A Survey on Hadoop Assisted K-Means Clustering of Hefty Volume Images
Journal Title: International Journal on Computer Science and Engineering - Year 2014, Vol 6, Issue 3
Abstract
The objects or the overview of the objects in a remote sensing image can be detected or generated directly through the use of basic K-means clustering method. ENVI, ERDAS IMAGINE are some of the software that can be used to get the work done on PCs. But the hurdle to process the large amount of remote sensing images is limitations of hardware resources and the processing time. The parallel or the distributed computing remains the right choice in such cases. In this paper, the efforts are put to make the algorithm parallel using Hadoop MapReduce, a distributed computing framework which is an open source programming model. The introductory part explains the color representation of remote sensing images. There is a need to convert the RGB pixel values to CIELAB color space which is more suitable for distinguishing colors. The overview of the traditional K-means is provided and in the later part programming model MapReduce and the Hadoop platform for K-Means is described. To achieve this parallelization of the algorithm using the customized MapReduce functions in two stages is essential. The map and reduce functions for the algorithm are described by pseudo-codes. This method will be useful in the many similar situations of remote sensing images.
Authors and Affiliations
Anil R Surve , Nilesh S Paddune
Keywords
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