RT Journal Article
T1 EffBaGAN: an efficient balancing GAN for Earth observation in data scarcity scenarios
A1 Vilela Pérez, Nicolás
A1 Blanco Heras, Dora
A1 Argüello Pedreira, Francisco
K1 BAGAN
K1 Classification
K1 Data augmentation
K1 EfficientNet
K1 Multispectral
K1 Residual generator
K1 Transformer
K1 Vegetation
AB Generative Adversarial Networks (GAN) 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 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 BAGAN architecture, incorporating a custom EfficientNet-based discriminator and generator. In particular, for the discriminator we propose RedEffDis, a reduced version of EfficientNet-B0 adapted to multispectral imagery. The generator, ResEffGen, includes a residual EfficientNet-based path, which enhances the quality of the generated synthetic samples. Additionally, 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.
PB IEEE
YR 2024
FD 2024-12-04
LK https://hdl.handle.net/10347/38377
UL https://hdl.handle.net/10347/38377
LA eng
NO N. Vilela-Pérez, D. B. Heras and F. Argüello, "EffBaGAN: An Efficient Balancing GAN for Earth Observation in Data Scarcity Scenarios," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 18, pp. 2477-2496, 2025, doi: 10.1109/JSTARS.2024.3510859.
NO Consellería de Cultura, Educación, Formación Profesional e Universidades, Xunta de Galicia
NO Agentia  Estatal de Investigación, Goberno de España
NO Fondo Europeo de Desenvolvemento Rexional (FEDER), Unión Europea
DS Minerva
RD 27 abr 2026