Includes preliminary background which is essential to those who work in hyperspectral imaging area

Develops sequential and progressive algorithms for finding endmembers as they relate to real time hyperspectral image processing

Designs algorithms for anomaly detection from causality and real time perspectives and investigates the effects of causality and real-time processing in anomaly detection

Includes supplementary 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.