Mac OSX The software in this version has been compiled again using Snow Leopard 10.6 with 64-bits.

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1 Release Notes GAMMA Software, (Urs Wegmüller, Charles Werner, 5-Dec-2013) Gamma Remote Sensing AG Worbstrasse 225, CH-3073 Gümligen This information is provided to users of the GAMMA software. Further distribution of this document is restricted. This release of the Gamma software includes new programs that provide new capability, additional features to existing programs and bug fixes. Linux Distribution This Linux Gamma software distribution is based on Ubuntu LTS 64-bits. In installations running 64-bit Redhat RHEL 6 OS it will be necessary to request software compiled using an older version of glibc, 2.11 from October The version of glibc is used in Ubuntu Linux Please contact Gamma for this version. Windows Distribution The Windows version of the Gamma software is now fully 64-bits! The software has been compiled and tested under 64-bit Windows 7. The software may work with 64-bit Windows 8, but has not been tested on this platform. The build uses the MINGW64 GCC compilers. The installation instructions for the binary distributions has been updated for the 64-bit release. This release requires installation of a new GAMMA_LOCAL_w64_ zip file containing updated libraries and support for the updated compiler. Support for LAPACK and LAPACKe, gdal 1.10, and hdf has been included in GAMMA_LOCAL_w64_ Using any previous versions of GAMMA_LOCAL will not work with this release. Mac OSX The software in this version has been compiled again using Snow Leopard 10.6 with 64-bits. GAMMA Software Training Courses GAMMA plans to organize in 2013 again training courses at GAMMA (near Bern, Switzerland) for SAR/INSAR (MSP/ISP/DIFF&GEO/LAT) and for PSI (IPTA). The dates for the courses ( not yet fixed) will be announced on our web-site under 1

2 Significant Changes in the Gamma Software Modules since the mid release Sensors GPRI: Furthermore, improvements in the software to specifically support data in the GPRI geometry were included. We are looking forward to support the upcoming SAR Sensors Sentinel-1 and ALOS PALSAR-2. MSP az_proc: Corrected moderate linear phase error that occurred when deskewing the output SLC to zero-doppler geometry. This error was only affecting interferograms of data pairs processed with different range dependent Doppler functions. In interferograms between SLC processed with the old version and the corrected new version will be affected by a phase ramp. If it is necessary to correct previously processed SLCs, Gamma can provide a program to correct the SLC phase of old SLCs. pre_rc: Added capability to process subbands of the range-chirp. This permits generation of range split-band interferograms using the upper and lower range portions of the range-chirp bandwidth (as an alternative to band-pass filtering of the full bandwidth SLCs). ISP par_cs_slc, par_cs_slc_tif: Changed sign on calibration gain factor (cal_gain) to be consistent with radcal_slc. ptarg_cal_slc, ptarg_cal_mli: Enhanced point target characterization functionality: 1. Updated programs to estimate null to null main lobe width using 3 db beamwidth 2. Change bounds for detection if the point target is centered in the data window 3. No longer attempt to subtract clutter power when sidelobes are same level or lower than the clutter. 4. Update ptarg_cal_slc.c to have the option to write out the clutter region data samples in fcomplex format to a file specified by the c_image parameter. fspf: Added mode to permit fast spatial filtering of GPRI interferograms with variable azimuth resolution by giving the option of providing an MLI parameter file. If an MLI parameter is provided, the number of looks in range and azimuth used to generate the intermediate 2Dmulti-look image are adjusted to have approximately equal dimension in ground-range and azimuth. If the data are GPRI, then the azimuth pixel spacing is calculated as a function of range. In the case of non-gpri data, when an MLI parameter file is provided, the ground range pixel spacing is determined from the incidence angle at the center of the swath. par_asar: Corrected sensor string in parameter file for ENVISAT ASAR Wide Swath SLC data to the form ASAR_SS1_VV to be compatible with radcal_slc. offset_slc_tracking: Parallelized using OPENMP, resulting in a factor ~3 speedup with 4 threads. Using lfit1() rather than lfit() to avoid exit when search of lease-squares fit of the peak fails. 2

