from Cartesian data with a wide range of selectable projection functions, includingdatum translations. It has been originally written by Gerald Evenden then of the USGS. It is well documented and can beused to transform the geo-referenced output of Doris to any desired projection, such as UTM. An example script for thesetransformations is included in the Doris distribution.

The GMT software is a set of tools to handle {x,y,z} data, implemented as UNIX filters. Each program performs a specifictask, such as creating a vector plot, or to perform gridding. The output of each program is either a processed data file ora postscript plot. The concept of applying programs on output of previous programs makes the GMT extremely flexible,particularly in combination with standard UNIX commands. GMT is extremely well documented, with a “Technical Ref-erence and Cookbook”, the “GMT Tutorial”, and the “GMT UNIX man pages”, which are all available online. GMT isfree software, released under the GNU General Public License. See also [19] and [10]. The GMT software can be used tocreate postscript plots of the output of Doris, but also for data interpolation and gridding. Scripts for gridding are includedwith Doris, but the GMT can be used for an endless variety of analysis purposes.

An example of the final output of Doris is given in Fig. 3, where the DEM is gridded in UTM coordinates. For compar-ison we plot the computed DEM next to a high precision reference DEM obtained with SRTM, see Fig. 4. The differencebetween both DEMs appears to be small on visual inspection, showing at least that no significant errors occurred duringthe processing with Doris.

4 GRASS GIS DATA ANALYSISThe Geographic Resources Analysis Support System (GRASS) [21]) can be used after geo-referencing of the end product.In this way, the computed DEM or deformation map is a layer in a geographic information system, and can be combinedwith other layers, such as ground-truth data. GRASS is an open source, Free Software Geographical Information System

Figure 4: High quality USGS DEM. Original horizontal resolution is 25 meter. This figure was created using theshade dem program of the German Space Agency (not in the public domain).

(GIS) with raster, topological vector, image processing, and graphics production functionality that operates on variousplatforms through a graphical user interface and shell in X-Windows. GRASS is released under the GNU General PublicLicense (GPL).

GRASS offers a set of tools for DEM or deformation map analysis like calculations of surface parameters (e.g., slope,aspect, and curvature), and allows to perform more detailed analysis like flowliles and their densities, watershed calcula-tion, and flow tracing. The latest version of GRASS includes nviz, a module for viewing data surfaces in three dimensionswith the ability to visualize multiple raster, vector, and site files at one time, with the addition of volume data. GRASS,like other GIS systems, has the ability to perform spatial analysis of data from different sources and stored in differentformats. GRASS can read a wide variety of data formats since the import function is supported by the gdal library [16].

For this work, a GRASS installation on a Linux-PC was used (2GHz processor, 512MB RAM). The {x,y,z} topographicdata from Doris was first converted to ASCII vectors using cpxfiddle, a generic utility distributed with Doris. This data wasimported in GRASS, and then interpolated to a regular grid using spline with tension function [7]. After interpolation,selected functionals of the surface parameters were calculated, specifically slope, aspect, flowlines and their density.Additionaly, for data analysis and visualization purposes, the Landsat data were imported. We used the Landsat geocodedcomposition archive from 1990 which is freely available for the largest part of the world, see [25]. The data are stored incompressed MrSid format. The freely available decoder from Lizard Tech [24] has been used for extraction of the datafor our area of interest, and saved in GeoTiff format. Landsat data were applied for 3D visualization of the interferometricDEM. Fig. 5 and Fig. 6 show examples of the GRASS capabilities.

5 CONCLUSIONSRadar interferometry using freely available software has matured to a stage in which data from different sensors can beroutinely processed to interferometric products. It is demonstrated that Doris is capable of handling the data format ofERS and ENVISAT ASAR. Together with other public domain tools—for phase unwrapping, coordinate transformationand data gridding—a DEM in UTM coordinates has been computed for the Las Vegas area. The flexibility of the softwarewas shown by performing the computations on a 900 MHz laptop with approximately 100 MB of available RAM, runningon a MS Windows operating system. The total processing time was approximately 1 hour.

Figure 5: Screenshot of GRASS. A watershed analysis is made from the output of Doris. For visualization, a LandSATimage has been draped over the computed DEM.

Figure 6: Perspective view of the Las Vegas area created with GRASS (nviz program) based on interferometrically derivedheights and a LandSAT image.

A large scientific user community can take advantage of the developed software, and from the other described tools,which allows them to perform their research with ENVISAT data in a convenient manner.

Not included in the described software is the SAR processing, i.e., the focussing of the raw radar data to SLC images(which is the input to the Doris software). This may be overcome by the recent release of the JPL ROI PAC softwarein the public domain [23] (at least for ERS). Also lacking is a generic multi-platform tool for interactive visualization ofinterferometric products. Here we may also benefit of the released DGX software [23].

Furthermore, it has not yet been investigated what the distribution format is for the SRTM DEMs, nor how to interfaceconveniently with this huge data set for topographic phase correction (or to use it to aid unwrapping, compute terraincorrected coherence, geo-reference deformation maps, etc.). For this step (reference phase computation using a DEM),the current algorithm is not very efficiently programmed, and the computations take a long time. It is also limited to theusage of DEMs in the WGS84 datum, in a regular grid, while most people may have a high accuracy DEM of their area ofinterest in some local datum. The PROJ.4 software in combination with the GMT can be used to circumvent this problem,but a standard solution is not provided by the Doris software.

A possible drawback of including several public domain package to perform different tasks may be different philoso-phies of different authors. For example, it is not possible to control memory usage of the SNAPHU software from withinDoris. In general however, most users nowadays will have a powerful computer available.

Further improvements to the described software that are considered are an interface to RadarSAT data format, andmodule to enable the subtraction of an available reference deformation model.

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