Adjacent Teacher: Semi-Supervised Oriented Object Detection Leveraging Adjacent Spatial Consistency Prior in Remote Sensing Images

Oriented object detection in remote sensing images (RSIs) relies heavily on costly annotated data. To alleviate this challenge, we propose a straightforward yet powerful approach for semi-supervised oriented object detection, termed adjacent teacher. Drawing inspiration from the first law of geography, “Everything is related to everything else, but near things are more related than distant things.” We observe that, in the adjacent space of RSIs, there is a widespread phenomenon that objects of the same category or closely related exhibit a clustered distribution and are roughly aligned in orientation. This discovery is generalized as the adjacent spatial consistency prior (ASCP), which reflects the consistent correlation between the categories and orientations of objects in the adjacent space of RSIs. Building on ASCP, two novel modules are introduced: low-confidence pseudo-label mining (LPM) and pseudo-label angle correcting (PAC). LPM boosts the number of reliable pseudo-labels by exploring low-confidence pseudo-labels that conform to the ASCP. PAC improves the quality of pseudo-labels by correcting their angles to satisfy ASCP. With these, adjacent teacher achieves state-of-the-art (SOTA) results on the DOTA-v1.5, SODA-A, and FAIR1M datasets, showing reduced missed detection rates and improved bounding box accuracy. Furthermore, the proposed method seamlessly integrates with existing pseudo-label-based semi-supervised oriented object detection models, significantly enhancing their performance. The code will be available at: https://github.com/Xia-tao/Adjacent-Teacher