Chang Real-Time Progressive Hyperspectral Image Processing
1. Auflage 2016
ISBN: 978-1-4419-6187-7
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark
Endmember Finding and Anomaly Detection
E-Book, Englisch, 623 Seiten, eBook
ISBN: 978-1-4419-6187-7
Verlag: Springer US
Format: PDF
Kopierschutz: 1 - PDF Watermark
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Research
Autoren/Hrsg.
Weitere Infos & Material
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.