BSEN40500 Hyperspectral imaging

Academic Year 2024/2025

This course will introduce students to the interdisciplinary field of hyperspectral imaging. Hyperspectral imaging (HSI) is a broad term encompassing spatially resolved spectral data obtained through a variety of modalities (e.g. Raman scattering, Fourier transform infrared microscopy, fluorescence, visible or near-infrared). It goes beyond the capabilities of conventional imaging and spectroscopy by obtaining spatially resolved spectra from objects at spatial resolutions varying from the level of single cells up to macroscopic objects (e.g. foods).

In this module, students will learn how to become critically aware of the basic principles, practice and applications of hyperspectral imaging. Particular focus is given to the fundamentals of hyperspectral image acquisition and analysis. Standard approaches and configurations for obtaining hyperspectral imaging data, spatial and spectral pre-processing methods and methods of data selection will be explored and the underlying theory will be illuminated through the use of real world examples. The extension of traditional multivariate data analysis tools to hyperspectral data will be probed and students will apply these basic principles to examine established and emerging applications of hyperspectral imaging.

Students will participate in lectures, through group work in tutorials and laboratory work, where they will select and evaluate an application in hyperspectral imaging. Participants will also be provided with hyperspectral data to explore using chemometric tools developed in MATLAB.

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Curricular information is subject to change

Learning Outcomes:

1. Understand basic principles of light-matter interaction at different wavelength ranges and spatial scales
2. Understand concept of hyperspectral image acquisition & distinction between different modalities
3. Implement spectral and spatial techniques for hyperspectral image analysis
4. Investigate and evaluate the performance of HSI data from multiple samples to solve a classification problem and, analyse the data to give meaningful results (i.e. quantification, interpretation)

Indicative Module Content:

1. Structure of spectral imaging data
2. Distinguishing between different spectral imaging modalities
3. Concepts of spectral and spatial background
4. Why pretreatments are needed in spectral imaging
5. Mathematical procedures involved in the most commonly used pretreatments
6. Benefits of multivariate data analysis in spectral imaging
7. Purpose, principle & applications of PCA
8. Purpose, principle & applications of clustering techniques
9. Purpose, principle & applications of classification techniques
10. Analysis of spectral imaging data in MATLAB
11. Measurement of samples using hyperspectral imaging equipment


Student Effort Hours: 
Student Effort Type Hours
Lectures

12
Practical

12
Autonomous Student Learning

98
Total

122
Approaches to Teaching and Learning:
active/task-based learning; peer and group work; lectures; enquiry & problem-based learning 
Requirements, Exclusions and Recommendations
Learning Requirements:

Basic knowledge of Matlab or R software.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Exam (In-person): Exam on topics covered in course n/a Graded No

50
Assignment(Including Essay): Group and Individual Assignments on Hyperspectral Image Analysis n/a Graded No

50

Carry forward of passed components
No
 
Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Pre- and post feedback on assignments will be provided throughout the semester for group and individual assessments.

Hyperspectral imaging by Amigo, Jose Manuel; Data handling in science and technology, 2020,
Name Role
Saeedeh Mohammadi Tutor
Áine Ní Fhuaráin Tutor
Dr Junli Xu Tutor