27 October 2016 Efficient detection of anomaly patterns through global search in remotely sensed big data
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Abstract
In order to leverage computational complexity and avoid information losses, “big data” analysis requires a new class of algorithms and methods to be designed and implemented. In this sense, information theory-based techniques can play a key role to effectively unveil change and anomaly patterns within big data sets. A framework that aims at detecting the anomaly patterns of a given dataset is introduced. The proposed method, namely PROMODE, relies on a representation of the given dataset performed by means of undirected bipartite graphs. Then the anomalies are searched and detected by progressively spanning the graph. The proposed architecture delivers a computational load that is less than that carried by typical frameworks in literature, so that PROMODE can be considered as a valid algorithm for efficient detection of change patterns in remotely sensed big data.
© 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)
Andrea Marinoni, Andrea Marinoni, Paolo Gamba, Paolo Gamba, } "Efficient detection of anomaly patterns through global search in remotely sensed big data," Journal of Applied Remote Sensing 10(4), 045012 (27 October 2016). https://doi.org/10.1117/1.JRS.10.045012 . Submission:
JOURNAL ARTICLE
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