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![Hyperspectral Imagery Target Detection Using Improved Anomaly Detection and Signature Matching Methods]( "Hyperspectral Imagery Target Detection Using Improved Anomaly Detection and Signature Matching Methods")
Hyperspectral Imagery Target Detection Using Improved Anomaly Detection and Signature Matching Methods60,99 €![Using Multiple Robust Parameter Design Techniques to Improve Hyperspectral Anomaly Detection Algorithm Performance]( "Using Multiple Robust Parameter Design Techniques to Improve Hyperspectral Anomaly Detection Algorithm Performance")
Using Multiple Robust Parameter Design Techniques to Improve Hyperspectral Anomaly Detection Algorithm Performance60,99 €![Hyperspectral Imagery Target Detection Using Principal Component Analysis]( "Hyperspectral Imagery Target Detection Using Principal Component Analysis")
Hyperspectral Imagery Target Detection Using Principal Component Analysis60,99 €![Hyperspectral Imagery Target Detection Using the Iterative RX Detector]( "Hyperspectral Imagery Target Detection Using the Iterative RX Detector")
Hyperspectral Imagery Target Detection Using the Iterative RX Detector60,99 €![Improved Hyperspectral Image Testing Using Synthetic Imagery and Factorial Designed Experiments]( "Improved Hyperspectral Image Testing Using Synthetic Imagery and Factorial Designed Experiments")
Improved Hyperspectral Image Testing Using Synthetic Imagery and Factorial Designed Experiments60,99 €![An Integrated Architecture and Feature Selection Algorithm for Radial Basis Neural Networks]( "An Integrated Architecture and Feature Selection Algorithm for Radial Basis Neural Networks")
An Integrated Architecture and Feature Selection Algorithm for Radial Basis Neural Networks60,99 €![Feature Extraction Using Principal and Independent Component Analysis aor Hyperspectral Imagery]( "Feature Extraction Using Principal and Independent Component Analysis aor Hyperspectral Imagery")
Feature Extraction Using Principal and Independent Component Analysis aor Hyperspectral Imagery60,99 €
This research extends the emerging field of hyperspectral image (HSI) target detectors that assume a global linear mixture model (LMM) of HSI and employ independent component analysis (ICA) to unmix HSI images. Via new techniques to fully automate feature extraction, feature selection, and target pixel identification, an autonomous global anomaly detector, AutoGAD, has been developed for potential employment in an operational environment for real-time processing of HSI targets. For dimensionality reduction (initial feature extraction prior to ICA), a geometric solution that effectively approximates the number of distinct spectral signals is presented.