Abstract
In Chap. 5, anomaly detection is considered as one type of passive target detection which requires no prior target knowledge. Under this blind environment, a target to be found must be the one that stands out naturally in some sense without appealing for any prior knowledge. In many applications the targets of these types generally appear as anomalies and cannot be identified by visual inspection. This indicates that anomalies are indeed most interesting targets to hyperspectral image analysts because they provide crucial and critical information in data analysis because they are generally unknown and cannot be obtained a priori. Despite the fact that many algorithms are developed for anomaly detection, it is unfortunate that very little effort is devoted to characterization of anomaly detection in terms of several issues: how large is the size for a target to be considered as an anomaly? how strong is it for a target to be considered as an anomaly in response to its surroundings? how sensitive is an anomaly to noise? how far away are two anomalies to be discriminated as separate anomalies? This chapter investigates issues of how to characterize anomalies and also conducts comprehensive computer simulations and experiments to study the effects of such issues in anomaly detection.
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Chang, CI. (2016). Anomaly Detection Characterization. In: Real-Time Progressive Hyperspectral Image Processing. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-6187-7_14
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DOI: https://doi.org/10.1007/978-1-4419-6187-7_14
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