Split-Band Interferometry (SBInSAR) exploits the large range bandwidth of the new generation of synthetic aperture radar (SAR) sensors to process images at subrange bandwidth. Its application to an interferometric pair leads to several lower resolution interferograms of the same scene with slightly shifted central frequencies. When SBInSAR is applied to frequency-persistent scatterers, the linear trend of the phase through the stack of interferograms can be used to perform absolute and spatially independent phase unwrapping. While the height computation has been the main concern of studies on SBInSAR so far, we propose instead to use it to assist conventional phase unwrapping. During phase unwrapping, phase ambiguities are introduced when parts of the interferogram are separately unwrapped. The proposed method reduces the phase ambiguities so that the phase can be connected between separately unwrapped regions. The approach is tested on a pair of TerraSAR-X spotlight images of Copahue volcano, Argentina. In this framework, we propose two new criteria for the frequency-persistent scatterers detection, based respectively on the standard deviation of the slope of the linear regression and on the phase variance stability, and we compare them to the multifrequency phase error. Both new criteria appear to be more suited to our approach than the multifrequency phase error. We validate the SBInSAR-assisted phase unwrapping method by artificially splitting a continuous phase region into disconnected subzones. Despite the decorrelation and the steep topography affecting the volcanic test region, the expected phase ambiguities are successfully recovered whatever the chosen criterion to detect the frequency-persistent scatterers. Comparing the aspect ratio of the distributions of the computed phase ambiguities, the analysis shows that the phase variance stability is the most efficient criterion to select stable targets and the slope standard deviation gives satisfactory results.

Split-Band Interferometry-Assisted Phase Unwrapping for the Phase Ambiguities Correction

The vulnerable region of Kivu is strongly affected by geohazards such as volcanism and landslides. An essential ingredient for modelling the geophysical processes from which these geohazards originate is the measure of the Earth’s surface displacements. In this complex region, monitoring these ground deformations can be efficiently performed by using Synthetic Aperture Radar Interferometry (InSAR). Nevertheless, geophysical modelling requires accurate measurements of the displacements over time, which are not always manageable with conventional InSAR due to area-related limitations such as geometric distortions. Consequently, we consider advanced processing techniques such as Split-Band Interferometry (SBInSAR), which potentially allows to calculate the absolute interferometric phase, and Multidimensional Small Baseline Subset (MSBAS), which reconstructs the history of deformations along the horizontal and vertical directions. The aims of this thesis are the investigation of the SBInSAR and MSBAS techniques for improving the ground deformation measurements, the development of these techniques in order to achieve an operational tool of monitoring, and eventually to combine the information provided by both methods. Regarding the Split-Band Interferometry, we consider its theoretical applicability, determine optimum processing parameters and propose a method that corrects phase ambiguities by using SBInSAR as a complement to classical phase unwrapping, which we call SBInSAR-assisted phase unwrapping. We establish that the best criterion for the detection of spectrally stable pixels is the slope standard deviation. We also study the temporal behaviour of spectrally stable pixels and show that few of them persist over time. We analyze the behaviour of artificial reflectors with respect to SBInSAR and provide strong evidences that there exists a correlation between the signal-to-clutter ratio of a target and its detectability as a spectrally stable pixel. Regarding the Multidimensional Small Baseline Subset, we present the InSAR mass processing chain developed in collaboration with the European Center for Geodynamics and Seismicity for MSBAS preprocessing, and we propose to optimize the spatial coverage of MSBAS measurements by using interpolation or global masking. An MSBAS analysis is carried out over the city of Bukavu for the period from January 2016 to September 2017. We also study temporal decorrelation over the Virunga Volcanic Province and we model it using a decreasing exponential function. As a complement, we implement the Range Split-Spectrum technique in the CIS software for the ionospheric phase compensation in low frequency interferograms. We finally discuss the options for merging SBInSAR and MSBAS together.

Towards operational use of combined Split-Band Interferometry and Multidimensional Small Baseline Subset: application to geohazard monitoring in the Kivu region

Most recent SAR sensors use wide band signals to achieve metric range resolution. One can also take advantage of wide band to split it into sub-bands and generate several lower-resolution images, centered on slightly different frequencies, from a single acquisition [1]. This process, named Multi Chromatic Analysis (MCA) corresponds to performing a spectral analysis of SAR images. From this spectral analysis, three potential applications are shown: ➡ Vessel detection based on spectral coherence analysis ➡ Absolute phase unwrapping based on Split Band Interferometry (SBInSAR) ➡ Ionospheric phase component retrieval in SAR interferometry

Split-Band SAR and Split Band InSAR principle and applications

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Masato Furuya

Papers

A step-by-step recipe of band-spli3ng technique for isola8on of ionospheric signal in L-band InSAR data