The textbook you need to understand spectroscopy for Remote Sensing.

Spectroscopy, Spectral Discrimination and Genetic Algorithms

So, what is object parameter estimation using spectral data, i.e. spectroscopy? What is spectral discrimination? Do they have something in common? Can a machine learning approach help to tackle both problem?

This textbook course answers all these questions and more! The textbook book course presents not only the basic theoretical principles of spectroscopy, spectral matching, labeling and discrimination, but also a new novel method, the k-step methodology, that automates the entire process. Both for object parameter estimation and spectral discrimination!

A machine learning approach is incorporated to achieve the full automation; the simple genetic algorithm.

For all these topics, extensive measurements were collected and experiments were performed in order to prove the concept.

Spectral measurements of  different varieties of plants (vetch and lentil) were used to showcase the subtle spectral discrimination concept.

Regarding the parameter estimation, soil spectral measurements were taken along with chemical analysis to quantify the soil organic matter.

A textbook to get started with automatic parameter estimation and discrimination using hyperspectral data

how spectral measurements can help estimate desired parameters or even discriminate phenomenical similar objects!
Enroll now and advance your skills

Course curriculum

  1. 01

    • Lesson 1: Introducing the contents of the textbook

    • Lesson 2: Object Parameter Estimation workflow or Spectroscopy

    • Lesson 3: Step by Step Analysis

    • Lesson 4: Spectral Discrimination
  2. 02

    • Lesson 5: Spectral Pre-Processing Algorithms (SPPAs)

    • Lesson 6: Smoothing

    • Lesson 7: Vector Normalization

    • Lesson 8: Value Normalization

    • Lesson 9: Discrete Fourier Transform

    • Lesson 10: Logarithm Transform

    • Lesson 11: Kubelka-Munck Transformation

    • Lesson 12: N Order Square Root Transformation

    • Lesson 13: Derivatives

    • Lesson 14: Continuum Removal

    • Lesson 15: Band Depth
  3. 03

    • Lesson 16: Spectral Matching and Labeling

    • Lesson 17: Similarity Measures

    • Lesson 18: Spectral Angle Mapper

    • Lesson 19: Cross Correlation

    • Lesson 20: Spectral Information Divergence

    • Lesson 21: SID – SAM Mixed Measure

    • Lesson 22: Continuum Intact Continuum Removed (CICR)
  4. 04

    • Lesson 23: Main Regression Algorithms Used

    • Lesson 24: Two Band Normalized Difference Regression (NDR)

    • Lesson 25: Multiple Linear Regression (MLR)

    • Lesson 26: Partial Least Squares Regression (PLSR)
  5. 05

    • Lesson 27: The Genetic Algorithm Concept
  6. 06

    • Lesson 28: Object Parameter Estimation (Part 1)

    • Lesson 29: Object Parameter Estimation (Part 2)

    • Lesson 30: Object Parameter Estimation (Part 3)

    • Lesson 31: Object Spectral Discrimination
  7. 07

    • Lesson 32: Data and experiment

    • Lesson 33: Experiment Results
  8. 08

    • Lesson 34: Data and experiment

    • Lesson 35: Experiment Results for NDR

    • Lesson 36: Experiment Results for MLR

    • Lesson 37: Experiment Results for PLSR
  9. 09

    • Lesson 38: Conclusions on Spectral Discrimination Use Case

    • Lesson 39: Conclusions on Parameter Estimation Use Case

    • Lesson 40: Overall Conclusions (Part 1)

    • Lesson 41: Overall Conclusions (Part 2)
  10. 10

    • References

What will you learn?

  • Spectral Pre-Processing Algorithms

  • Simple Genetic Algorithm

  • Spectral Matching, Labeling, Discrimination

  • Regression Algorithms

  • Spectroscopy

  • Spectral Similarity Measures

Any prerequisites?

  • Practically none. Just some basic understanding of remote sensing

Student Profile?

  • Under/post graduate students

  • Professionals and Companies

  • Master students and PhD candidates

  • Researchers and Academics

Some more information

  • Certificates of Completion

    After you successfully finish the course, you can claim your Certificate of Completion with NO extra cost! You can add it to your CV, LinkedIn profile etc

  • Available at any time! Study at your best time

    We know hard it is to acquire new skills. All our courses are self paced.

  • Online and always accessible

    Even when you finish the course and you get your certificate, you will still have access to course contents! Every time an Instructor makes an update you will be notified and be able to watch it for FREE

About your Instructor

Dimitris Sykas is a Remote Sensing and Surveying Engineer with a Ph.D. in hyperspectral remote sensing from the National Technical University of Athens (2016). He has spent fifteen years turning satellite data into operational products: at the NTUA Remote Sensing Laboratory, at Planetek Hellas as Senior EO Engineer, at Neuropublic building machine-learning models for smart farming and CAP monitoring, as a postdoctoral researcher on deep learning for agricultural Earth observation at the National Observatory of Athens, and as CTO of the cloudeo group. In 2024 he co-founded GOVADI, an Athens-based company building sovereign, on-premises AI systems for the Greek public sector and large enterprises, where he leads the technical direction of its products: Greek speech-to-text, retrieval-augmented generation with local models and embedding stores, document OCR and PII anonymisation. He has taken part in more than 25 funded EU and ESA projects, has authored over 23 peer-reviewed publications in remote sensing, and holds an international patent in hyperspectral data compression. He founded GEO University to bring hands-on GIS and Earth observation training online.

Dimitris Sykas

Remote Sensing Engineer, Ph.D. · Co-founder and Director, GOVADI · Founder, GEO University

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