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.

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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.

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  • Researchers and Academics

Some more information

  • Certificates of Completion

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About your Instructor

I'm a Remote Sensing and a Surveying Engineer. I received my degree from NTUA in 2010, where I also received my Ph.D. in hyperspectral remote sensing in 2016. From graduation in 2010, my career started as a Researcher Associate and Teaching Associate in the Laboratory of Remote Sensing of NTUA. From that time I also worked at several private companies as a Remote Sensing Expert and Geospatial Analyst. From the beginning of 2015 I was positioned as Senior Earth Observation Expert. During these years, I have participated in more than 20 funded European Commission and European Space Agency projects, have over 16 peer reviewed scientific publications in the field of Remote Sensing, and have an international patent in hyperspectral data compression.My main research and professional interests are in the optical remote sensing area, where I specialize in data (images, point measurements) processing and algorithm design and development. Some of the software tools that I operate to accomplish my research and business dreams are SNAP, ENVI, IDL, QGIS, ERDAS Imagine, ArcGIS, and Python. I have been working with these tools since 2008.

Dimitris Sykas

Remote Sensing Expert

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