BSEN40500 Hyperspectral imaging

Academic Year 2022/2023

This module is designed for students who wish to understand and 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. A series of lectures will inform about standard approaches and configurations for obtaining hyperspectral imaging data, spatial and spectral pre-processing methods and methods of data selection will be introduced. The extension of traditional chemometric tools (e.g. PCA, PLS) to hyperspectral data will be presented. Hands on hyperspectral image analysis using MATLAB. Participants will 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. Understand basic chemometric techniques, pretreatments and image analysis required in HSI– why and when to use them
4. Ability to take HSI data from multiple samples, analyse the data to give meaningful results (i.e. quantification, interpretation)
5. Ability to code analysis for dataset of multiple HSI images in MATLAB

Student Effort Hours: 
Student Effort Type Hours




Autonomous Student Learning




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
Assignment: Report based on application of the techniques learned during the course to a set of hyperspectral images Week 9 n/a Graded No


Continuous Assessment: Tasks requiring student to apply data analysis methods to hyperspectral images. Varies over the Trimester n/a Graded No


Carry forward of passed components
Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
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.