Unsupervised PolSAR image classification based on ensemble partitioning

Yin, Xiaoshuang; Song, Hui; Yang, Wen; He, Chu; Xu, Xin

doi:10.1109/igarss.2013.6723503

Cited by 3 publications

(1 citation statement)

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“…To classify such high-dimensional complex data with a large number of classes, in recent years researchers have proposed several techniques. Some of these are pure classification techniques [10][11][12], while others use clustering algorithms to classify data [30][31][32][33][34]. The major issue with these techniques is the poor performance of classifying high-dimensional data with a large number of classes in terms of classification accuracy and computation cost.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Gamma Mixture Model-Based Method for Classification of High-Dimensional Data

Azhar

Huang

2019

Entropy

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Data classification is an important research topic in the field of data mining. With the rapid development in social media sites and IoT devices, data have grown tremendously in volume and complexity, which has resulted in a lot of large and complex high-dimensional data. Classifying such high-dimensional complex data with a large number of classes has been a great challenge for current state-of-the-art methods. This paper presents a novel, hierarchical, gamma mixture model-based unsupervised method for classifying high-dimensional data with a large number of classes. In this method, we first partition the features of the dataset into feature strata by using k-means. Then, a set of subspace data sets is generated from the feature strata by using the stratified subspace sampling method. After that, the GMM Tree algorithm is used to identify the number of clusters and initial clusters in each subspace dataset and passing these initial cluster centers to k-means to generate base subspace clustering results. Then, the subspace clustering result is integrated into an object cluster association (OCA) matrix by using the link-based method. The ensemble clustering result is generated from the OCA matrix by the k-means algorithm with the number of clusters identified by the GMM Tree algorithm. After producing the ensemble clustering result, the dominant class label is assigned to each cluster after computing the purity. A classification is made on the object by computing the distance between the new object and the center of each cluster in the classifier, and the class label of the cluster is assigned to the new object which has the shortest distance. A series of experiments were conducted on twelve synthetic and eight real-world data sets, with different numbers of classes, features, and objects. The experimental results have shown that the new method outperforms other state-of-the-art techniques to classify data in most of the data sets.

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