Semisupervised hyperspectral imagery classification based on a three-dimensional convolutional adversarial autoencoder model with low sample requirements

Although there are many state-of-the-art methods for hyperspectral classification, data deficiency is a problem that should be addressed before popularizing hyperspectral technology. To solve this problem, it is worth exploring methods based on small datasets. Inspired by the advanced deep learning classification methods and the autoencoder structure, we propose a structure named three-dimensional convolutional adversarial autoencoder that combines the two processes for semisupervised hyperspectral classification. Our experiments show its utility in data-deficient situations, and our study analyzes its advantages and disadvantages, and points out a probable direction toward optimization.