Title:Levy-stable Distribution Based Models for Speckle on Remote Sensing Images

Speaker: Prof. Ercan Engin KURUOGLU

Time: 16: 30-17: 00, December 5, 2023

Place: Liangjiang Grand Ballroom

Abstract: Synthetic aperture radar is a powerful remote-sensing technology widely adopted for airborne or spaceborne geo-sensing and surveillance applications due to its significant advantages of high azimuthal resolution and weather-independent operation. A significant problem effecting performance in segmentation and feature or target detection in SAR images is the presence of speckle noise due to the remote sensing image generation mechanism. Although various statistical models have been proposed for speckle, the accurate characterization of heterogeneous SAR image data such as urban scenes remains an obstinate problem that awaits attention. Amongst the versatile statistical models established for SAR images, generalizing stable distribution to the complex isotropic scenario attracts attention due to theoretical justification of these models motivated by wave propagation dynamics and Gnedenko and Kolmogorov’s generalization of the central limit theorem under the removal of finite variance assumption. Yet the lack of analytical representation for the model restricted its further development. We provide a derivation and proof justifying the use of the complex-isotropic models belonging to the Levy-stable family. We describe the existing generalized Rayleigh model which is now one of the state of the art models and present two new distributions which take care of scenarios with dominant reflectors and asymmetric reflections. For the purposes of parameter estimation, we extend the method of moments to beyond power moments into algebraic moments and propose closed form solutions. We believe that the potentials of the proposed models and estimation methods go beyond SAR imaging to ultrasound imaging and multipath fading in communications.

Biography: Ercan E. Kuruoğlu received MPhil and PhD degrees in information engineering from the University of Cambridge, United Kingdom, in 1995 and 1998, respectively. In 1998, he joined Xerox Research Center Europe, Cambridge. He was an ERCIM fellow in 2000 with INRIA-Sophia Antipolis, France. In 2002, he joined ISTI-CNR, Pisa, Italy where he became a Chief Scientist in 2020. Currently, he is a Full Professor at Tsinghua-Berkeley Shenzhen Institute since March 2022. He served as an Associate Editor for the IEEE Transactions on Signal Processing and IEEE Transactions on Image Processing. He was the Editor in Chief of Digital Signal Processing: A Review Journal between 2011-2021. He is currently co-Editor-in-Chief of Journal of the Franklin Institute, Data Science and Signal Processing Section. He acted as a Technical co-Chair for EUSIPCO 2006 and a Tutorials co-Chair of ICASSP 2014. He is a member of the IEEE Technical Committees (TC) on Machine Learning for Signal Processing, on Signal Processing Theory and Methods, and on Image, Video and Multidimensional Signal Processing and EURASIP TACs on Machine Learning for Signal and Data Analytics. He is also member of the IEEE Data Collections and Challenges Committee. He was a member of IEEE Ethics Committee in 2011. He was a plenary speaker at DAC 2007, ISSPA 2010, IEEE SIU 2017, Entropy 2018, MIIS 2020 and tutorial speaker at IEEE ICSPCC 2012. He was an Alexander von Humboldt Experienced Research Fellow in the Max Planck Institute for Molecular Genetics in 2013-2015. His research interests are in the areas of statistical signal and image processing, Bayesian machine learning and information theory with applications in remote sensing, environmental sciences, telecommunications and computational biology.