AutoGAD: An Improved ICA-Based Hyperspectral Anomaly Detection Algorithm

This work extends the emerging field of hyperspectral imagery (HSI) anomaly detectors and employs independent component (IC) analysis (ICA) to identify anomalous pixels. Using new techniques to fully automate feature extraction, feature selection, and anomaly pixel identification, an Autonomous Global Anomaly Detector has been developed for employment in an operational environment for real-time processing of HSI. Dimensionality reduction, which is the initial feature extraction prior to ICA, is effected through a geometric solution that estimates the number of retained principal components. The solution is based on the theory of the shape of the eigenvalue curve of the covariance matrix of spectral data containing noise. This research presents two new features, namely, potential anomaly signal-to-noise ratio and maximum pixel score, both of which are computed for each of the ICs to create a new 2-D feature space where the overlap between anomaly and nonanomaly classes is reduced. After anomaly feature selection, adaptive noise filtering is applied iteratively to suppress the background. Finally, a zero-detection histogram method is applied to the smoothed signals to identify anomaly locations to the user. After the algorithm is fully developed, a set of designed experiments are conducted to identify a reasonable set of input parameters.