Remote Sensing: Advanced

In this module we will dive into advanced methods and technologies in various dimensions for analyzing and interpreting remote image data. Building on foundational knowledge, we will explore further techniques that are essential for unlocking the full potential of remote sensing in modern applications.

Throughout this course, we will cover topics such as processing imaging spectroscopy, where we will learn how to extract meaningful information from the vast data captured by hyperspectral sensors. We will then explore time series data, enabling you to track and understand dynamic changes in the environment over time. Additionally, the course also addresses the topic of object-based analysis, a crucial technique that allows more accurate classification and interpretation of spatial patterns in remotely sensed images. We will also look at cutting-edge technologies such as convolutional neural networks (CNNs), which are revolutionizing the way we interpret and analyze imagery, particularly in the context of pattern recognition and deep learning. Finally, we will cover the principles of radar data analysis, a tool in remote sensing that should not be underestimated.

10 Nov
November 10, 2025 - February 9, 2026
Remote Sensing Advanced – Registration
09 Mar
March 9, 2026 - June 8, 2026
Remote Sensing Advanced – Registration
06 Jul
July 6, 2026 - October 5, 2026
Remote Sensing Advanced – Registration
09 Nov
November 9, 2026 - February 8, 2027
Remote Sensing Advanced – Registration

3 months

English

3 ECTS

The module is free of charge for UNIGIS students working to meet their elective subject requirements. Included are:

  • all related study materials
  • supervision and assessment
  • module accreditation according to the curriculum

ClubUNIGIS members can register at a price of € 210,-.

Included are:

  • all related study materials
  • supervision and assessment
  • course certificate upon completion of the module

  • Be aware of challenges in image classification, understand Artificial Intelligence concepts and important image operators and have practice with pixel calculations.
  • Be able to explain the principles of hyperspectral remote sensing.
  • Be able to analyze high-resolution spectra.
  • Know about available sensors.
  • Be aware of the importance of calibration.
  • Understand the principles of remote sensing time series,
  • Have explored the potential of image data cubes.
  • Know how about the concept of OBIA.
  • Know about the observation geometry of RADAR.
  • Have had your first experiences handling SAR data.
  • Get an basic understanding of a few ongoing trends.

Remote Sensing: Basics module or comparable knowledge.

We would like to inform you that this is an exclusively English language module, hence any kind of communication with the module lecturer should be in English. A discussion forum is maintained in moodle in order to support efficient module instruction. You are requested to submit all your questions related to this module to this forum only. The lecturer will check all incoming comments on a regular basis. He will answer your questions or provide you with pointers for solving your problems. The module is delivered in form of an instructed self-study that is based on explorative learning process. Theoretical concepts are complemented with practice oriented examples demonstrated with help of multimedia elements. Upon completion of the module students are requested to evaluate the module, which is a part of our quality assurance policy and practice.

ArcGIS Pro

SNAP

eCognition

Google Earth Engine

Jupyter notebook

ArcGIS Pro
Please contact UNIGIS.office@plus.ac.at if you have further questions on system requirements.

The assessment is based on your completed assignments. They must be submitted in written format to the Dropbox within the required time period. If assignments are submitted late, the lecturer is not obligated to grade them.

Lesson 1 – Introduction and basic concepts

Upon completion of this lesson you should be aware of challenges in image classification, understand Artificial Intelligence concepts and important image operators and have practice with pixel calculations.

Lesson 2 – Imaging spectroscopy 

Upon completing this lesson, you should be able to explain the principles of hyperspectral remote sensing, be able to analyze high-resolution spectra, know about available sensors, be aware of the importance of calibration and know ways to process and analyze hyperspectral data.

Lesson 3 – Time series

By the end of this lesson you should understand the principles of remote sensing time series, know different algorithms for trajectory analysis, be aware of post-classification change monitoring, and have explored the potential of image data cubes.

Lesson 4 – Object-based Image Analysis (OBIA)

By the end of this lesson you should know how about the concept of OBIA, be able to face challenges of segmentation algorithms, be able to explain multiresolution segmentation, have heard about KOS, be aware of pros and cons of Object-based Image Analysis.

Lesson 5 – Convolutional neural networks (CNN)

By the end of this lesson you understand the most important terminology, know how neural networks work with space, have gained some in-depth application experience, keep training the model.

Lesson 6 – Synthetic Aperture Radar (SAR)

By the end of this lesson, you should know about the observation geometry of RADAR, love the word “backscatter”, be aware of different acquisition modes, and have had your first experiences handling SAR data.

Lesson 7 – Future trends

By the end of this lesson you should get an basic understanding of a few ongoing trends.