  In this paper, we address the hyperspectral image (HSI) classification task
with a generative adversarial network and conditional random field (GAN-CRF)
-based framework, which integrates a semi-supervised deep learning and a
probabilistic graphical model, and make three contributions. First, we design
four types of convolutional and transposed convolutional layers that consider
the characteristics of HSIs to help with extracting discriminative features
from limited numbers of labeled HSI samples. Second, we construct
semi-supervised GANs to alleviate the shortage of training samples by adding
labels to them and implicitly reconstructing real HSI data distribution through
adversarial training. Third, we build dense conditional random fields (CRFs) on
top of the random variables that are initialized to the softmax predictions of
the trained GANs and are conditioned on HSIs to refine classification maps.
This semi-supervised framework leverages the merits of discriminative and
generative models through a game-theoretical approach. Moreover, even though we
used very small numbers of labeled training HSI samples from the two most
challenging and extensively studied datasets, the experimental results
demonstrated that spectral-spatial GAN-CRF (SS-GAN-CRF) models achieved
top-ranking accuracy for semi-supervised HSI classification.