3 DIFF&GEO SLC_diff_intf, offset_pwrm: Now using init_openmp() subroutine to initialize OPENMP. Running the program with an user selected number of threads greater than 4 is now possible. The number of threads in any program using openmp for parallelization is done by setting the OMP_NUM_THREADS environment variable (e.g. export OMP_NUM_THREADS=2). projection_params.h, create_dem_par: Added support for the Albers Equal Area Conic AEAC for Alaska. create_dem_par Now prints out a list of possible projections if an unknown region is entered when searching for regions with defined projection parameters. pixel_area: Improved parallelization of pixel_area so that there is a factor of 3 speedup when using 4 threads. phase_sim, phase_sim_orb: Added new capability to remove a multiple of 2PI from the simulated phase to increase the precision of the simulated phases in float format. offset_list_fitm: Added new option to output the input coordinates using the lookup-table and polynomial model. dh_map_orb: New program to calculate the sensitivity of the interferometric phase to terrain height and height difference. Especially useful for scaling unwrapped differential interferometric phases when working with Tandem-X data. map_trans, dem_trans: Updated to correct failure in DEM corner calculation when EQA is selected as the output projection. DISP DISP_lib: Corrected memory allocation error when reading/writing BMP image lines. float_math: Added capability to use a user-specified region as a reference. data in that area are averaged and used to normalize the data. In the case of addition or subtraction, the average value in the reference region is subtracted from the data. In the case of multiplication or division, the data are divided by the average value. cpx_math: Added program to permit simple mathematical operations on fcomplex format data files. data2geotiff: Added support for the Albers Equal Area Conic projection for Alaska (EPSG 3338). LAT mt_lee_filt_cpx: Corrected error in the mt_lee_filt_cpx program that precluded filtering the entire complex image scene. The program used the wrong size of the data objects and therefore calculated the incorrect number of lines of the input images. lin_comb: Corrected error when only one file is supplied as input. 3

4 IPTA pt2geo: The point geocoding program now supports the GPRI polar format data geometry. IPTA processing of GPRI data: Substantial tests were conducted to confirm that the IPTA package is fully compatible with GPRI data. def_mod_pt: Program has been parallelized using OPENMP. With 4 threads a speedup factor > 3 can be achieved. fspf_pt: Added support for GPRI data to the fast spatial filter fspf_pt. The GPRI data have variable azimuth pixel spacing from near to far range. Data are resampled to constant azimuth pixel spacing before filtering. Tandem-X DEM generation In this section a possible approach for the generation of a Digital Elevation Model (DEM) using Tandem-X data under the assumption that a pre-existing DEM as the SRTM DEM is available. SRTM Preparation: In the preparation of the SRTM DEM over the area it is recommended to modify the geoidal heights which are provided in the SRTM tiles to WGS84 heights as this is what is specified in the DEM parameter file as vertical Datum. The offset can be determined using a geoidal height calculator as available under For our example (Mount Etna) we determine for the center coordinate (center_latitude: degrees, center_longitude: degrees) a Geoid height of m. We add this value to the SRTM heights to get WGS84 heights. Tandem-X SLC data preparation: We determine based on the meta data if the data is acquired in bistatic mode (as in our example) or in ping-pong mode (accordingly we have to select the corresponding phase model later on). We also check which sensor was the master and is used as the geometric reference. In our example this was TSX (and TDX was receive only). Accordingly TSX is our master SLC for the interferograms. The Tandem-X SLC pair is already co-registered, we call the two scenes and the related SLC parameter files TSX_HH.rslc, TSX_HH.rslc.par TDX_HH.rslc, TDX_HH.rslc.par We determine an MLI image with 4 range and 4 azimuth looks: multi_look TSX_HH.rslc TSX_HH.rslc.par TSX_HH.mli TSX_HH.mli.par

