Hyperspectral Imagery Target Detection Using Improved Anomaly Detection and Signature Matching Methods
Timothy E. Smetek
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Hyperspectral Imagery Target Detection Using Improved Anomaly Detection and Signature Matching Methods

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This research extends the field of hyperspectral target detection by developing autonomous anomaly detection and signature matching methodologies that reduce false alarms relative to existing benchmark detectors. The proposed anomaly detection methodology adapts multivariate outlier detection algorithms for use with hyperspectral datasets containing thousands of high-dimensional spectral signatures. In so doing, the limitations of existing, non-robust anomaly detectors are identified, an autonomous clustering methodology is developed to divide an image into homogeneous background materials, an...