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!

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.

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

Course curriculum

  • 02
    Background: Spectral Pre-Processing Algorithms
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    • Lesson 5: Spectral Pre-Processing Algorithms (SPPAs) FREE PREVIEW
    • 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
  • 03
    Background: Spectral Matching & Labeling and Similarity Measures
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    • Lesson 16: Spectral Matching and Labeling FREE PREVIEW
    • 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)
  • 04
    Background: Regression Algorithms
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    • 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)
  • 05
    Background: Machine Learning with the Simple Genetic Algorithm
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    • Lesson 27: The Genetic Algorithm Concept
  • 06
    Methodological Approach
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    • 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
  • 07
    Use case: Spectral Discrimination of Plant Species
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    • Lesson 32: Data and experiment
    • Lesson 33: Experiment Results
  • 08
    Use case: Soil Parameter Estimation
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  • 09
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    • 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
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    • 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

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