Course Introduction

Course description

In this course, different SAR systems and processing techniques are introduced. It is designed for graduate students in Geographic Information Sciences and Remote Sensing. The course includes the following topics:

1. Introduction to Microwave Remote Sensing

2. SAR image processing

3. Introduction into SAR interferometry

4. Surface motion estimation with D-InSAR and derived methods

5. Surface motion estimation with pixel offset tracking

6. Applications of SAR in monitoring geohazards and infrastructure stability

Course objectives

After completion of the course, students will:

· Have knowledge on basic and advanced methods of SAR remote sensing.

· Be able to differentiate the advantages and disadvantages of SAR remote sensing and have an understanding for what applications SAR remote sensing is useful.

· Have knowledge on SAR interferometry, D-InSAR, and advanced methods.

· Gained practical experience in SAR data processing.

· Have knowledge on possible applications of SAR data.

· Be able to express the basic concepts and operations described in this course in English, both orally and in written form.

Course outline

· Lecture 1 (Prof. Timo Balz):

Ø Introduction to Microwave Remote Sensing

Ø Introduction to SAR

· Lecture 2 (Prof. Timo Balz):

Ø SAR image interpretation

Ø StereoSAR

Ø Pixel Tracking

Ø SAR geodesy

· Lecture 3 (Prof. Timo Balz):

Ø SAR interferometry

Ø Differential SAR interferometry

· Lecture 4 (Prof. Timo Balz / Prof. Zhang, Lu / Prof. Liao Mingsheng):

Ø Permanent Scatterer Interferometry

Ø Applications of Pixel Tracking

Ø Applications of PSI

· Lecture 5 (Prof. Timo Balz):

Ø Advanced PS-InSAR and TmoSAR

· Practical Course 2 (Prof. Timo Balz):

Ø PSI processing with WhuMPS

Reading List

No textbook to purchase. Lecture notes will be made available and the slides will be distributed via the Internet. Below are some of the reference books for the course:

1.Woodhouse, I., 2006, Introduction to Microwave Remote Sensing, Taylor & Francis

2.Lillesand & Kiefer, 2000, Remote Sensing & image interpretation, John Wiley

3.Jensen, 2006, Remote Sensing of the environment, Prentice Hall

Lecturer

Prof. Timo Balz, Prof. Liao Mingsheng, Prof. Zhang Lu

Course description

This short course will briefly introduce the fundamental positioning theory and methodologies based on the sensors and RF radios built in smartphones. It covers the positioning solutions for indoor and outdoor environments. The course will be concluded with a lecture of location-based services with live demonstrations.

Course objectives

Upon successful completion of this short course, students should

understand the fundamental theory and methodologies of smartphone positioning indoors/outdoors and have the fundamental knowledge of mobile location-based services.

Course outline

· Lecture1: (Prof. Chen Ruizhi / Prof. Chen Liang)

Ø Introduction to Ubiquitous Positioning in Smartphone

· Lecture2: (Prof. Chen Ruizhi)

Ø Outdoor Positioning with GNSS

· Lecture3: (Prof. Chen Liang)

Ø Indoor Positioning with RF Radio Signals (1)

· Lecture4: (Prof. Chen Liang)

Ø Indoor Positioning with RF Radio Signals (2)

· Lecture5: (Prof. Chen Ruizhi)

Ø Visual Positioning and Location-based services

· Lecture6: (Prof. Chen Ruizhi / Prof. Chen Liang)

Ø Project

Reading List

Handout and copies of PowerPoint slides

Lecturer

Prof. Chen Ruizhi, Prof. Chen Liang

Course description

The course aims at introducing the students to the main principles and algorithms of modern machine learning, along with some applications in such areas as computer vision and pattern recognition. Topics includes neural networks and deep learning, statistical learning theory, support vector machines, spectral clustering, game-theoretic methods etc.

Course objectives

Upon successful completion of this short course, students should

understand the fundamental theory and methodologies of Machine Learning.

Course outline

· Lecture1: (Prof. Marcello Pelillo)

Ø Neural Networks and Deep Learning

· Lecture2: (Prof. Marcello Pelillo)

Ø Statistical Learning Theory

· Lecture3: (Prof. Marcello Pelillo g)

Ø Support Vector Machines

· Lecture4: (Prof. Marcello Pelillo)

Ø Spectral Clustering

· Lecture5: (Prof. Marcello Pelillo)

Ø Game-theoretic Methods

· Lecture6: (Prof. Marcello Pelillo)

Ø Project

Lecturer

Prof. Marcello Pelillo