Explore anomaly detection techniques in hyperspectral imaging through this informative conference talk. Discover how hyperspectral imaging excels at detecting minor signals in heterogeneous mixtures, even when the signal of interest is small volumetrically but dominant in individual pixels. Learn about three methods for detecting minor target signals: generalized least squares (GLS) target detection, iterative GLS and extended least squares (ELS), and whitened principal components analysis (WPCA) targeted anomaly detection. Understand the mathematical similarities between these approaches and how they model clutter locally for flexible 'adaptive' models. Gain insights into targeted anomaly detection's expansion of the adaptive concept by characterizing target signals locally. Examine practical examples demonstrating the application of these techniques, including detection of contaminants in wheat gluten and hidden watermarks. Delve into topics such as PCA anomaly detection, T² weighting, iterative de-weighting, and the comparison between de-weighting and orthogonalization methods. Read more