Shiqi Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Shiqi Li has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Junyu Wei
College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
1 shared publication - Shaojing Su
College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
1 shared publication - Tao Ou
School of Mechanical and Vehicle Engineering, Hunan University, Changsha 410012, China
1 shared publication - Weijia Gao
College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
1 shared publication - Yongqi Li
College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
1 shared publication - Zhendong Wang
College of Intelligence Science and Technology, National University of Defense Technology, Changsha 410073, China
1 shared publication
Latest Publications
- Open Access
- 25 November 2025
- Published by MDPI AG in Electronics
- Vol. 14 (23) , 4628
An Innovative Multi-Scale Feature Fusion Network for Change Detection of Remote Sensing Images
Change detection in remote sensing images is crucial for various applications such as military reconnaissance and urban management. However, traditional change detection methods suffer from low accuracy and complex operations. Meanwhile, existing deep learning approaches struggle to fully understand multi-scale semantic information, and thus still face limitations in accuracy and generalization capability. To overcome these limitations, this paper proposed the MSTAN, which consists of a multi-scale Transformer encoder and a decoder centered on a four-layer ASFF module. The four-layer ASFF module dynamically learns spatially adaptive weights to capture multi-scale semantic information. Comparative experiments demonstrate that MSTAN achieves high-precision change detection. Cross-dataset evaluation experiments demonstrate that MSTAN possesses strong generalization ability. Ablation experiments confirm the effectiveness of the four-layer ASFF module in fusing multi-scale features. Complexity analysis quantifies the computational overhead of the four-layer ASFF module. These results highlight MSTAN’s powerful generalization capability and its promising potential for change detection tasks.
- 25 November 2025
- Published by MDPI AG in Electronics
- Vol. 14 (23) , 4628
Open AccessAn Innovative Multi-Scale Feature Fusion Network for Change Detection of Remote Sensing Images
Change detection in remote sensing images is crucial for various applications such as military reconnaissance and urban management. However, traditional change detection methods suffer from low accuracy and complex operations. Meanwhile, existing deep learning approaches struggle to fully understand multi-scale semantic information, and thus still face limitations in accuracy and generalization capability. To overcome these limitations, this paper proposed the MSTAN, which consists of a multi-scale Transformer encoder and a decoder centered on a four-layer ASFF module. The four-layer ASFF module dynamically learns spatially adaptive weights to capture multi-scale semantic information. Comparative experiments demonstrate that MSTAN achieves high-precision change detection. Cross-dataset evaluation experiments demonstrate that MSTAN possesses strong generalization ability. Ablation experiments confirm the effectiveness of the four-layer ASFF module in fusing multi-scale features. Complexity analysis quantifies the computational overhead of the four-layer ASFF module. These results highlight MSTAN’s powerful generalization capability and its promising potential for change detection tasks.