Image and Signal Processing Methods

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

[+] Author Affiliations
Andrea Marinoni, Paolo Gamba

University of Pavia, Department of Electrical, Computer, and Biomedical Engineering, Telecommunications and Remote Sensing Laboratory, via Ferrata 5, Pavia I-27100, Italy

J. Appl. Remote Sens. 10(4), 045012 (Oct 27, 2016). doi:10.1117/1.JRS.10.045012
History: Received January 29, 2016; Accepted October 11, 2016
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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.

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Citation

Andrea Marinoni and Paolo Gamba
"Efficient detection of anomaly patterns through global search in remotely sensed big data", J. Appl. Remote Sens. 10(4), 045012 (Oct 27, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.045012


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