The aim of this course is to get an overview of several methodologies/algorithms for analysis of different sensor specific information products. It is focused at students that like to deepen their knowledge and understanding of remote sensing for environmental applications.
Learning objective
The course is divided into two main parts, starting with a brief introduction to remote sensing imaging (4 lectures), and is followed by an introduction to different methodologies (8 lectures) for the quantitative estimation of bio-/geo-physical parameters. The main idea is to deepen the knowledge in remote sensing tools in order to be able to understand the information products, with respect to quality and accuracy.
Content
Each lecture will be composed of two parts: Theory: During the first hour, we go trough the main concepts needed to understand the specific algorithm. Practice: During the second hour, the student will test/develop the actual algorithm over some real datasets using Matlab. The student will not be asked to write all the code from scratch (especially during the first lectures), but we will provide some script with missing parts or pseudo-code. However, in the later lectures the student is supposed to build up some working libraries.
Lecture notes
Handouts for each topic will be provided.
Literature
Suggested readings: T. M. Lillesand, R.W. Kiefer, J.W. Chipman, Remote Sensing and Image Interpretation, John Wiley & Sons Verlag, 2008 J. R. Jensen, Remote Sensing of the Environment: An Earth Resource Perspective, Prentice Hall Series in Geograpic Information Science, 2000
Prerequisites / Notice
At the end of the course (last course day) there will a written exam.
Competencies
Subject-specific Competencies
Concepts and Theories
fostered
Techniques and Technologies
fostered
Method-specific Competencies
Analytical Competencies
assessed
Problem-solving
fostered
Personal Competencies
Critical Thinking
fostered
Performance assessment
Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course