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Master'sRobotics, Automation & Control Applications open

Master’s in Multispectral Vision Engineering

Online master's programme of 1,900 hours in multispectral vision engineering, structured in 12 modules covering spectral sensors, radiometric calibration, chemometrics, spectral deep learning, industrial thermography and material sorting according to ASTM E1655-17, ISO 18434-1:2008 and ISO 9712:2021, with an assigned technical tutor.

Duration1900 h
ModeOnline
LanguageEnglish
Start datesSet by the faculty
AwardSEIUM Institute diploma
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Learning outcomes

What you will be able to do

01

Understand how radiation interacts with materials.

02

Select multispectral, hyperspectral and thermal sensors.

03

Carry out radiometric and spectral calibration of systems.

04

Preprocess spectral data cubes.

05

Develop chemometric and deep learning models.

06

Apply thermography to industrial inspection.

07

Integrate multispectral systems into production lines.

Profile

Who it's for

  • Spectral vision engineers seeking a complete grounding from physics to deployment.
  • Chemometrics specialists moving into imaging.
  • Industrial thermographers.
  • Waste sorting engineers.
  • PAT process analysts in pharmaceutical or food production.
  • Hyperspectral imaging researchers with an industrial focus.
  • Engineering or science graduates seeking to enter the recycling sector.
Standards & tools

What you'll work with

  • ASTM E1655-17 (practices for infrared multivariate quantitative analysis)
  • ISO 18434-1:2008 (condition monitoring: thermography, general procedures)
  • ISO 9712:2021 (qualification and certification of NDT personnel)
  • ASTM E2617-17 (validation of empirically derived multivariate calibrations)
  • EMVA 1288 (characterisation of machine vision cameras and sensors)
  • ICH Q2(R2) (validation of analytical procedures)
  • ENVI
  • Perception Studio
  • Spectral Python (SPy)
  • PLS_Toolbox
  • Unscrambler X
Syllabus

