Angle Tokenization Guided Multi-Scale Vision Transformer for Oriented Object Detection in Remote Sensing Imagery
Cong Zhang, Tianshan Liu, Kin‐Man Lam · IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium · 2022
In this paper, an angle tokenization guided multi-scale Trans-former framework is proposed for oriented object detection in remote sensing images. Different from existing detectors that are based on convolutional neural networks (CNNs), our proposed method is based on a pyramid Transformer architecture with a compact and flexible angle tokenization module (ATM) to efficiently learn the orientation knowledge for rotated geospatial objects. The Transformer structure can progressively render long-range dependencies and multi-scale spatial details required for accurate localization, while the ATM provides robust guidance on feature refinement for angle prediction, jointly achieving end-to-end orientation de-tection. To the best of our knowledge, this is the first work to adapt Vision Transformers to remote sensing oriented object detection. Experimental results demonstrate the effectiveness and superiority of our method.