In the image processing as well as analysis field, Content-Based Satellites Image Retrieval (CBSIR) is a vitalissue. Though there are numerousprevailing Image Retrieval (IR) methods, they still need improvement in the retrieval accuracy along with computational intricacy.Thus, this paper proposed an efficient CBSIR system utilizingWeighted Brownian Motion-based Monarch Butterfly Optimizations(WBMMBO). Initially, the Satellite Images (SI)is taken as the input. On account of the explosive augmentation of SI, the dataset is larger, which in turn increases the requisite for attaining the best retrieval system. Next, the Adjusted Intensity-based Variant of Adaptive histograms equalization (AIVA) enhances the images’ contrast. After that, the LPDF, DCD, BoVW, SF, along with BRIEF features are extracted. Then, the WBMMBO takes care of the Feature Selection (FS) process. Subsequently, the same process is executed for the Query Images (QI) as well. Subsequently, the similarity is computed between the chosen features of the QI and that of the inputted image utilizing MSSIM for retrieving the image. Lastly, theThreshold-centered Checking (TC) is employed to check the retrieved image. The tentative outcomesdisclose that the proposed work can attainnoteworthy precision in addition to recall rates with superior computational efficiency.

在图像处理和分析领域，基于内容的卫星图像检索（CBSIR）是一个至关重要的问题。尽管有许多流行的图像检索（IR）方法，但它们仍然需要提高检索精度和计算复杂性。因此，本文提出了一种利用基于加权布朗运动的帝王蝶优化（WBMMBO）的高效 CBSIR 系统。最初，卫星图像（SI）被作为输入。由于 SI 的爆炸性增强，数据集更大，这反过来又增加了获得最佳检索系统的必要条件。接下来，基于调整强度的自适应直方图均衡化 (AIVA) 变体增强了图像的对比度。之后，提取LPDF、DCD、BoVW、SF以及BRIEF特征。然后，WBMMBO 负责特征选择 (FS) 过程。随后，对查询图像（QI）也执行相同的处理。随后，使用 MSSIM 检索图像，计算 QI 的所选特征与输入图像的特征之间的相似性。最后，采用以阈值为中心的检查 (TC) 来检查检索到的图像。初步结果表明，除了具有卓越计算效率的召回率外，所提出的工作还可以获得显着的精度。以阈值为中心的检查（TC）用于检查检索到的图像。初步结果表明，除了具有卓越计算效率的召回率外，所提出的工作还可以获得显着的精度。以阈值为中心的检查（TC）用于检查检索到的图像。初步结果表明，除了具有卓越计算效率的召回率外，所提出的工作还可以获得显着的精度。