Abstract––A novel and computationally efficient algorithm for autonomous detection and localization of anomalies in hyperspectral imagery is presented. Anomaly refers to any object whose spectral radiance does not comport with that of its immediate neighborhood. It is assumed that the spatial extent of the anomaly is smaller than a sensor detector footprint, and that it is entirely confined to a single image pixel. The technique developed here is an unsupervised learning algorithm that examines each pixel in the context of its immediate neighborhood without any a priori knowledge about the spatial and spectral characteristics of the expected background or potential anomalies. The image representing each of the spectral bands of the hyperspectral image under consideration is independently converted to a two-dimensional binary anomaly map, which lends itself to straightforward parallelization of the computational process. The composite anomaly map is then obtained by adding the entire set of anomaly maps to which a threshold is applied and detection decisions are subsequently made. The results of the application of the algorithm to hyperspectral cubes obtained from the AVIRIS data and color RGB images are presented. It is shown that the algorithm provides a robust anomaly detection methodology with very-low computational overhead. This new algorithm has demonstrated computational efficiency of over three orders of magnitude better than the Boeing computationally-enhanced version of the N-FINDR. Unlike the N-FINDR, real-time application of the new anomalous source detection algorithm appears practicable.
Keywords–– multispectral imaging, hyperspectral imaging, image recognition, algorithms, filters, passive remote sensing.
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