计算机科学
云计算
棱锥(几何)
算法
台风
计算机数据存储
高光谱成像
遥感
人工智能
计算机硬件
气象学
操作系统
光学
物理
地质学
作者
Mengzhao Yang,Wei Song,Haibin Mei
出处
期刊:Sensors
[MDPI AG]
日期:2017-07-23
卷期号:17 (7): 1693-1693
被引量:7
摘要
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.
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