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 textbook FREE PREVIEW
- Lesson 2: Object Parameter Estimation workflow or Spectroscopy FREE 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 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)
- 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 experiment FREE 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
Under/post graduate students
Professionals and Companies
Master students and PhD candidates
Researchers and Academics
Based on Block-chain 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
Remote Sensing Expertdimsyk@gmail.com
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