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

Mengzhao Yang1, Wei Song2, Haibin Mei3

  • 1College of Information Technology, Shanghai Ocean University, Shanghai 201306, China. mzyang@shou.edu.cn.

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|July 25, 2017
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Summary
This summary is machine-generated.

This study introduces an efficient cloud-based method for storing and retrieving massive ocean remote sensing (RS) images. The approach enhances data management for critical applications like storm surge and typhoon warnings.

Keywords:
Hadoop systemfast retrievalmean-shift algorithmocean disasterspyramid HDFS storageremote sensing (RS)

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Area of Science:

  • Earth and Space Sciences
  • Computer Science
  • Data Science

Background:

  • High-resolution remote sensing (RS) images generate massive datasets, posing storage and retrieval challenges.
  • Efficiently accessing ocean RS data is crucial for timely disaster analysis, including storm surges and typhoon warnings.
  • Existing cloud storage and retrieval methods struggle with the scale and speed required for massive RS image datasets.

Purpose of the Study:

  • To develop an efficient cloud-based system for storing and retrieving massive ocean RS images.
  • To improve the performance of RS image data management for disaster analysis applications.
  • To address the challenges of large-volume data storage and rapid retrieval in oceanographic remote sensing.

Main Methods:

  • A distributed construction method using a pyramid model and a maximum hierarchical layer algorithm for efficient RS image storage on cloud platforms.
  • Implementation of a high-performance processing framework for massive RS images within the Hadoop ecosystem.
  • An improved mean-shift algorithm fused with the canopy algorithm, utilizing Hadoop MapReduce for RS image retrieval, based on a pyramid Hadoop Distributed File System (HDFS) storage method.

Main Results:

  • The proposed pyramid HDFS storage method demonstrates superior data storage performance compared to HDFS alone and WebGIS-based HDFS.
  • The integrated mean-shift and canopy algorithm achieves efficient retrieval of massive ocean RS images.
  • Speedup and scaleup performance show near-linear increases with the volume of RS images, confirming method efficiency.

Conclusions:

  • The developed cloud-based approach offers an efficient solution for managing and retrieving large-scale ocean RS image data.
  • The method significantly improves data storage and retrieval performance, crucial for rapid ocean disaster analysis.
  • The system's scalability and efficiency make it suitable for handling the growing volume of high-resolution remote sensing data.