Journal
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
Volume 14, Issue 11, Pages 2092-2096Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LGRS.2017.2752750
Keywords
Generative adversarial networks (GANs); scene classification; unsupervised representation learning
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Funding
- National Natural Science Foundation of China [41501485, 61331017]
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With the development of deep learning, supervised learning has frequently been adopted to classify remotely sensed images using convolutional networks. 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.
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