Chun-Han Chen
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Research Interests
Chun-Han Chen has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
- Chun-Fu Chen
Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
1 shared publication - Pei-Jun Lee
Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
1 shared publication - Shimaa Bergies
Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan
1 shared publication
Latest Publications
- Open Access
- 4 February 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Vol. 19 (19391404) , 6389-6402
Efficient Cloud Removal for Remote Sensing Data Transmission via Model Compression and Sparse Accelerator Design
Cloud coverage significantly degrades the quality and usability of remote sensing imagery, while also leading to unnecessary bandwidth consumption and power expenditure in satellite payloads. To address this challenge, a high-efficiency hardware–software co-design framework for cloud removal in remote sensing applications is presented. At the algorithmic level, a two-stage model compression strategy is designed, combining structured channel pruning and adaptive unstructured pruning, which reduces model parameters by more than 98% while preserving segmentation accuracy across multisource datasets. At the hardware level, a low-voltage sparse matrix accelerator enhanced with a super balanced path mechanism is designed, enabling stable operation at 0.65 V and achieving 43.2% higher energy efficiency and 11.5% better area efficiency compared to prior designs. Extensive experiments on GF-1 WFV, Sentinel-2 CloudSEN12, and Lilium-1 imagery validate the generalization capability and robustness of the proposed approach. The joint optimization of algorithm and hardware not only delivers state-of-the-art efficiency but also provides a practical pathway for reducing noninformative data transmission and enhancing the sustainability of future satellite missions.
- 4 February 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Vol. 19 (19391404) , 6389-6402
Open AccessEfficient Cloud Removal for Remote Sensing Data Transmission via Model Compression and Sparse Accelerator Design
Cloud coverage significantly degrades the quality and usability of remote sensing imagery, while also leading to unnecessary bandwidth consumption and power expenditure in satellite payloads. To address this challenge, a high-efficiency hardware–software co-design framework for cloud removal in remote sensing applications is presented. At the algorithmic level, a two-stage model compression strategy is designed, combining structured channel pruning and adaptive unstructured pruning, which reduces model parameters by more than 98% while preserving segmentation accuracy across multisource datasets. At the hardware level, a low-voltage sparse matrix accelerator enhanced with a super balanced path mechanism is designed, enabling stable operation at 0.65 V and achieving 43.2% higher energy efficiency and 11.5% better area efficiency compared to prior designs. Extensive experiments on GF-1 WFV, Sentinel-2 CloudSEN12, and Lilium-1 imagery validate the generalization capability and robustness of the proposed approach. The joint optimization of algorithm and hardware not only delivers state-of-the-art efficiency but also provides a practical pathway for reducing noninformative data transmission and enhancing the sustainability of future satellite missions.