Multispectral imaging extracts rich spectral information from targets, which greatly expands the function of traditional imaging technology. Multispectral imaging is widely used in agriculture, military, medicine, industry, and meteorology. Because of the information redundancy in multispectral images, it is necessary to reduce the dimension by pre-processing. In recent years, most of the researchers have adopted the methods of pre-processing before classification. Based on the principles of feature selection, feature transformation, and feature extraction, common dimensionality reduction methods are introduced, and the advantages and disadvantages of them are discussed. Afterwards, classification methods are divided into traditional methods and deep learning methods, and their characteristics and application prospect are discussed. Through comparison, the former are cost-effective and have the mature theories, while the latter have strong adaptability and high classification accuracy. At present, methods could be optimized from the perspective of saving computing resources and using spectral information efficiently. In the future, traditional methods will be improved and comprehensively used, while new methods with stronger adaptability and precision will be developed. © 2021 ACM.