Remote sensing image data clustering is a tough task, which involves classifying the image without any prior information. Remote sensing imageclustering, in essence, belongs to a complex optimization problem, due to ...
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Remote sensing image data clustering is a tough task, which involves classifying the image without any prior information. Remote sensing imageclustering, in essence, belongs to a complex optimization problem, due to the high dimensionality and complexity of remote sensing imagery. Therefore, it can be easily affected by the initial values and trapped in locally optimal solutions. Meanwhile, remote sensing images contain complex and diverse spatial-spectral information, which makes them difficult to model with only a single objective function. Although evolutionary multiobjective optimization methods have been presented for the clustering task, the tradeoff between the global and local search abilities is not well adjusted in the evolutionary process. In this article, in order to address these problems, a multiobjective sine cosine algorithm for remote sensing imagedata spatial-spectral clustering (MOSCA_SSC) is proposed. In the proposed method, the clustering task is converted into a multiobjective optimization problem, and the Xie-Beni (XB) index and Jeffries-Matusita (Jm) distance combined with the spatial information term (SI_Jm measure) are utilized as the objective functions. In addition, for the first time, the sine cosine algorithm (SCA), which can effectively adjust the local and global search capabilities, is introduced into the framework of multiobjective clustering for continuous optimization. Furthermore, the destination solution in the SCA is automatically selected and updated from the current Pareto front through employing the knee-point-based selection approach. The benefits of the proposed method were demonstrated by clustering experiments with ten UCI datasets and four real remote sensing imagedatasets.
Recently, both semi-supervised clustering and cluster ensemble have received tremendous attention due to their accurate and reliable performance. There are mainly two kinds of existing semi-supervised clustering algor...
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Recently, both semi-supervised clustering and cluster ensemble have received tremendous attention due to their accurate and reliable performance. There are mainly two kinds of existing semi-supervised clustering algorithms called constraint-based and metric-based. In this paper, we present a semi-supervised clustering ensemble approach which takes both pairwise constraints and metric measure into account. Firstly, under the assistance of supervised information included pairwise constraints and labeled data, the approach generates different base clustering partitions respectively using constraint-based semi-supervised clustering and metric-based semi-supervised clustering, in which the latter develops a new metric function. Given the spatial particularity of image pixels, the metric considers spatial distribution of surrounding pixels besides inherent features of pixels in the process of image feature extraction. And then the target clustering is obtained by integrating those base clustering partitions into an ensemble function. Finally, we conduct experimental verification on general data sets and imagedata sets, and compare clustering performance of our approach with those of other approaches. Both theoretical analysis and experimental results demonstrate that the proposed method produces considerable improvement in clustering accuracy and yields superior clustering results over a number of representative clustering methods.
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