Real-time Progressive Hyperspectral Image Processing Endmember Finding And Anoma

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.