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.

中文翻译：

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遥感影像空间谱聚类的多目标正弦余弦算法。

遥感图像数据聚类是一项艰巨的任务，涉及在没有任何先验信息的情况下对图像进行分类。遥感图像聚类由于遥感图像的高维性和复杂性，实质上属于复杂的优化问题。因此，它很容易受到初始值的影响，并陷入局部最优解中。同时，遥感图像包含复杂多样的空间光谱信息，这使得仅凭一个目标函数就很难建模。尽管已经提出了用于聚类任务的进化多目标优化方法，但是在进化过程中并未很好地调整全局搜索能力和局部搜索能力之间的权衡。在本文中，为了解决这些问题，提出了一种用于遥感图像数据空间谱聚类的多目标正弦余弦算法。该方法将聚类任务转化为一个多目标优化问题，并将Xie-Beni（XB）指数和Jeffries-Matusita（Jm）距离与空间信息项（SI_Jm测度）相结合作为目标函数。此外，首次将可有效调整局部和全局搜索能力的正弦余弦算法（SCA）引入多目标聚类框架中，以进行连续优化。此外，通过采用基于拐点的选择方法，可以从当前Pareto前端自动选择和更新SCA中的目标解决方案。