RotCLIP: Tuning CLIP with visual adapter and textual prompts for rotation robust remote sensing image classification

In recent years, Contrastive Language-Image Pretraining (CLIP) has achieved remarkable success in a range of visual tasks by aligning visual and textual features. However, it remains a challenge to improve the robustness of CLIP for rotated images, especially for remote sensing images (RSIs) where objects can present various orientations. In this paper, we propose a Rotation Robust CLIP model, termed RotCLIP, to achieve the rotation robust classification of RSIs with a visual adapter and dual textual prompts. Specifically, we first compute the original and rotated visual features through the image encoder of CLIP and the proposed Rotation Adapter (Rot-Adapter). Then, we explore dual textual prompts to compute the textual features which describe original and rotated visual features through the text encoder of CLIP. Based on this, we further build a rotation robust loss to limit the distance of the two visual features. Finally, by taking advantage of the powerful image-text alignment ability of CLIP, we build a global discriminative classification loss by combining the prediction results of both original and rotated image-text features. To verify the effect of our RotCLIP, we conduct experiments on three RSI datasets, including the EuroSAT dataset used for scene classification, and the NWPU-VHR-10 and RSOD datasets used for object classification. Experimental results show that the proposed RotCLIP improves the robustness of CLIP against image rotation, outperforming several state-of-the-art methods.