Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm
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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2017-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/17/7/1693 |
| _version_ | 1817989905919246336 |
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| author | Mengzhao Yang Wei Song Haibin Mei |
| author_facet | Mengzhao Yang Wei Song Haibin Mei |
| author_sort | Mengzhao Yang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T00:52:37Z |
| format | Article |
| id | doaj.art-7f9d88e3166a4664b4b9dc57e6ef607d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T00:52:37Z |
| publishDate | 2017-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7f9d88e3166a4664b4b9dc57e6ef607d2022-12-22T02:21:44ZengMDPI AGSensors1424-82202017-07-01177169310.3390/s17071693s17071693Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift AlgorithmMengzhao Yang0Wei Song1Haibin Mei2College of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Information Technology, Shanghai Ocean University, Shanghai 201306, ChinaThe 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.https://www.mdpi.com/1424-8220/17/7/1693remote sensing (RS)fast retrievalocean disastersmean-shift algorithmHadoop systempyramid HDFS storage |
| spellingShingle | Mengzhao Yang Wei Song Haibin Mei Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm Sensors remote sensing (RS) fast retrieval ocean disasters mean-shift algorithm Hadoop system pyramid HDFS storage |
| title | Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm |
| title_full | Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm |
| title_fullStr | Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm |
| title_full_unstemmed | Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm |
| title_short | Efficient Retrieval of Massive Ocean Remote Sensing Images via a Cloud-Based Mean-Shift Algorithm |
| title_sort | efficient retrieval of massive ocean remote sensing images via a cloud based mean shift algorithm |
| topic | remote sensing (RS) fast retrieval ocean disasters mean-shift algorithm Hadoop system pyramid HDFS storage |
| url | https://www.mdpi.com/1424-8220/17/7/1693 |
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