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A Novel Approach for Multispectral Satellite Image Classification Based on the Bat Algorithm

机译：基于蝙蝠算法的多光谱卫星图像分类新方法

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摘要

Among the multiple advantages and applications of remote sensing, one of the most important uses is to solve the problem of crop classification, i.e., differentiating between various crop types. Satellite images are a reliable source for investigating the temporal changes in crop cultivated areas. In this letter, we propose a novel bat algorithm (BA)-based clustering approach for solving crop type classification problems using a multispectral satellite image. The proposed partitional clustering algorithm is used to extract information in the form of optimal cluster centers from training samples. The extracted cluster centers are then validated on test samples. A real-time multispectral satellite image and one benchmark data set from the University of California, Irvine (UCI) repository are used to demonstrate the robustness of the proposed algorithm. The performance of the BA is compared with two other nature-inspired metaheuristic techniques, namely, genetic algorithm and particle swarm optimization. The performance is also compared with the existing hybrid approach such as the BA with K-means. From the results obtained, it can be concluded that the BA can be successfully applied to solve crop type classification problems.
机译：在遥感的多种优点和应用中，最重要的用途之一是解决作物分类的问题，即区分各种作物类型。卫星图像是调查农作物耕地面积随时间变化的可靠来源。在这封信中，我们提出了一种基于蝙蝠算法（BA）的新型聚类方法，用于使用多光谱卫星图像解决作物类型分类问题。提出的分区聚类算法用于从训练样本中以最佳聚类中心的形式提取信息。然后对提取的聚类中心进行测试样本验证。来自加利福尼亚大学欧文分校（UCI）资料库的实时多光谱卫星图像和一个基准数据集用于证明所提出算法的鲁棒性。将BA的性能与其他两种受自然启发的元启发式技术进行比较，即遗传算法和粒子群优化。还将性能与现有的混合方法（例如具有K均值的BA）进行比较。从获得的结果可以得出结论，BA可以成功地用于解决作物类型分类问题。

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