Aerial image recognition in discriminative bi-transformer

Yichen Zhao, Yaxiong Chen, Xiongbo Lu, Lei Zhou, Shengwu Xiong

Abstract: Highlights•To apply ViT to adaptively focus on more discriminative regions in aerial images, a novel transformer-based method, called D-BiT, is proposed, which achieves SOTA performance on several standard benchmarks.•To address the fitting dilemma of ViT caused by the lack of trainable aerial images, a Discriminative Label Smoothing Module (DLSM) is designed to enhance ViT to fully learn discriminative features without overfitting the distribution of noisy data, even in the condition of insufficient data volume.•Aiming at aerial images with complex backgrounds, a discriminative supervised contrastive loss is proposed to effectively combine label information and achieve a more compact and reasonable intra-class structure.