Efficient detection of anomaly patterns through global search in remotely sensed big data

Andrea Marinoni; Paolo Gamba

doi:10.1117/1.JRS.10.045012

27 October 2016 Efficient detection of anomaly patterns through global search in remotely sensed big data

Andrea Marinoni, Paolo Gamba

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 4, 045012 (October 2016). https://doi.org/10.1117/1.JRS.10.045012

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.

Citation Download Citation

Andrea Marinoni and 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

Published: 27 October 2016

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