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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. 1.1Visible, NIR, SWIR and LWIR spectrum
  2. 1.2Light-matter interaction: reflection and absorption
  3. 1.3Spectral signatures of materials
  4. 1.4Radiometry and photometric quantities
  5. 1.5Emissivity and Planck's law
  6. 1.6Broadband illumination sources
  7. 1.7Multiband LED illumination
  8. 1.8Atmospheric transmission and spectral windows
  9. 1.9Effects of temperature and humidity
  10. 1.10Practical: reflectance measurement with a spectrometer
  1. 2.1Silicon CMOS sensors
  2. 2.2InGaAs detectors for SWIR
  3. 2.3Microbolometers for thermal infrared
  4. 2.4Mosaic filter cameras
  5. 2.5Pushbroom and line scanners
  6. 2.6Snapshot hyperspectral cameras
  7. 2.7Filter wheels and tunable filters
  8. 2.8Signal-to-noise ratio and spectral resolution
  9. 2.9Interfaces and data formats
  10. 2.10Case study: selecting a SWIR camera
  1. 3.1Dark current correction
  2. 3.2Calibration with a white reference
  3. 3.3Illumination normalisation
  4. 3.4Wavelength calibration
  5. 3.5Non-uniformity correction
  6. 3.6Thermal calibration with a blackbody
  7. 3.7Band-to-band registration
  8. 3.8Stability and recalibration
  9. 3.9Certified reflectance standards
  10. 3.10Lab: calibration of a hyperspectral cube
  1. 4.1Hyperspectral cubes and their structure
  2. 4.2MSC and SNV scatter correction
  3. 4.3Savitzky-Golay derivatives
  4. 4.4Spectral noise reduction
  5. 4.5Selection of relevant bands
  6. 4.6Principal component analysis
  7. 4.7Background segmentation
  8. 4.8Normalised spectral indices
  9. 4.9Handling large data volumes
  10. 4.10Practical: cube preprocessing in Python
  1. 5.1PLS regression for chemical properties
  2. 5.2PLS-DA discriminant analysis
  3. 5.3Support vector machines
  4. 5.4Random forests for spectra
  5. 5.5SIMCA and class modelling
  6. 5.6Cross-validation and external sets
  7. 5.7RMSEP and prediction errors
  8. 5.8Calibration transfer
  9. 5.9Interpretation of coefficients
  10. 5.10Workshop: PLS moisture model
  1. 6.11D networks for spectra
  2. 6.23D spectral-spatial networks
  3. 6.3Segmentation of hyperspectral cubes
  4. 6.4Autoencoders for dimensionality reduction
  5. 6.5Spectral anomaly detection
  6. 6.6Scarce data and spectral augmentation
  7. 6.7RGB and multispectral fusion
  8. 6.8Real-time inference
  9. 6.9Band explainability
  10. 6.10Lab: spectral classification with a 1D network
  1. 7.1Principles of infrared thermography
  2. 7.2Passive and active thermography
  3. 7.3Pulsed and lock-in thermography
  4. 7.4Detection of subsurface defects
  5. 7.5Inspection of electrical panels
  6. 7.6Measurement in thermal processes
  7. 7.7Emissivity correction
  8. 7.8ISO 9712 certification of thermographers
  9. 7.9Thermographic testing standards
  10. 7.10Practical: active thermographic inspection
  1. 8.1NIR-based plastic separation
  2. 8.2Municipal solid waste recycling
  3. 8.3Sorting of metals and glass
  4. 8.4Textile quality control
  5. 8.5Identification of black polymers
  6. 8.6High-speed optical sorters
  7. 8.7Synchronised pneumatic ejection
  8. 8.8Purity and recovery of fractions
  9. 8.9Circular economy and regulations
  10. 8.10Case study: sorting PET and PE
  1. 9.1Ripeness and quality of fruit
  2. 9.2Fat content in meat
  3. 9.3Foreign body detection
  4. 9.4Mycotoxins and contamination
  5. 9.5Sorting of grains and seeds
  6. 9.6In-line moisture analysis
  7. 9.7Food safety and traceability
  8. 9.8Validation of analytical methods
  9. 9.9Integration into packaging lines
  10. 9.10Practical: foreign body detection in NIR
  1. 10.1Pharmaceuticals: blend uniformity
  2. 10.2Process analytical technology (PAT)
  3. 10.3Detection of coatings and adhesives
  4. 10.4Semiconductor inspection
  5. 10.5Crop monitoring with drones
  6. 10.6Mining and mineral characterisation
  7. 10.7Infrared weld inspection
  8. 10.8Art and cultural heritage
  9. 10.9Multispectral satellite remote sensing
  10. 10.10Case study: pharmaceutical blend control
  1. 11.1In-line illumination systems
  2. 11.2Synchronisation with conveyor belts
  3. 11.3GPU and FPGA processing
  4. 11.4Communication with PLCs and ejectors
  5. 11.5Spectral vision software
  6. 11.6Edge architectures for hyperspectral imaging
  7. 11.7Calibration maintenance
  8. 11.8System validation in production
  9. 11.9Costs and investment
  10. 11.10Workshop: integrating an in-line sorter
  1. 12.1Definition of the spectral problem
  2. 12.2Selection of the range and sensor
  3. 12.3Acquisition and calibration design
  4. 12.4Building the database
  5. 12.5Development of the classification model
  6. 12.6Integration and in-line testing
  7. 12.7Performance analysis
  8. 12.8Conclusions and industrial proposal
  9. 12.9Master's Thesis report
  10. 12.10Master'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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