Multi- and Hyperspectral Imaging and Analysis 2026 (3+2hp)

This is the course webpage for the CVL PhD course Multi- and hyperspectral imaging and analysis, given in Spring 2026. The course provides an in-depth exploration of multi- and hyperspectral imaging technologies, their applications, and the analytical techniques used to interpret the data. Students will gain a comprehensive understanding of the principles, data acquisition methods, and advanced analysis techniques, preparing them for research and practical applications in various fields such as remote sensing, agriculture, environmental monitoring, and medical imaging.

This course will be given in hybrid mode - making it accessible also for PhD students outside LiU. See Examination.

Course examiner: Amanda Berg

Learning Objectives

By the end of this course, students will be able to:

• Understand the fundamental principles of multi- and hyperspectral imaging.
• Differentiate between multispectral and hyperspectral imaging technologies and their respective applications.
• Acquire and preprocess multi- and hyperspectral data.
• Apply advanced analytical techniques to interpret spectral data.
• Evaluate the strengths and limitations of different imaging systems.
• Conduct independent research using multi- and hyperspectral imaging.

Course Organization

• Lectures by a few different people.
• Article presentations at seminars by participants.
• Two lab visits (FOI + Termisk).
• One company visit (Vantor).
• An individual project and project presentation.

Examination

Credits for the course are divided in 3hp for the main course and 2hp for an optional project. To receive full credits (5hp), the participant must:

• Attend at least 10 of the sessions (lectures, lab/company visits, etc. 13 in total). At least two of them have to be a lab/company visit and at least one (preferrably two) a seminar.
• Provide written answers to study questions that will be given after each lecture.
• Participate actively in the seminars. Present one article and prepare questions/discussion points for another.
• Project report and presentation.
• Lectures, seminars, and project presentations will be possible to attend remotely for those outside LiU. The lab and company visits, however, have to be attended in person.

Course Outline

Date, time, and location will be announced as soon as they have been decided.

Lecture 1: Introduction to Spectral Imaging and Basics (Amanda/Jörgen)
February 24, 10.15-12 in Stora Visionen.

Lecture 2: Fundamentals of Multi- and Hyperspectral Imaging (Jörgen)
March 3, 10.15-12 in Zulu.

Lecture 3: Spectral Data Analysis (Maria A)
March 17, 10.15-12 in Zulu.

Lab visit 1: Termisk
April 1, 13.15-15 at Diskettgatan 11b, Linköping.

Lab visit 2: Demo by FOI (i.e. not really a lab visit)
April 17, 10.15-12 outside (exact location TBA) if weather is nice, otherwise in Systemet.

Lecture 4: Imaging within Remote Sensing @ Vantor (Leif and/or Folke)
April 28, 10.15-12 at Ebbegatan 13, Linköping.

Lecture 6: Applications in Remote Sensing (Yonghao)
May 6, 10.15-12 in Zulu.

Lecture 7: Applications in Medical Imaging (Maria M)
April 22, 10.15-12 in Zulu.

Seminar 1: Article presentations and discussions (Amanda)
April 22, 13.15-15 in Zulu.

Seminar 2: Article presentations and discussions (Amanda)
April 28, 13.15-15 at Ebbegatan 13, Linköping.

Seminar 3: Article presentations and discussions (Amanda)
May 12, 13.15-15 in Zulu.

Lecture 8: Project presentations (Amanda)

Seminars

Each course participant is expected to present one paper and prepare discussion points for another. The choice of paper should preferably be within the participant's own field. Below is a list of suggestions that may be chosen.

Abel A. Reyes-Angulo, and Sidike Paheding, Forward-Forward Algorithm for Hyperspectral Image Classification, PBVS 2024 [Link]
A. Runnemalm, J. Ahlberg, A. Appelgren, and S. Sjökvist, Automatic Inspection of Spot Welds by Thermography, Journal of nondestructive evaluation 2014 [Link]
I. Renhorn, D. Bergström, J. Hedborg, D. Letalick, and S. Möller, High spatial resolution hyperspectral camera based on a linear variable filter, Optical Engineering 2016 [Link]
Q. Xiao, L. Zhao and S. Chen, Tensor Low-Rank Sparse Representation Learning for Hyperspectral Anomaly Detection, IGARSS 2023 [Link]

Projects

Send me your project ideas, deadline project reports: May 29.

Recommended Reading

There is a compendium on the course Nextcloud file area.

This thermography reference manual from Teledyne FLIR covers both practical and theoretical aspects of infrared imaging. It’s designed to help users understand how to correctly capture, analyze, and interpret thermal images.

Registration

Register before Feb 17, 2026, by sending an email to the course examiner. Include the following details:

1. Your full name
2. LiU-ID (LiU PhD students only) and personal number
3. The name of your main supervisor

Related information

For more details, contact the course examiner.

See also the full list of PhD courses at the division.