Thermal and Multispectral Imaging (4hp)

PhD course late spring 2018

Course topics:
Introduction to thermal and multi/hyperspectral imaging. Applicatons.
Radiometry: How radiative power is transferred from one surface to another, and what makes the surface(s) radiate at all. Radiometric concepts like radiance, transmittance, exitance, radiosity, ..., and their spectral and directional variations. Blackbodies, graybodies, emissivity spectra.
Sensors & cameras: Thermal camera basics. Different ways of constructing multi/hyperspectral cameras and sample the data cube.
Thermal image analysis: Temperature estimation. Target/background contrast. Small target ("hot spot") detection.
Hyperspectral image analysis: Atmosphere simulation and correction. Spectral modelling and dimensionality reduction. Classification. Spectral unmixing, end-members and abundance estimation.
Hyperspectral detection: Target, anomaly, and change detection.

The course will not cover how image sensors work (electron band gaps, ...) or things like camera geometry (perspective projection, lens distortion, ...), since this is covered by, e.g, TSBB09 Image Sensors. Rather, this is an expansion of the parts of TSBB09 on radiometry and thermal/multispectral sensors.

Linear algebra.
Elementary physics.
Course organisation:
Lectures by Jörgen.
Article presentations by the participants.
Two lab visits.
To receive credits, the participant must:
  • Attend at least 5 of the sessions (lectures, lab visits).
  • Present one article.
  • Propose at least one correction / improvement to the compendium.
  • Solve the exercises.
  • J. Ahlberg et al., "Thermal and Multispectral imaging," 2018. (chapters 1-5 somewhat stable, so far) [pdf]

  1. W. Zhao et al., "Object-Based Convolutional Neural Network for High-Resolution Imagery Classification," IEEE Journal on Selected Topics in Applied Earth Observations and Remote Sensing, 10(7):3386-3396, 2017. doi: 10.1109/JSTARS.2017.2680324. [pdf]
  2. Ma et al., "A Signal Processing Perspective on Hyperspectral Unmixing", IEEE Signal processing Magazine, 31(1):67-81, 2014. doi: 10.1109/MSP.2013.2279731 [pdf]
  3. L. S. Bernstein et al., "The QUick Atmospheric Correction (QUAC) Code: Algorithm Description and Recent Upgrades", Optical Engineering, 51(11), 2012. doi: 10.1117/1.OE.51.11.111719 [pdf]
  4. P. Ghamisi et al., "Advanced Spectral Classifiers for Hyperspectral Images: A review," IEEE Geoscience and Remote Sensing Magazine, 5(1):8-32, 2017. doi: 10.1109/MGRS.2016.2616418 [pdf]
1Mon Apr 2313:15SystemetLecture: Introduction.
Distribution of articles.
2Thu May 310:15NollställetLecture: Radiometry.
Lecture: Sensors & cameras.
3Mon May 713:15SystemetLecture: Thermal image analysis.
Article presentation 1: Zhao (Amanda)
4Wed May 1613:00FOILab visit: FOI (LVF, Imspec, LWIR-interferometer)
5Mon May 2113:15NollställetLecture: Hyperspectral image analysis.
6Thu May 2413:15NollställetArticle presentation 2: Ma (Gustav).
Article presentation 3: Bernstein (Karl).
7Mon May 2813:15NollställetLecture: Hyperspectral detection.
Article discussion 4: Ghamisi (together).
8Tue May 2915:15Tema entréLab visit: Telops Hyper-Cam Methane
For course credits: Amanda B, Karl H, Gustav H.
Not for course credits: Maria A, Hannes O.
Ask Jörgen Ahlberg.

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