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Article Dans Une Revue INTERNATIONAL JOURNAL OF DIGITAL EARTH Année : 2016

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

Du Zhenhong
  • Fonction : Auteur
Yuhua Guo
  • Fonction : Auteur
Chuanrong Zhang
  • Fonction : Auteur
Zhang Feng
  • Fonction : Auteur
Liu Renyi
  • Fonction : Auteur
Weidong Li
  • Fonction : Auteur

Résumé

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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Dates et versions

hal-01389852 , version 1 (30-10-2016)

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Citer

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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