Benchmarking Vision Transformers for Satellite Image Classification based on Data Augmentation Techniques
Enas Ali Mohammed, Amir Lakizadeh · International Journal of Advances in Soft Computing and its Applications · 2025
This article evaluates the implications on transformer model performance in satellite image classification by means of numerous data augmentation techniques using the Eurosat dataset. We examine the Swin-tiny, Swin-small, Convit-small, and Crossvit-small models under several augmentation methods including MixUp, CutMix, Geometric, WGP-GAN, and DCGAN. Our findings demonstrate that Mixup and WGP-GAN augmentations significantly enhance model performance; Swin small achieves 99.26% test accuracy the best. CutMix was more helpful for Swin-small than for Swin-tiny; geometric augmentation improved Swin-tiny and Crossvit-small. DCGAN behaved differently on many models. These results highlight the significance of selecting appropriate augmentation techniques suited for certain model architectures to increase performance in assignments needing satellite image classification.