In recent years remote sensing is used for the knowledge elicitation of earth's surface and atmosphere on a very global scale. Land cover mapping is a method for acquiring the geo-spatial information from satellite data. The land cover problem is solved by image classification of the satellite image. This paper focuses on the optimised approach of image classification of satellite multi spectral images. This paper deals with the land cover mapping by using swarm computing techniques. Here we have used the improved Ant Miner algorithm i.e, cant Miner and the Hybrid PSO-ACO algorithm for image classification. The motivation of this paper is to explore the improved swarm computing algorithms for the satellite image classification.
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机译：近年来，遥感用于在非常全球范围内地球表面和大气的知识引发。 Land Cover Mapping是一种从卫星数据获取地理空间信息的方法。通过卫星图像的图像分类解决了土地覆盖问题。本文重点介绍卫星多光谱图像的图像分类的优化方法。本文通过使用群计算技术处理土地覆盖映射。在这里，我们使用了改进的蚂蚁矿工算法i.e，Cant矿工和混合PSO-ACO算法进行图像分类。本文的动机是探讨卫星图像分类的改进的群计算算法。
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