Nowadays, Satellite image retrieval could be a huge issue to induce information for natural disaster management, military target detection, meteorology, urban designing, harm assessment and change detection, etc. Basis on the image substance, content based image retrieval extracts the images that are relevant to the user given query image from massive image databases. Most of the existing image retrieval methods are still incompetent of providing retrieval outcome with elevated retrieval accuracy and not as much of computational intricacy. This paper proposed Fuzzy multi-characteristic clustering technique to realize this goal that is based on Fuzzy logic and clustering. Fuzzy sets used to represent the vagueness occur in user query, similarity measure and image substance. Clustering is an unsupervised method of classification that provides a small amount of control to clustering and improves the clustering performance drastically. The tentative results reveal that our proposed method can achieve significant precision and recall rates with better computational efficiency.

如今，卫星图像检索可能是一个巨大的问题，无法为自然灾害管理，军事目标检测，气象，城市设计，危害评估和变更检测等引入信息。基于图像实质，基于内容的图像检索可提取图像。与来自海量图像数据库的给定查询图像的用户相关。大多数现有的图像检索方法仍无法提供具有提高的检索精度且没有那么多计算复杂度的检索结果。本文提出了基于模糊逻辑和聚类的模糊多特征聚类技术来实现这一目标。用于表示模糊性的模糊集出现在用户查询，相似性度量和图像实质中。聚类是一种无监督的分类方法，可为聚类提供少量控制，并显着提高聚类性能。初步结果表明，我们提出的方法可以达到较高的精度和召回率，具有更好的计算效率。