Advanced Remote Sensing

What it is about

You will learn fundamentals and advanced methods of remote sensing and machine learning. This includes supervised and unsupervised procedures for regression and classification, neural networks/Deep Learning, as well as fundamentals of microwave remote sensing and Synthetic Aperture Radar (SAR). In the end you will be able to select appropriate ML methods and apply them to practice-oriented remote sensing problems, and explain fundamental SAR methods.

What you will be able to do

• Understanding the fundamentals of machine learning theory
• Analyzing potentials and limits of different methods
• Applying modern machine-learning concepts to real problems
• Explaining fundamental methods and applications of Synthetic Aperture Radar

What the module consists of

• Lecture with integrated exercisesConveying system theory and machine-learning fundamentals; theoretical content and discussions
• Lecture: Introduction to SARIntroduction to SAR remote sensing and its applications
• Exercise/LabNumerical exercises with MATLAB and Python; completion as homework with interim reports

Teaching method

• Lecture/PresentationIntroduction and explanation of the theoretical content
• Discussion and practical examplesFosters deeper engagement with current topics
• Exercises and term papersSolidifies understanding through practice-oriented problem solving
• Lab exercises in MATLAB and PythonApply and implement the methods on numerical examples