Remote sensing image recognition network design based on filter pruning and lightweight GAN

With the application of deep learning in object detection and recognition, remote sensing image recognition has also developed rapidly. However, the existing remote sensing image datasets are generally small, and remote sensing images are abundant and messy, so we use GAN to generate fake images that can be confused with real ones and build on this by using cGAN to expand the remote sensing datasets. At the same time, the existing research does not pay enough attention to low-resolution image recognition. We use the high-resolution model learning and the extended datasets to train classifiers, which can learn more general features conducive to low-resolution image recognition to improve the accuracy of the model used for remote sensing image recognition. In particular, we prune the model to reduce the weight redundancy of the network structure. Experiments demonstrate that on the SAT-4 dataset, our work both presses model size and maintains high model accuracy while effectively identifying remote sensing images.