Contextual Detection of Anomalies within Hyperspectral Images

摘要

The majority of anomaly detectors in Hyperspectral Imaging use only the statistical aspects of the spectral readings in the image. These detectors fail to use the spatial context that is contained in the images. The use of this information can yield detectors that out perform their spatially myopic counterparts. To demonstrate this, we will use an independent component analysis based detector, autonomous global anomaly detector (AutoGAD), developed at AFIT augmented to account for the spatial context of the detected anomalies. Through the use of segmentation algorithms, the anomalies identified are formed into regions. The size and shape of these regions are then used to decide if the region is anomalous or not. A Bayesian Belief Network structure is used to update a posterior probability of the region being anomalous.