Efficient Cloud Removal for Remote Sensing Data Transmission via Model Compression and Sparse Accelerator Design

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Abstract
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.
Funding Information
  • National Science and Technology Council (NSTC 113-2221-E-011-102-MY2)
  • National Science and Technology Council (NSTC 113-2218-E-006-020)
  • National Science and Technology Council (NSTC 113-2640-E-008-003)

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