PhD Defence Mr Adugna Girma Mullissa

Department of Earth Observation Science


Title of Defence

Quality aspects of distributed scatterers in polarimetric differential SAR interferometry


Ground deformation measurements deliver valuable information for the understanding of natural hazards such as landslides, ground subsidence, earthquakes and volcanism. Satellite based SAR interferometry provides detailed, cost effective measurements for this purpose. The quality of these measurements, however, is often degraded by decorrelation. Here quality is defined as the number of measurement points and accuracy of deformation measurements from these points. The degree of decorrelation is in particular severe in natural environments where the number of coherent targets is limited. Hence, improving the quality of deformation measurements in natural environments is important to improve their usability.

This research exploits the polarimetric diversity provided by fully and partially polarized SAR images to increase the number of measurement points and deformation measurement accuracy. It is composed of three major topics. First, the performance of three polarimetric optimization methods has been analyzed and evaluated to select the polarization state that is least affected by decorrelation. This was done in a single and double phase center scenario. These scenarios are applied on dual and quad polarized SAR images acquired from a completely natural environment in Ethiopia. The purpose was to quantify the coherence improvement in different types of distributed
scatterers. Bias in the coherence estimation of the different optimizers was investigated from simulated PolInSAR image. Deformations observed from three different polarimetric optimization methods were compared with the deformation values obtained from an in-situ GPS. The applied polarimetric optimization routines substantially improved coherence estimation. These routines have a lower estimation bias as compared to the traditionally used single polarization channel.

Second, we developed a spatial filter using statistical homogeneous pixels (SHP) based upon the scattering mechanism to improve the signal to noise ratio of interferometric phase over distributed scatterers. This method was first developed to estimate polarimetric coherency matrix for a single image. It improved the estimation of coherency matrix by avoiding the indiscriminate use of moving averages. Improvement was achieved by iteratively classifying and refining the estimate of the coherency matrix. It derived the scattering mechanism by applying the Cloude-Pottier eigenvalue-eigenvector decomposition technique whereas the Wishart distance measure was used to classify the scattering mechanism. Classification of scattering mechanisms was used as a basis to group similar pixels for the spatial filter. To reduce bias caused by pixel selection and that is generated by an incorrect decomposition and classification of scattering mechanisms, an iterative refinement of decomposition and classification was used. The method was implemented on both simulated and real PolSAR images acquired from the San-Francisco area, USA and Flevoland, The Netherlands. The results were compared with other state of the art spatial filters. Results indicated that the proposed method compares favorably with other state of the art spatial filters in preserving polarimetric information, spatial details and point scatterers.

Third, we adapted the scattering mechanism based spatial filter to improve the signal to noise ratio of interferometric phase over distributed scatterers and estimate interferometric coherence matrix. Selection of the most coherent scattering mechanisms within a distributed scatterer candidate is done by applying an eigenvalue decomposition of the interferometric coherence matrix. To identify the distributed scatterer candidate for optimization we derived a phase entropy measure to be applied as a threshold. Coherent scatterers were selected by identifying scattering mechanisms in the resolution cell that interfere with the dominant scattering mechanism. Performance was evaluated on full and dual polarized SAR images acquired over Los Angeles area, USA and Groningen, The Netherlands. Number of measurement points and deformation estimates were compared with those of traditional PSI methods. Results indicate that the proposed method substantially improved the number of measurement points and the deformation estimate.

To summarize, this dissertation contributes to improving the quality of deformation measurement in natural environments from both fully polarized and partially polarized SAR images.


Adugna Girma Mullissa obtained his BSc degree in Geology and geophysics from Addis Abeba University, Ethiopia in 2004, and the MSc degree in Earth sciences with focus on Remote Sensing for Earth science applications at the Addis Abeba University in 2008, Addis Abeba, Ethiopia. In November 2013, he joined the Faculty of Geo-Information Science and Earth Observation (ITC) at the University of Twente for his PhD research which culminated in this PhD thesis. From September 2016 to January 2017, he was a visiting research scholar at Lyle school of civil engineering, Purdue University, United States. His research interests include Polarimetric SAR data denoising, multi-temporal polarimetric differential SAR interferometry and pattern recognition and machine learning for Polarimetric SAR data.


Event starts: Friday 22 December 2017 at 16:45
Venue: UT Waaier 4
City where event takes place: Enschede

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