Fusion Detection via Distance-Decay IoU and weighted Dempster-Shafer Evidence Theory

TL;DR

This paper introduces a fast multi-source fusion detection framework for remote sensing imagery that combines optical and SAR data using a novel distance-decay IoU and Dempster-Shafer evidence theory, achieving high accuracy in all-weather conditions.

Contribution

It proposes a new shape encoding method and a fusion strategy that reduces the need for large paired datasets, improving detection robustness and accuracy.

Findings

Outperforms optical detection by 20.13% in average precision

Effective in all-weather, all-day detection scenarios

Demonstrated on real-world satellite images of ships

Abstract

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%.