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Proceedings Paper

Efficient feature extraction from wide-area motion imagery by MapReduce in Hadoop
Author(s): Erkang Cheng; Liya Ma; Adam Blaisse; Erik Blasch; Carolyn Sheaff; Genshe Chen; Jie Wu; Haibin Ling
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Paper Abstract

Wide-Area Motion Imagery (WAMI) feature extraction is important for applications such as target tracking, traffic management and accident discovery. With the increasing amount of WAMI collections and feature extraction from the data, a scalable framework is needed to handle the large amount of information. Cloud computing is one of the approaches recently applied in large scale or big data. In this paper, MapReduce in Hadoop is investigated for large scale feature extraction tasks for WAMI. Specifically, a large dataset of WAMI images is divided into several splits. Each split has a small subset of WAMI images. The feature extractions of WAMI images in each split are distributed to slave nodes in the Hadoop system. Feature extraction of each image is performed individually in the assigned slave node. Finally, the feature extraction results are sent to the Hadoop File System (HDFS) to aggregate the feature information over the collected imagery. Experiments of feature extraction with and without MapReduce are conducted to illustrate the effectiveness of our proposed Cloud-Enabled WAMI Exploitation (CAWE) approach.

Paper Details

Date Published: 19 June 2014
PDF: 8 pages
Proc. SPIE 9089, Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II, 90890J (19 June 2014); doi: 10.1117/12.2054690
Show Author Affiliations
Erkang Cheng, Temple Univ. (United States)
Liya Ma, Temple Univ. (United States)
Adam Blaisse, Temple Univ. (United States)
Erik Blasch, Air Force Research Lab. (United States)
Carolyn Sheaff, Air Force Research Lab. (United States)
Genshe Chen, Intelligent Fusion Technology, Inc. (United States)
Jie Wu, Temple Univ. (United States)
Haibin Ling, Temple Univ. (United States)


Published in SPIE Proceedings Vol. 9089:
Geospatial InfoFusion and Video Analytics IV; and Motion Imagery for ISR and Situational Awareness II
Matthew F. Pellechia; Kannappan Palaniappan; Shiloh L. Dockstader; Paul B. Deignan; Peter J. Doucette; Donnie Self, Editor(s)

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