The Network Based Method Spectral Unmixing Framework
An introduction to the Network Based Method concept regarding the spectral unmixing of hyperspectral images. Bringing together several years of research performed in the unmixing domain, the readers can understand the principles behind the theory of the NBM framework and corresponding algorithms. A comprehensive analysis is also made on the current stateofart of techniques and methods used to spectrally unmix remote sensing/hyperspectral data. The textbook course is intended for researchers, tutors, and students who want to read and understand the mathematical framework of NBM along with corresponding implemented algorithms. The full spectrum of spectral unmixing is covered, i.e. estimation of number of endmembers, endmember extraction, abundance estimation.
Pricing  Lifetime Access

$15.00
Course curriculum

01
Introduction
Show details 
02
Spectral Mixture Analysis
Show details Lesson 2: Spectral Mixing Models FREE PREVIEW
 Lesson 3: Linear Mixing Model FREE PREVIEW
 Lesson 4: Nonlinear Mixing Model

03
The Spectral Unmixing Concept
Show details Lesson 5: Overview of Spectral Unmixing FREE PREVIEW
 Lesson 6: Spectral Unmixing Frameworks (Part 1)
 Lesson 7: Spectral Unmixing Frameworks (Part 2)

04
Endmember Number Estimation and Extraction
Show details Lesson 8: Number of Endmembers and Endmember Extraction (Part 1)
 Lesson 9: Number of Endmembers and Endmember Extraction (Part 2)
 Lesson 10: Number of Endmembers and Endmember Extraction (Part 3)
 Lesson 11: Number of Endmembers and Endmember Extraction (Part 4)

05
Abundance Estimation
Show details Lesson 12: Abundance Estimation (Part 1)
 Lesson 13: Abundance Estimation (Part 2)
 Lesson 14: Abundance Estimation (Part 3)
 Lesson 15: Abundance Estimation (Part 4)
 Lesson 16: Fully Constrained Least Squared Method (FCLS)

06
The Network Concept in Hyperspectral Unmixing
Show details Lesson 17: Overview of the Network Concept in Hyperspectral Unmixing (Part 1)
 Lesson 18: Overview of the Network Concept in Hyperspectral Unmixing (Part 2)
 Lesson 19: Network Based Endmember Extraction (Part 1)
 Lesson 20: Network Based Endmember Extraction (Part 2)
 Lesson 21: Network Based Endmember Extraction (Part 3)
 Lesson 22: Network Based Estimation of the number of endmembers
 Lesson 23: Network Based Abundance Estimation
 Lesson 24: Detection of an endmember not extracted by the previous unmixing steps

07
Algorithms for Each Step
Show details Lesson 25: The Fractional Distance Algorithm (Part 1)
 Lesson 26: The Fractional Distance Algorithm (Part 2)
 Lesson 27: The Fractional Distance Algorithm (Part 3)
 Lesson 28: The NBM Algorithm (Part 1)
 Lesson 29: The NBM Algorithm (Part 2)

08
Datasets and Experiments  Fractional Distance
Show details Lesson 30: Synthetic Dataset (Part 1)
 Lesson 31: Synthetic Dataset (Part 2)
 Lesson 32: Real Dataset

09
Results and Discussion  Fractional Distance
Show details Lesson 33: Synthetic Data  Number of Endmembers
 Lesson 34: Synthetic Data  Endmember Extraction
 Lesson 35: Synthetic Data  Endmember number estimation and extraction
 Lesson 36: Real Data  Number of Endmembers
 Lesson 38: Real Data  Endmember Extraction
 Lesson 39: Real Data  Endmember number estimation and extraction

10
Datasets and Experiments  NBM Algorithm
Show details Lesson 40: Synthetic Dataset
 Lesson 41: Real Dataset

11
Results and Discussion  NBM Algorithm
Show details Lesson 42: Synthetic Data
 Lesson 43: Real Data

12
Conclusions and Bibliography
Show details Lesson 44: Conclusions
 Acknowledgements
 Bibliography
What will you learn?

Spectral Unmxing

Endmember Extraction

Endmember Number Estimation

Network Based Method

Abundance Estimation

Synthetic and Real Remote Sensing Datasets
Any Prerequisites?

Practically none.
Student Profile?

Under/post graduate students

Professionals and Companies

Master students and PhD candidates

Researchers and Academics