Real-Time Progressive Hyperspectral Image Processing Endmember Finding and Anomaly Detection
The book covers the most crucial parts of real-time hyperspectral image processing: causality and real-time capability. Recently, two new concepts of real time hyperspectral image processing, Progressive Hyperspectral Imaging (PHSI) and Recursive Hyperspectral Imaging (RHSI). Both of these can be us...
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| Format: | eBook |
| Language: | English |
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New York, NY
Springer New York
2016, 2016
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| Edition: | 1st ed. 2016 |
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| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Overview and Introduction
- Part I: Preliminaries
- Linear Spectral Mixture Analysis
- Finding Endmembers in Hyperspectral Imagery
- Linear Spectral Unmixing with Three Criteria, Least Squares Error, Simplex Volume and Orthogonal Projection
- Hyperspectral Target Detection
- Part II: Sample-wise Sequential Processes for Finding Endmembers
- Abundance-Unconstrained Sequential Endmember Finding Algorithms: Orthogonal Projection
- Fully Abundance-Constrained Sequential Endmember Finding Algorithms: Simplex Volume Analysis
- Partially Abundance Non-Negativity-Constrained Endmember Finding Algorithms: Convex Cone Volume Analysis
- Fully Abundance-Constrained Sequential Linear Spectral Mixture Analysis for Finding Endmembers
- Part III: Sample-Wise Progressive Processes for Finding Endmembers
- Abundance-Unconstrained Progressive Endmember Finding Algorithms: Orthogonal Projection
- Fully Abundance-Unconstrained Progressive Endmember Finding Algorithms: Simplex Volume Analysis
- Partially Abundance Non-Negativity-Constrained Progressive Endmember Finding Algorithms: Convex Cone Volume Analysis
- Sully Abundance-Constrained Progressive Linear Spectral Mixture Analysis for Finding Endmembers
- Part IV: Sample-Wise Progressive Unsupervised Target Detection
- Progressive Anomaly Detection
- Progressive Adaptive Anomaly Detection
- Progressive Window-Based Anomaly Detection
- Progressive Subpixel Target Detectio n and Classification