Semantic-Fusion Gans for Semi-Supervised Satellite Image Classification
Most of the public satellite image datasets contain only a small number of annotated images. The lack of a sufficient quantity of labeled data for training is a bottleneck for the use of modern deep-learning based classification approaches in this domain. In this paper we propose a semi -supervised approach to deal with this problem. We use the discriminator (D) of a Generative Adversarial Network (GAN) as the final classifier, and we train D using both labeled and unlabeled data. The main novelty we introduce is the representation of the visual information fed to D by means of two different channels: the original image and its “semantic” representation, the latter being obtained by means of an external network trained on ImageNet. The two channels are fused in D and jointly used to classify fake images, real labeled and real unlabeled images. We show that using only 100 labeled images, the proposed approach achieves an accuracy close to 69% and a significant improvement with respect to other GAN-based semi-supervised methods. Although we have tested our approach only on satellite images, we do not use any domain-specific knowledge. Thus, our method can be applied to other semi-supervised domains.
Published in: 2018 25th IEEE International Conference on Image Processing (ICIP), 10.1109/ICIP.2018.8451836, Institute of Electrical and Electronics Engineers (IEEE)
- © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.