卫星图像分割中多目标遗传模糊聚类的交互式方法

A. Mukhopadhyay
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引用次数: 4

摘要

卫星图像的分割问题可以归结为在强度空间中对像素进行聚类的问题。最近的一些研究将数据聚类问题作为一个多目标优化问题,其中多个聚类有效性指标同时优化以获得鲁棒聚类解。由于没有一组效度指标在所有类型的图像中表现都是一样的,因此确定一组最佳的同时进行优化的效度指标是一个重要的问题。本文研究了一种基于交互式遗传算法的多目标模糊聚类技术,用于卫星图像聚类问题。该算法在寻找聚类解的同时,进化出一组待优化的有效性测度。该方法周期性地与人类决策者(DM)交互,并自适应学习,以获得最优的有效性度量集以及最终的聚类结果。以印度加尔各答为例,对该技术的性能进行了验证,并与其他现有的聚类技术进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interactive approach to multiobjective genetic fuzzy clustering for satellite image segmentation
The problem of segmenting a satellite image can be posed as the task of clustering the pixels in the intensity space. Some recent studies have posed the problem of data clustering as a multiobjective optimization problem, where several cluster validity indices are simultaneously optimized to obtain robust clustering solutions. Since no validity index performs equally well in all kinds of images, identifying the best set of validity indices to be optimized simultaneously is therefore an important problem. In this article, we study an interactive genetic algorithm based multiobjective fuzzy clustering technique for satellite im-age clustering problem. The algorithm simultaneously finds the clustering solution as well as evolves the set of validity measures that are to be optimized simultaneously. The method periodically interacts with a human decision maker (DM) and adaptively learns to obtain the optimum set of validity measures along with the final clustering result. The performance of the technique has been demonstrated on an Indian city, Kolkata and compared with that of some other existing clustering techniques.
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