ParSymG: a parallel clustering approach for unsupervised classification of remotely sensed imagery

Abstract : Symmetry is a common feature in the real world. It may be used to improve a classification by using the point symmetry-based distance as a measure of clustering. However, it is time consuming to calculate the point symmetry-based distance. Although an efficient parallel point symmetry-based K-means algorithm (ParSym) has been propsed to overcome this limitation, ParSym may get stuck in sub-optimal solutions due to the K-means technique it used. In this study, we proposed a novel parallel point symmetry-based genetic clustering (ParSymG) algorithm for unsupervised classification. The genetic algorithm was introduced to overcome the sub-optimization problem caused by inappropriate selection of initial centroids in ParSym. A message passing interface (MPI) was used to implement the distributed master–slave paradigm. To make the algorithm more time-efficient, a three-phase speedup strategy was adopted for population initialization, image partition, and kd-tree structure-based nearest neighbor searching. The advantages of ParSymG over existing ParSym and parallel K-means (PKM) alogithms were demonstrated through case studies using three different types of remotely sensed images. Results in speedup and time gain proved the excellent scalability of the ParSymG algorithm.
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https://hal-amu.archives-ouvertes.fr/hal-01389852
Contributor : Sébastien Mavromatis <>
Submitted on : Sunday, October 30, 2016 - 7:58:21 AM
Last modification on : Wednesday, September 12, 2018 - 1:27:27 AM

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Du Zhenhong, Yuhua Guo, Chuanrong Zhang, Zhang Feng, Liu Renyi, et al.. ParSymG: a parallel clustering approach for unsupervised classification of remotely sensed imagery. INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2016, ⟨10.1080/17538947.2016.1229818⟩. ⟨hal-01389852⟩

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