Enhancing Pix2Pix for Remote Sensing Image Classification

Remote sensing image classification is challenging due to low separation between different classes and difficulty in learning discriminative features. GAN (Generative Adversarial Model) is promising for this task due to the generator in reproducing samples and the discriminator for improving the generator. Among GANs variants for image translation and image classification tasks, Pix2Pix performs best. However, Pix2Pix is limited in explicitly capturing the relationship between the source domain and the reconstructed ones from the target domain. To address the above problem, an improved Pix2Pix is proposed in this paper, where a controller is added to Pix2Pix whose role is to improve classification performance and enhance training stability. Experiments demonstrate the effectiveness and advantages of the proposed approach.