Hyperdimensional Multimedia Perception and Frontier Security

Journal article

Junqing Huang, Shucheng Ji, Yapeng Wang, Min Xia, Xiaochen Yuan
IEEE Transactions on Geoscience and Remote Sensing, vol. 64, 2026, pp. 1-19

DOI: 10.1109/TGRS.2026.3650952

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APA Click to copy
Huang, J., Ji, S., Wang, Y., Xia, M., & Yuan, X. (2026). An SAM Fine-Tuning Framework With Frequency-Domain Interactive LoRA for Remote Sensing Change Detection. IEEE Transactions on Geoscience and Remote Sensing, 64, 1–19. https://doi.org/10.1109/TGRS.2026.3650952

Chicago/Turabian Click to copy
Huang, Junqing, Shucheng Ji, Yapeng Wang, Min Xia, and Xiaochen Yuan. “An SAM Fine-Tuning Framework With Frequency-Domain Interactive LoRA for Remote Sensing Change Detection.” IEEE Transactions on Geoscience and Remote Sensing 64 (2026): 1–19.

MLA Click to copy
Huang, Junqing, et al. “An SAM Fine-Tuning Framework With Frequency-Domain Interactive LoRA for Remote Sensing Change Detection.” IEEE Transactions on Geoscience and Remote Sensing, vol. 64, 2026, pp. 1–19, doi:10.1109/TGRS.2026.3650952.

BibTeX Click to copy

@article{huang2026a,
  title = {An SAM Fine-Tuning Framework With Frequency-Domain Interactive LoRA for Remote Sensing Change Detection},
  year = {2026},
  journal = {IEEE Transactions on Geoscience and Remote Sensing},
  pages = {1-19},
  volume = {64},
  doi = {10.1109/TGRS.2026.3650952},
  author = {Huang, Junqing and Ji, Shucheng and Wang, Yapeng and Xia, Min and Yuan, Xiaochen}
}

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Abstract: Achieving high-accuracy remote sensing change detection (RSCD) algorithms requires high-quality semantic feature extraction from remote sensing images (RSIs). Due to its powerful general-purpose feature extraction capability, the Segment Anything Model (SAM) has found wide application across diverse fields. However, SAM may not be optimally suited for RSIs. To address this limitation, we propose a Frequency-domain Interactive LoRA Fine-tuning Architecture (FILFArch) to enhance the performance of SAM in RSCD tasks. Based on FILFArch, we then develop two task-specific algorithms, the FILFBCD for Binary Change Detection (BCD), and the FILFSCD for Semantic Change Detection (SCD). To enhance the capability of SAM in capturing bi-temporal RSIs feature relationship, the Bi-temporal Feature Interactive LoRA (BIF-LoRA) is designed with Siamese architecture. Within BIF-LoRA, Frequency-Domain Feature Interaction (FDFI) utilizes Fast Fourier Transform Block (FFTB) to fuse bi-temporal frequency-domain features. This enables cross-temporal frequency-domain interaction, effectively discriminating spatio-temporal feature differences. Additionally, we use a shared BCD Decoder to serves as the binary change detector for both FILFBCD and FILFSCD. The BCD Decoder first applies a Coarse Difference Feature Extraction (CDFE) to coarsely fuse deep semantic features, yielding a coarse-grained change feature map. Subsequently, a Frequency-Domain Feature Enhancement (FDFE) refines these abstract features to generate a fine-grained change map. In FILFSCD, FDFE is further utilized to recover semantic change information of each temporal RSIs. Experimental results demonstrate that FILFBCD achieves the highest F1 scores of 83.53%, 66.75%, and 83.79% on BCD datasets MLCD, S2Looking, and SYSU-CD, respectively. Meanwhile, FILFSCD achieves the highest F1 scores of 64.05% and 87.02% on SCD datasets SECOND, and DSCD, respectively. These results demonstrate the effectiveness and versatility of the proposed FILFArch for RSCD tasks. The code is available at https://github.com/juncyan/filora