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Anomalous and matched hyperspectral change detection applications for SHARE data collection

Various hyperspectral change detection methods exist in the literature. Here prediction-based methods, such as chronochrome and covariance equalization, are reviewed and compared with a more recently developed model-based approach. These methods are typically applied for anomalous change detection. Several methods for extending these algorithms to achieve matched change detection are discussed. The algorithms are then applied to airborne visible to near infrared hyperspectral data collected recently over Rochester, New York.