5 and generate a rasterfile of it raspwr TSX_HH.mli Geocoding heights of master using SRTM (with refinement) Then we conduct a geocoding using the SRTM heights. This is done mainly to get the SRTM heights (corrected for the geoid offset) into the SAR geometry of the master. For this we prepared a DEM_parameter file in the required resolution (EQA.dem_par) and the related heights were obtained using dem_trans. The geocoding steps done include: gc_map TSX_HH.mli.par - EQA.dem_par EQA.dem EQA.dem_seg_par EQA.dem_seg lt 1 1 EQA sim_sar - - EQA inc - - EQA ls_map geocode lt EQA sim_sar sim_sar create_diff_par TSX_HH.mli.par diff_par 1 0 offset_pwrm sim_sar TSX_HH.mli diff_par offs snr offsets offset_fitm offs snr diff_par coffs coffsets 10 3 and we check the quality achieved considering the refinement polynomial and the offset statistics (in the screen output of offset_fitm): final solution: 772 offset estimates accepted out of 4096 samples final range offset poly. coeff.: e e-04 final azimuth offset poly. coeff.: e e-05 final range offset poly. coeff. errors: e e e-07 final azimuth offset poly. coeff. errors: e e e-07 final model fit std. dev. (samples) range: azimuth: The refinement is applied to the lookup table:: gc_map_fine lt diff_par lt_fine 1 Permitting us to transform data between the map geometry and the SAR geometry geocode lt_fine EQA.dem_seg hgt rashgt hgt TSX_HH.mli hgt.ras and between SAR geometry and map: geocode_back TSX_HH.mli lt_fine EQA TSX_HH.mli raspwr EQA TSX_HH.mli Generate multi-look interferogram, unwrapping, generation of relative heights We first generate for the master and the slave a multi-look intensity image: multi_look TSX_HH.rslc TSX_HH.rslc.par TSX_HH.mli TSX_HH.mli.par

6 multi_look TDX_HH.rslc TDX_HH.rslc.par TDX_HH.mli TDX_HH.mli.par then we define the offset parameter file: create_offset../slc/ tdx_hh.rslc.par../slc/ tsx_hh.rslc.par off and simulate the interferometric phase. Here it is relevant that we use phase_sim_orb (not phase_sim) and that we use the correct scene as the master and that we indicate that the data is bistatic data (if that is the case as in our example). phase_sim_orb TSX_HH.rslc.par TDX_HH.rslc.par off hgt ph_sim_orb TSX_HH.rslc.par We use then SLC_diff_intf to calculate the differential interferogram. SLC_diff_intf../slc/ TSX_HH.rslc../slc/ TDX_HH.rslc../slc/ TSX_HH.rslc.par../slc/ TDX_HH.slc.par off ph_sim_orb diff and display the differential interferogram: rasmph_pwr diff TDX_HH.mli The differential interferogram looks quite flat but shows local deviations related to noise (e.g. over the sea), topographic effects related to the low resolution of the SRTM DEM e.g. to the north of the area, and deviations related to actual changes of the topography as observed in the Mount Etna peak region (see Figure 1). Figure 1: Differential interferogram Figure 2: Unwrapped differential interferogram 6

7 The crucial step, the phase unwrapping is addressed. For this example we get a good result when using mcf with a weighting factor based on the coherence, without application of any spatial filtering (but maksing of coherence below 0.3): cc_ad diff TDX_HH.mli TSX_HH.mli cc rascc_mask cc TSX_HH.mli cc.ras mcf diff cc diff.unw The result is displayed (see Figure 2 rasrmg diff.unw TDX_HH.mli diff.unw.ras and carefully checked for unwrapping errors. In the case errors appear e.g. some spatial filtering may be used. It is very important that the unwrapped phase are carefully checked and if errors are observed these need to be fixed or masked as well as possible. To convert the unwrapped phases to relative heights the new program dh_map_orb is used. Basically, this program calculates for each pixel the phase to height sensitivity and applies it to scale the unwrapped phases: dh_map_orb TSX_HH.rslc.par TDX_HH.rslc.par off hgt diff.unw dpdh dh TSX_HH.rslc.par 0 To move to absolute heights we need to use a height reference and possibly also a large scale reference to remove any tilts that may be present. For both we can well use the SRTM heights. Our assumption is that the deviation from the SRTM should be zero at large scale and without linear trend. We determine for this reason a plane through the relative heights and subtract it from the relative heights to get the height corrections that we have to apply to the SRTM heights to get an initial Tandem-X height map. create_diff_par TSX_HH.mli.par diff_par 1 0 quad_fit dh diff_par cc.ras plotdata.txt 3 quad_sub dh diff_par dh.shifted 0 0 SVD fit parameters: e e e-03 std. dev. of SVD phase model residuals (radians): This means that besides the offset a range trend of 0.66m per 1000 range pixels and an azimuth trend of -5.4m per 1000 azimuth pixels (in MLI geometry) is corrected. Geocoding The relative heights are then resampled into the map geometry using the refined lookup table calculated based on the SRTM DEM. It is clear that this is not the final solution as a lookup table based on the Tandem-X heights should be used (see below). Besides the relative height we also resample the coherence and the backscattering geocode_back dh.shifted lt_fine EQA dhgt

