Remote-sensing image super-resolution using classifier-based generative adversarial networks

The rapid development of the aerospace industry has significantly increased the demand for remote-sensing images with high resolution and quality. Generating images with expected resolution from the samples obtained by common acquisition devices is a challenging task as the trade-off between cost and efficiency must be considered. We propose a super-resolution (SR) algorithm especially for remote-sensing images that is based on generative adversarial networks optimized by a classifier, which is called classifier-based super-resolution generative adversarial network (CSRGAN). We hypothesize that the confidence scores of classification can be a critical factor for representing the features in target remote-sensing images. To sufficiently take this factor into account during training, we add the class-score as an error into the loss function in addition to mean square error and high-dimensional features extracted from deep neural networks. Then, the classifier is utilized for both better SR performance and more precise classification. The classifier-testing branch of our system can also be flexibly combined with other network architectures to optimize SR performance on remote-sensing images. We validate the model on the NWPU-RESISC45 dataset considering both SR and classification performance. The final analysis is also provided and shows that the proposed CSRGAN outperforms existing algorithms.