  With the development of deep learning, supervised learning has frequently
been adopted to classify remotely sensed images using convolutional networks
(CNNs). However, due to the limited amount of labeled data available,
supervised learning is often difficult to carry out. Therefore, we proposed an
unsupervised model called multiple-layer feature-matching generative
adversarial networks (MARTA GANs) to learn a representation using only
unlabeled data. MARTA GANs consists of both a generative model $G$ and a
discriminative model $D$. We treat $D$ as a feature extractor. To fit the
complex properties of remote sensing data, we use a fusion layer to merge the
mid-level and global features. $G$ can produce numerous images that are similar
to the training data; therefore, $D$ can learn better representations of
remotely sensed images using the training data provided by $G$. The
classification results on two widely used remote sensing image databases show
that the proposed method significantly improves the classification performance
compared with other state-of-the-art methods.