8 geocode_back TDX_HH.mli lt_fine EQA TDX_HH.mli geocode_back cc lt_fine EQA cc Generation of initial Tandem-X DEM In the map geometry we add then relative heights to the SRTM heights: lin_comb 2../DEM/EQA.dem_seg EQA dhgt EQA hgt and do some more quality control. For this we mask low coherence areas rascc_mask EQA cc EQA TDX_HH.mli EQA cc.ras mask_class EQA cc.ras EQA hgt0 EQA hgt0.tmp determine a mask for outliers (= high deviation from spatially filtered value). interp_ad EQA hgt0 EQA hgt.filt lin_comb 2 EQA hgt0 EQA hgt.filt EQA dh single_class_mapping 1 EQA dh EQA dh1a.ras and remove heights for outliers mask_class EQA dh1a.ras EQA hgt0.tmp1 EQA hgt and determine a mask containing area of non-zero data (to avoid interpolation outside this area): lin_comb 1 EQA hgt tmp rascc_mask tmp1 EQA TDX_HH.mli EQA mask1.ras Then we apply a slight interpolation to fill very small gaps interp_ad EQA hgt1 EQA hgt and apply a slight spatial filtering to reduce noise interp_ad EQA hgt2 EQA hgt.interp.final rashgt EQA hgt.final1 EQA TSX_HH.mli to get the final result for the first iteration. EQA hgt.interp.final1 (see Figure 3). Of course this conditioning can be modified. What is done in the example is just to show some possibilities. 8

9 Figure 3: Tandem-X height map obtained in initial iteration using a color scale with 200m per color cycle. 9

10 Additional iterations We can now do iterations to change the geocoding from using the SRTM height based lookup table to a Tandem-X height based lookup table. Typically, 2 iterations should be sufficient.. gc_map TSX_HH.mli.par - EQA.dem_seg_par EQA hgt.interp.final1 EQA.dem_seg_par.tmp EQA.dem_seg.tmp lt 1 1 EQA sim_sar - - EQA inc - - EQA ls_map gc_map_fine lt diff_par lt_fine 1 geocode_back hgt.shifted lt_fine EQA dhgt geocode_back TSX_HH.mli /DEM/ lt_fine EQA TSX_HH.mli geocode_back cc lt_fine EQA cc lin_comb 2 EQA.dem_seg EQA dhgt EQA hgt followed again by the conditioning step to determine the solution for the first iteration: EQA hgt.interp.final1 Validation No strict validation on this result was done, but a comparison of the Tandem-X DEM (using 2012 data) and the SRTM DEM (using 2000 data) is shown in Figure km 0.7km -20m 0m +20m elevation change -150m 0m +150m elevation change Figure 4 Mount Etna, Sicily. Elevation change between 2000 (SRTM) and 2012 (Tandem-X data). The image brightness corresponds to the shaded relief of the 2012 Tandem-X interferometric DEM. Elevation increase > 100m has been observed for some areas. Tandem- X data courtesy INSA3397, DLR. InSAR processing by GAMMA. 10