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Doctoral defense in Electrical and Computer Engineering - Han Van Nguyen

Doctoral defense in Electrical and Computer Engineering - Han Van Nguyen - Available at University of Iceland
When 
Thu, 08/12/2022 - 13:00 to 15:00
Where 

Aðalbygging

Further information 
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Doctoral candidate: Han Van Nguyen

Dissertation title: Unsupervised Deep Learning in Remote Sensing with Application to Image Fusion and Denoising

Opponents: Professor Danfeng Hong, Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, China.
Professor Farid Melgani, Department of Information Engineering and Computer Science, University of Trento, Italy.

Advisors: Dr. Magnús Örn Úlfarsson, Professor at the Faculty of Electrical and Computer Engineering, University of Iceland.
Dr. Jóhannes Rúnar Sveinsson, Professor at the Faculty of Electrical and Computer Engineering, University of Iceland.

Doctoral committee: Dr. Mauro Dalla Mura, GIPSA-lab, Professor at the Signal and Image Department, Grenoble Institute of Technology (INP), France.

Chair of Ceremony: Dr. Lotta María Ellingsen, Associate Professor and Head of Faculty of Electrical and Computer Engineering, University of Iceland.

Abstract

Optical remote sensing (RS) uses optical sensors to create images of the Earth's surface. Those imaging sensors are mounted on spaceborne or airborne vehicles and capture visible, near-infrared, and shortwave infrared radiation reflected from the Earth's surface. Optical remote sensing imaging systems usually provide multi-band images, such as hyperspectral images (HSIs) and multispectral images (MSIs), often with band-dependent spatial resolution. However, those images are often corrupted by noise and have low spatial/spectral resolution. This is caused by several reasons, such as atmospheric absorption, sensor imperfection, and a trade-off between spectral and spatial resolutions. Therefore, denoising or sharpening the images is crucial for many RS applications. This thesis focuses on HSI denoising and RS image fusion. HSI denoising is the problem of recovering the original true image from the noisy HSI. On the other hand, in RS image fusion, one has a set of co-registered images, each acquired at a different frequency band and having a different spatial resolution. The aim is to sharpen the images so they all have a spatial resolution equal to the highest spatial resolution of the input images. The main objective of this thesis is to propose new HSI denoising and RS image fusion methods using unsupervised deep learning (DL). The unsupervised DL methods proposed are inspired by the deep image prior idea, which centers around training a convolutional neural network (CNN) in an unsupervised manner. Moreover, several novel points are proposed, such as sparse and low-rank ideas, the sensors' modulation transfer functions (MTFs) utilization, and the usage of Stein's unbiased risk estimate (SURE).

About the doctoral candidate

Han Van Nguyen was born in Hai Duong, Vietnam, in 1985. He received the B.S. degree in information and communication engineering from Hanoi University of Science and Technology, Hanoi, Vietnam, in 2008, and the M.S. degree in information and communication engineering from Chosun University, Gwangju, South Korea, in 2014. He has started pursuing the Ph.D. degree in electrical and computer engineering since 2019 with the University of Iceland, Reykjavik, Iceland. He joined the Faculty of Electrical and Electronic Engineering, Nha Trang University, Nha Trang, Vietnam, as a Lecturer in 2010. His research interests include digital image processing, remote sensing, machine learning, and deep learning.

Han Van Nguyen

Doctoral defense in Electrical and Computer Engineering - Han Van Nguyen