ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2503.12006
34
0

ROS-SAM: High-Quality Interactive Segmentation for Remote Sensing Moving Object

15 March 2025
Zhe Shan
Yang Liu
Lei Zhou
C. Yan
H. Wang
Xia Xie
ArXivPDFHTML
Abstract

The availability of large-scale remote sensing video data underscores the importance of high-quality interactive segmentation. However, challenges such as small object sizes, ambiguous features, and limited generalization make it difficult for current methods to achieve this goal. In this work, we propose ROS-SAM, a method designed to achieve high-quality interactive segmentation while preserving generalization across diverse remote sensing data. The ROS-SAM is built upon three key innovations: 1) LoRA-based fine-tuning, which enables efficient domain adaptation while maintaining SAM's generalization ability, 2) Enhancement of deep network layers to improve the discriminability of extracted features, thereby reducing misclassifications, and 3) Integration of global context with local boundary details in the mask decoder to generate high-quality segmentation masks. Additionally, we design the data pipeline to ensure the model learns to better handle objects at varying scales during training while focusing on high-quality predictions during inference. Experiments on remote sensing video datasets show that the redesigned data pipeline boosts the IoU by 6%, while ROS-SAM increases the IoU by 13%. Finally, when evaluated on existing remote sensing object tracking datasets, ROS-SAM demonstrates impressive zero-shot capabilities, generating masks that closely resemble manual annotations. These results confirm ROS-SAM as a powerful tool for fine-grained segmentation in remote sensing applications. Code is available atthis https URL.

View on arXiv
@article{shan2025_2503.12006,
  title={ ROS-SAM: High-Quality Interactive Segmentation for Remote Sensing Moving Object },
  author={ Zhe Shan and Yang Liu and Lei Zhou and Cheng Yan and Heng Wang and Xia Xie },
  journal={arXiv preprint arXiv:2503.12006},
  year={ 2025 }
}
Comments on this paper