EffBaGAN: An Efficient Balancing GAN for Earth Observation in Data Scarcity Scenarios

Generative adversarial networks (GANs) can be used as a data augmentation technique in scenarios with limited labeled information and class imbalances, common issues in remote sensing datasets. The EfficientNet architecture has gained attention for achieving high accuracy with moderate computational cost. This work introduces efficient balancing generative adversarial network (EffBaGAN), a generative network specifically designed for the classification of multispectral remote sensing images based on EfficientNet, addressing data scarcity and class imbalances while minimizing network complexity. EffBaGAN is built upon a balancing generative adversarial network (BAGAN) architecture, incorporating a custom EfficientNet-based discriminator and generator. In particular, for the discriminator we propose reduced EfficientNet discriminator, a reduced version of EfficientNet-B0 adapted to multispectral imagery. The generator, residual EfficientNet generator, includes a residual EfficientNet-based path, which enhances the quality of the generated synthetic samples. In addition, a superpixel-based sample extraction procedure is used to further reduce the computational cost of the method. Experiments were conducted on large, very high-resolution multispectral images of vegetation, demonstrating that EffBaGAN achieves higher accuracy than other advanced classification methods, including vision transformers and residual BAGAN, while maintaining a significantly lower computational cost. In fact, EffBaGAN is more than twice as fast as the residual BAGAN, making it an efficient solution for remote sensing image classification in data-scarce environments.