A Machine learning approach for Object Parameter Estimation and Discrimination Using Hyperspectral Data
The textbook you need to understand spectroscopy for Remote Sensing
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.
Lesson 1: Introducing the contents of the textbookFREE PREVIEW
Lesson 2: Object Parameter Estimation workflow or SpectroscopyFREE PREVIEW
Lesson 3: Step by Step Analysis
Lesson 4: Spectral Discrimination
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
Lesson 16: Spectral Matching and LabelingFREE 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)
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)
Lesson 27: The Genetic Algorithm Concept
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
Lesson 32: Data and experiment
Lesson 33: Experiment Results
Lesson 34: Data and experimentFREE PREVIEW
Lesson 35: Experiment Results for NDR
Lesson 36: Experiment Results for MLR
Lesson 37: Experiment Results for PLSR
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)
Spectral Pre-Processing Algorithms
Simple Genetic Algorithm
Spectral Matching, Labeling, Discrimination
Spectral Similarity Measures
Practically none. Just some basic understanding of remote sensing
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