TY - GEN
T1 - SAM-FE
T2 - 2025 IEEE International Conference on Multimedia and Expo, ICME 2025
AU - Huang, Junqing
AU - Liu, Tong
AU - Lam, Chan Tong
AU - Yuan, Xiaochen
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - Semantic change detection (SCD) is a crucial research topic in remote sensing. To achieve high-precision semantic segmentation results, Segment Anything Model-Guided Feature Enhancement (SAM-FE) is proposed. SAM-FE utilizes Mobile-SAM to extract features from bi-temporal remote sensing images (RSIs). In addition, the cross-temporal feature aggregation module (CTFA), the multiscale contextual information fusion module (MCIF), and the change feature enhancement module (CFE) are utilized to enhance the general features and the representation of the change information of the RSIs, thus improving the accuracy of change detection. Experimental results indicate that SAM-FE significantly outperforms the existing methods in both the Second datasets and MusSCD, with F1 of 0.6241 and 0.8316, respectively. Meanwhile, SAM-FE maintains lower parameters, demonstrating its superiority and practicality.
AB - Semantic change detection (SCD) is a crucial research topic in remote sensing. To achieve high-precision semantic segmentation results, Segment Anything Model-Guided Feature Enhancement (SAM-FE) is proposed. SAM-FE utilizes Mobile-SAM to extract features from bi-temporal remote sensing images (RSIs). In addition, the cross-temporal feature aggregation module (CTFA), the multiscale contextual information fusion module (MCIF), and the change feature enhancement module (CFE) are utilized to enhance the general features and the representation of the change information of the RSIs, thus improving the accuracy of change detection. Experimental results indicate that SAM-FE significantly outperforms the existing methods in both the Second datasets and MusSCD, with F1 of 0.6241 and 0.8316, respectively. Meanwhile, SAM-FE maintains lower parameters, demonstrating its superiority and practicality.
KW - Feature enhancement
KW - Multi-scale context information
KW - Remote sensing
KW - Segment anything model
KW - Semantic change detection
UR - https://www.scopus.com/pages/publications/105022654043
U2 - 10.1109/ICME59968.2025.11209043
DO - 10.1109/ICME59968.2025.11209043
M3 - Conference contribution
AN - SCOPUS:105022654043
T3 - Proceedings - IEEE International Conference on Multimedia and Expo
BT - 2025 IEEE International Conference on Multimedia and Expo
PB - IEEE Computer Society
Y2 - 30 June 2025 through 4 July 2025
ER -