## 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 state-of-art 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.

## Course curriculum

1. 02
2. 03
3. 04
• 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)

4. 05
• 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)

5. 06
• 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

6. 07
• 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)

7. 08
• Lesson 30: Synthetic Dataset (Part 1)

• Lesson 31: Synthetic Dataset (Part 2)

• Lesson 32: Real Dataset

8. 09
• 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

9. 10
• Lesson 40: Synthetic Dataset

• Lesson 41: Real Dataset

10. 11
• Lesson 42: Synthetic Data

• Lesson 43: Real Data

11. 12
• 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?

• Professionals and Companies

• Master students and PhD candidates

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