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Journal of Graphics ›› 2026, Vol. 47 ›› Issue (3): 543-552.DOI: 10.11996/JG.j.2095-302X.2026030543

• Image Processing and Computer Vision • Previous Articles     Next Articles

Adapter fine-tuning SAM and low-frequency fusion for semantic segmentation of remote sensing images

LI Jingtao1,2, FENG Jun1,2(), ZHAO Zhihong1,2   

  1. 1 School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang Hebei 050043, China
    2 Hebei Key Laboratory of Electromagnetic Environmental Effects and Information Processing, Shijiazhuang Hebei 050043, China
  • Received:2025-10-16 Accepted:2026-02-06 Online:2026-06-30 Published:2026-06-30
  • Contact: FENG Jun
  • Supported by:
    Natural Science Foundation of Hebei Province(F2024210005);Science and Technology Program of Shijiazhuang(2512100201A);Scientific Research Plan Project of China Railway Nanning Bureau Group Co., Ltd.(G25-2)

Abstract:

To address the problems of low small-target segmentation accuracy, insufficient cross-modal feature extraction and unsatisfactory fusion effect in multimodal remote sensing image semantic segmentation models, a dual-branch multimodal network Segment Anything Model Low-Frequency Fusion (SAMLoF), based on adapter optimization and low-frequency information extraction and fusion was proposed. By efficiently fine-tuning the parameters of the Segment Anything Model image encoder through the proposed joint adapter structure, the model could learn specific knowledge in the field of remote sensing while maintaining its general capabilities. The specific low-frequency information input modules for different modal images were designed to provide more large-scale environmental structures and contextual information for the model through the Fast Fourier Transform. An efficient feature fusion module based on attention was proposed to promote the full fusion of relevant information in different modal features. SAMLoF was evaluated and compared with 15 methods on the Vaihingen and Potsdam datasets. Both mF1 and mIoU metrics of SAMLoF reached the optimal values. The results showed that SAMLoF could effectively extract and fuse different modal features in remote sensing images, especially generating accurate and smooth boundary contours for complex small target objects.

Key words: semantic segmentation of remote sensing images, segment anything model, adapter fine-tuning low-frequency information, feature fusion

CLC Number: