Many topics discuss the processing of remote sensing hyperspectral imagery and its various application applications. This is because the hyperspectral image can easily distinguish between several materials that share the same spectral features. The amount of information contained in the hyperspectral image requires a special method to extract the information. The classification method is one solution to extract information contained in the hyperspectral image. Hyperspectral image classification

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... method consists of supervised classification, unsupervised classification and semi-supervised classification. Classification is a big challenge in remote sensing because of many factors that affect its success. These factors include images with low spatial resolution due to data quality, a complex area, homogeneous areas, and Hughes Phenomenon. Based on these problems, this study aims to review various classification methods used to solve existing classification problems. The results of this study recommend classification techniques that can be used based on performance parameters.