The remote sensed satellite images are big repository of information and provide the coverage of large areas. However, these images may not be able to describe the finer details of area being covered. Satellite Image optimization is the process of augmenting the components of an image for better and effective interpretations from satellite images. In order to obtain better visibility properties to fetch more information, various artificial intelligence techniques can be considered for the optimization process. Finding out the best technique for optimization is a challenging and time-consuming task [U1]. In this paper, applications of swarm-based artificial intelligence techniques such as ant colony optimization, particle swarm optimization, bat algorithm, artificial bee colony algorithm etc. are being analysed to process the remote sensed data. The detailed comparison with respect to classifier, utility, images considered, and observation are discussed. The comprehensive analysis revealed that particle swarm optimization is the most widely used technique. Further, various application areas such as land-use land-cover are discussed with possibilities of future research [U2].

中文翻译：

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群算法在遥感数据处理中的应用分析

遥感卫星图像是信息的大仓库，并提供大面积的覆盖范围。但是，这些图像可能无法描述所覆盖区域的详细信息。卫星图像优化是增强图像组成部分的过程，以便更好地，有效地从卫星图像进行解释。为了获得更好的可见性属性以获取更多信息，可以为优化过程考虑各种人工智能技术。找出最佳的优化技术是一项艰巨而耗时的任务[U1]。本文分析了蚁群优化，粒子群优化，蝙蝠算法，人工蜂群算法等基于群体的人工智能技术在遥感数据处理中的应用。讨论了有关分类器，效用，所考虑的图像和观察的详细比较。综合分析表明，粒子群算法是应用最广泛的技术。此外，还讨论了各种应用领域，例如土地利用的土地覆盖以及未来的研究[U2]。