The Network Based Method Spectral Unmixing Framework

# The Network Based Method Spectral Unmixing Framework

The complete theoretical framework with experiments and results

## Curriculum

### Introduction

• Lesson 1: Introducing the contents of the textbook FREE TRIAL

### Spectral Mixture Analysis

• Lesson 2: Spectral Mixing Models FREE TRIAL
• Lesson 3: Linear Mixing Model FREE TRIAL
• Lesson 4: Non-linear Mixing Model

### The Spectral Unmixing Concept

• Lesson 5: Overview of Spectral Unmixing FREE TRIAL
• Lesson 6: Spectral Unmixing Frameworks (Part 1)
• Lesson 7: Spectral Unmixing Frameworks (Part 2)

### Endmember Number Estimation and Extraction

• 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)

### Abundance Estimation

• 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)

### The Network Concept in Hyperspectral Unmixing

• 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

### Algorithms for Each Step

• 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)

### Datasets and Experiments - Fractional Distance

• Lesson 30: Synthetic Dataset (Part 1)
• Lesson 31: Synthetic Dataset (Part 2)
• Lesson 32: Real Dataset

### Results and Discussion - Fractional Distance

• 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

### Datasets and Experiments - NBM Algorithm

• Lesson 40: Synthetic Dataset
• Lesson 41: Real Dataset

### Results and Discussion - NBM Algorithm

• Lesson 42: Synthetic Data
• Lesson 43: Real Data

### Conclusions and Bibliography

• Lesson 44: Conclusions
• Acknowledgements
• Bibliography

### Dimitris Sykas

Remote Sensing Expert

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.

## Pricing

\$9.90 Regular Price