Semisupervised hyperspectral image classification based on generative adversarial networks and spectral angle distance

Abstract

Collecting ground truth labels for hyperspectral image classification is difficult and time-consuming. Without an adequate number of training samples, hyperspectral image (HSI) classification is a challenging problem. Using generative adversarial networks (GANs) is a promising technique for solving this problem because GANs can learn features from both labeled and unlabeled samples. The cost functions widely used in current GAN methods are suitable for 2D nature images. Compared with natural images, HSIs have a simpler one-dimensional structure that facilitates image generation. Motivated by the one-dimensional spectral features of HSIs, we propose a novel semisupervised algorithm for HSI classification by introducing spectral angle distance (SAD) as a loss function and employing multilayer feature fusion. Since the differences between spectra can be quickly calculated using the spectral angle distance, the convergence speed of the GAN can be improved, and the samples generated by the generator model in the GAN are closer to the real spectrum. Once the entire GAN model has been trained, the discriminator can extract multiscale features of labeled and unlabeled samples. The classifier is then trained for HSI classification using the multilayer features extracted from a few labeled samples by the discriminator. The proposed method was validated on four hyperspectral datasets: Pavia University, Indiana Pines, Salinas, and Tianshan. The experimental results show that the proposed model provides very promising results compared with other related state-of-the-art methods.