Multiobjective Sine Cosine Algorithm for Remote Sensing Image Spatial-Spectral Clustering

Remote sensing image data clustering is a tough task, which involves classifying the image without any prior information. Remote sensing image clustering, in essence, belongs to a complex optimization problem, due to the high dimensionality and complexity of remote sensing imagery. Therefore, it can...

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Vydáno v:IEEE transactions on cybernetics Ročník 52; číslo 10; s. 11172 - 11186
Hlavní autoři: Wan, Yuting, Ma, Ailong, Zhang, Liangpei, Zhong, Yanfei
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.10.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Clustering
Clustering algorithms
Complexity
Datasets
Genetic algorithms
Image classification
Image data clustering
Indexes
knee point-based selection
Linear programming
multiobjective optimization
Multiple objective analysis
Optimization
Pareto optimization
Remote sensing
sine cosine
Spatial data
Task analysis
Trigonometric functions
ISSN:2168-2267, 2168-2275, 2168-2275
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Shrnutí:Remote sensing image data clustering is a tough task, which involves classifying the image without any prior information. Remote sensing image clustering, 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 image data 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 image datasets.
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ISSN:2168-2267
2168-2275
2168-2275
DOI:10.1109/TCYB.2021.3064552