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Autoencoded Reduced Clusters For Anomaly Detection Enrichment (Arcade) In Hyperspectral Imagery, Brenden A. Mclean
Autoencoded Reduced Clusters For Anomaly Detection Enrichment (Arcade) In Hyperspectral Imagery, Brenden A. Mclean
Theses and Dissertations
Anomaly detection in hyper-spectral imagery is a relatively recent and important research area. The shear amount of data available in a many hyper-spectral images makes the utilization of multivariate statistical methods and artificial neural networks ideal for this analysis. Using HYDICE sensor hyper-spectral images, we examine a variety of preprocessing techniques within a framework that allows for changing parameter settings and varying the methodological order of operations in order to enhance detection of anomalies within image data. By examining a variety of different options, we are able to gain significant insight into what makes anomaly detection viable for these images, …