Parallel K-means clustering of remote sensing images based on mapreduce

Authors:
Zhenhua Lv;Yingjie Hu;Haidong Zhong;Jianping Wu;Bo Li;Hui Zhao
Affiliations:
Key Laboratory of Geographic Information Science, Ministry of Education, Geography Department, East China Normal University, Shanghai, China;Key Laboratory of Geographic Information Science, Ministry of Education, Geography Department, East China Normal University, Shanghai, China;Key Laboratory of Geographic Information Science, Ministry of Education, Geography Department, East China Normal University, Shanghai, China;Key Laboratory of Geographic Information Science, Ministry of Education, Geography Department, East China Normal University, Shanghai, China;Institute of Software Engineering, East China Normal University, Shanghai, China;Institute of Software Engineering, East China Normal University, Shanghai, China
Venue:
WISM'10 Proceedings of the 2010 international conference on Web information systems and mining
Year:
2010

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Abstract

The K-Means clustering is a basic method in analyzing RS (remote sensing) images, which generates a direct overview of objects. Usually, such work can be done by some software (e.g. ENVI, ERDAS IMAGINE) in personal computers. However, for PCs, the limitation of hardware resources and the tolerance of time consuming present a bottleneck in processing a large amount of RS images. The techniques of parallel computing and distributed systems are no doubt the suitable choices. Different with traditional ways, in this paper we try to parallel this algorithm on Hadoop, an open source system that implements the MapReduce programming model. The paper firstly describes the color representation of RS images, which means pixels need to be translated into a particular color space CIELAB that is more suitable for distinguishing colors. It also gives an overview of traditional K-Means. Then the programming model MapReduce and a platform Hadoop are briefly introduced. This model requires customized 'map/reduce' functions, allowing users to parallel processing in two stages. In addition, the paper detail map and reduce functions by pseudo-codes, and the reports of performance based on the experiments are given. The paper shows that results are acceptable and may also inspire some other approaches of tackling similar problems within the field of remote sensing applications.