Ship Target Search in Multisource Visible Remote Sensing Images Based on Two-Branch Deep Learning
Xiunan Li, Peng Chen, Jingsong Yang, Wentao An, Gang Zheng, Dan Luo, Aiying Lu · IEEE Geoscience and Remote Sensing Letters · 2024
Ship target search tasks aim to match specific ships across two or more satellite images. Like pedestrian and vehicle re-identification tasks in computer vision, accurate ship re-identification encounters challenges, including subtle differences between ships of the same type and substantial intra-instance variations due to satellite angle of view and spectral differences. To tackle these challenges, this paper introduces a deep learning-based two-branch framework for ship target search, integrating ship detection and re-identification tasks. One branch extracts the target ship features while the other captures the search region features. These features are then fused through a dedicated layer, and the final output is derived from the keypoint detection header. A new dataset was curated using Sentinel-2 and Gaofen-1 satellite data. Experimental results validate the robustness of the proposed method, achieving an accuracy of 94.37% on the new dataset. Our method’s scalability has been validated through experiments using CBERS-04 and Gaofen-6 satellite data.