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Journal of Applied Remote Sensing

Efficient detection of anomaly patterns through global search in remotely sensed big data
Author(s): Andrea Marinoni; Paolo Gamba
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Paper 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.

Paper Details

Date Published: 27 October 2016
PDF: 14 pages
J. Appl. Rem. Sens. 10(4) 045012 doi: 10.1117/1.JRS.10.045012
Published in: Journal of Applied Remote Sensing Volume 10, Issue 4
Show Author Affiliations
Andrea Marinoni, Univ. degli Studi di Pavia (Italy)
Paolo Gamba, Univ. degli Studi di Pavia (Italy)

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