12 modules · 1900 h

  1. 1.1. Visible, NIR, SWIR and LWIR spectrum
  2. 1.2. Light-matter interaction: reflection and absorption
  3. 1.3. Spectral signatures of materials
  4. 1.4. Radiometry and photometric quantities
  5. 1.5. Emissivity and Planck's law
  6. 1.6. Broadband illumination sources
  7. 1.7. Multiband LED illumination
  8. 1.8. Atmospheric transmission and spectral windows
  9. 1.9. Effects of temperature and humidity
  10. 1.10. Practical: reflectance measurement with a spectrometer
  1. 2.1. Silicon CMOS sensors
  2. 2.2. InGaAs detectors for SWIR
  3. 2.3. Microbolometers for thermal infrared
  4. 2.4. Mosaic filter cameras
  5. 2.5. Pushbroom and line scanners
  6. 2.6. Snapshot hyperspectral cameras
  7. 2.7. Filter wheels and tunable filters
  8. 2.8. Signal-to-noise ratio and spectral resolution
  9. 2.9. Interfaces and data formats
  10. 2.10. Case study: selecting a SWIR camera
  1. 3.1. Dark current correction
  2. 3.2. Calibration with a white reference
  3. 3.3. Illumination normalisation
  4. 3.4. Wavelength calibration
  5. 3.5. Non-uniformity correction
  6. 3.6. Thermal calibration with a blackbody
  7. 3.7. Band-to-band registration
  8. 3.8. Stability and recalibration
  9. 3.9. Certified reflectance standards
  10. 3.10. Lab: calibration of a hyperspectral cube
  1. 4.1. Hyperspectral cubes and their structure
  2. 4.2. MSC and SNV scatter correction
  3. 4.3. Savitzky-Golay derivatives
  4. 4.4. Spectral noise reduction
  5. 4.5. Selection of relevant bands
  6. 4.6. Principal component analysis
  7. 4.7. Background segmentation
  8. 4.8. Normalised spectral indices
  9. 4.9. Handling large data volumes
  10. 4.10. Practical: cube preprocessing in Python
  1. 5.1. PLS regression for chemical properties
  2. 5.2. PLS-DA discriminant analysis
  3. 5.3. Support vector machines
  4. 5.4. Random forests for spectra
  5. 5.5. SIMCA and class modelling
  6. 5.6. Cross-validation and external sets
  7. 5.7. RMSEP and prediction errors
  8. 5.8. Calibration transfer
  9. 5.9. Interpretation of coefficients
  10. 5.10. Workshop: PLS moisture model
  1. 6.1. 1D networks for spectra
  2. 6.2. 3D spectral-spatial networks
  3. 6.3. Segmentation of hyperspectral cubes
  4. 6.4. Autoencoders for dimensionality reduction
  5. 6.5. Spectral anomaly detection
  6. 6.6. Scarce data and spectral augmentation
  7. 6.7. RGB and multispectral fusion
  8. 6.8. Real-time inference
  9. 6.9. Band explainability
  10. 6.10. Lab: spectral classification with a 1D network
  1. 7.1. Principles of infrared thermography
  2. 7.2. Passive and active thermography
  3. 7.3. Pulsed and lock-in thermography
  4. 7.4. Detection of subsurface defects
  5. 7.5. Inspection of electrical panels
  6. 7.6. Measurement in thermal processes
  7. 7.7. Emissivity correction
  8. 7.8. ISO 9712 certification of thermographers
  9. 7.9. Thermographic testing standards
  10. 7.10. Practical: active thermographic inspection
  1. 8.1. NIR-based plastic separation
  2. 8.2. Municipal solid waste recycling
  3. 8.3. Sorting of metals and glass
  4. 8.4. Textile quality control
  5. 8.5. Identification of black polymers
  6. 8.6. High-speed optical sorters
  7. 8.7. Synchronised pneumatic ejection
  8. 8.8. Purity and recovery of fractions
  9. 8.9. Circular economy and regulations
  10. 8.10. Case study: sorting PET and PE
  1. 9.1. Ripeness and quality of fruit
  2. 9.2. Fat content in meat
  3. 9.3. Foreign body detection
  4. 9.4. Mycotoxins and contamination
  5. 9.5. Sorting of grains and seeds
  6. 9.6. In-line moisture analysis
  7. 9.7. Food safety and traceability
  8. 9.8. Validation of analytical methods
  9. 9.9. Integration into packaging lines
  10. 9.10. Practical: foreign body detection in NIR
  1. 10.1. Pharmaceuticals: blend uniformity
  2. 10.2. Process analytical technology (PAT)
  3. 10.3. Detection of coatings and adhesives
  4. 10.4. Semiconductor inspection
  5. 10.5. Crop monitoring with drones
  6. 10.6. Mining and mineral characterisation
  7. 10.7. Infrared weld inspection
  8. 10.8. Art and cultural heritage
  9. 10.9. Multispectral satellite remote sensing
  10. 10.10. Case study: pharmaceutical blend control
  1. 11.1. In-line illumination systems
  2. 11.2. Synchronisation with conveyor belts
  3. 11.3. GPU and FPGA processing
  4. 11.4. Communication with PLCs and ejectors
  5. 11.5. Spectral vision software
  6. 11.6. Edge architectures for hyperspectral imaging
  7. 11.7. Calibration maintenance
  8. 11.8. System validation in production
  9. 11.9. Costs and investment
  10. 11.10. Workshop: integrating an in-line sorter
  1. 12.1. Definition of the spectral problem
  2. 12.2. Selection of the range and sensor
  3. 12.3. Acquisition and calibration design
  4. 12.4. Building the database
  5. 12.5. Development of the classification model
  6. 12.6. Integration and in-line testing
  7. 12.7. Performance analysis
  8. 12.8. Conclusions and industrial proposal
  9. 12.9. Master's Thesis report
  10. 12.10. Master's Thesis: defence before a panel
Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. There are no fixed timetables: you progress at your own pace within each intake, so the programme is compatible with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is ENVI, Perception Studio, Spectral Python (SPy), PLS_Toolbox and Unscrambler X, in free, academic or evaluation versions. Tests are studied on real procedures: calibration of hyperspectral cameras, chemometric quality models and thermographic inspection of components. Assessment is continuous by module, plus the final Master's Thesis.

Standards covered: ASTM E1655-17, ISO 18434-1:2008, ISO 9712:2021, ASTM E2617-17, EMVA 1288, ICH Q2(R2).

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.
Standards-based learning:guided reading of ASTM E1655-17, ISO 18434-1:2008 and ISO 9712:2021 applied to real cases, not as a theoretical annex.
Industry software:ENVI, Perception Studio, Spectral Python (SPy), PLS_Toolbox and Unscrambler X, in free, academic or evaluation versions.
Master's Thesis with defence:a complete multispectral vision solution, from sensor selection and calibration to the classification model and in-line testing, defended before a panel.
Flexible format:100% online, no fixed timetables, compatible with full-time work.
Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Hyperspectral cube calibration
Calibrate a hyperspectral cube with dark, white and wavelength references.
Spectral preprocessing
Preprocess cubes in Python with scatter correction, derivatives and band selection.
Chemometric model
Build and validate a PLS or deep learning model for a quality property.
In-line sorter integration
Integrate the classifier with conveyors, PLCs and ejectors on a production line.
Master's Thesis
A complete multispectral inspection or sorting solution, defended before a panel.
Career opportunities

Where this takes you

Spectral vision engineerChemometrics specialistIndustrial thermographerWaste sorting engineerPAT process analystHyperspectral imaging researcher
Admissions & scholarships

Requirements

  • University degree in engineering, science or architecture, or equivalent demonstrable professional experience.
  • Internet connection and a computer capable of running the programme software.
  • English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with ENVI, Perception Studio, Spectral Python (SPy), PLS_Toolbox and Unscrambler X, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture, or equivalent professional experience, plus English sufficient to read technical standards.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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