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Sensors 2017, 17(7), 1693; https://doi.org/10.3390/s17071693

Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm

College of Information Technology, Shanghai Ocean University, Shanghai 201306, China
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Author to whom correspondence should be addressed.
Received: 21 June 2017 / Revised: 10 July 2017 / Accepted: 19 July 2017 / Published: 23 July 2017
(This article belongs to the Special Issue Marine Sensing)
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Abstract

The rapid development of remote sensing (RS) technology has resulted in the proliferation of high-resolution images. There are challenges involved in not only storing large volumes of RS images but also in rapidly retrieving the images for ocean disaster analysis such as for storm surges and typhoon warnings. In this paper, we present an efficient retrieval of massive ocean RS images via a Cloud-based mean-shift algorithm. Distributed construction method via the pyramid model is proposed based on the maximum hierarchical layer algorithm and used to realize efficient storage structure of RS images on the Cloud platform. We achieve high-performance processing of massive RS images in the Hadoop system. Based on the pyramid Hadoop distributed file system (HDFS) storage method, an improved mean-shift algorithm for RS image retrieval is presented by fusion with the canopy algorithm via Hadoop MapReduce programming. The results show that the new method can achieve better performance for data storage than HDFS alone and WebGIS-based HDFS. Speedup and scaleup are very close to linear changes with an increase of RS images, which proves that image retrieval using our method is efficient. View Full-Text
Keywords: remote sensing (RS); fast retrieval; ocean disasters; mean-shift algorithm; Hadoop system; pyramid HDFS storage remote sensing (RS); fast retrieval; ocean disasters; mean-shift algorithm; Hadoop system; pyramid HDFS storage
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Yang, M.; Song, W.; Mei, H. Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm. Sensors 2017, 17, 1693.

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