Fusion Detection via Distance-Decay IoU and weighted Dempster-Shafer Evidence Theory
This work addresses the problem of robust object detection in remote sensing for applications requiring high accuracy under varying conditions, though it appears incremental as it builds on existing fusion methods with specific enhancements.
The paper tackles the challenge of all-day, all-weather object detection in remote sensing by fusing optical and synthetic aperture radar images, achieving a 20.13% improvement in average precision over optical-only detection on a self-built dataset.
In recent years, increasing attentions are paid on object detection in remote sensing imagery. However, traditional optical detection is highly susceptible to illumination and weather anomaly. It is a challenge to effectively utilize the cross-modality information from multi-source remote sensing images, especially from optical and synthetic aperture radar images, to achieve all-day and all-weather detection with high accuracy and speed. Towards this end, a fast multi-source fusion detection framework is proposed in current paper. A novel distance-decay intersection over union is employed to encode the shape properties of the targets with scale invariance. Therefore, the same target in multi-source images can be paired accurately. Furthermore, the weighted Dempster-Shafer evidence theory is utilized to combine the optical and synthetic aperture radar detection, which overcomes the drawback in feature-level fusion that requires a large amount of paired data. In addition, the paired optical and synthetic aperture radar images for container ship Ever Given which ran aground in the Suez Canal are taken to demonstrate our fusion algorithm. To test the effectiveness of the proposed method, on self-built data set, the average precision of the proposed fusion detection framework outperform the optical detection by 20.13%.