Chun-Fu Chen

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.

This paper proposes an optimized cloud segmentation accelerator to enable low data rate transmission in satellite payload applications. The system employs an advanced cloud detection and segmentation framework to distinguish cloud-covered and cloud-free regions in high resolution satellite imagery accurately. A quantized U-Net architecture has been developed with a focus on efficient resource utilization, tailored specifically for FPGA implementation. This design is built upon the H.265 video encoder, significantly reducing bandwidth usage while ensuring high-quality image output. Experimental results highlight impressive segmentation accuracy, achieving a mean Intersection over Union (mIoU) of 85.64%. The architecture is also notable for its low resource consumption, with only 0.27M parameters and 5.2 GFLOPs, along with minimal power requirements of just 0.16W. These attributes make this solution an ideal choice for edge deployment on satellite platforms.

Applying cloud detection algorithms to edge devices using semantic segmentation poses significant challenges due to the high computational complexity and power consumption. This paper proposes a parameter reduction method that combines linear channel pruning and adaptive pruning techniques to effectively compress the large UNet model. Linear channel pruning efficiently reduces model redundancy, while adaptive pruning further enhances the model's sparsity. This approach not only maintains performance comparable to the original UNet in terms of Intersection over Union (IoU) and other key metrics, but also significantly reduces the model's parameter count, thereby lowering DRAM access power consumption. Experimental results show that the first and second stages of parameter reduction decreased the model's parameter count by 98.5% and 70.3%, respectively, while reducing memory access by 67.5% and 65%, respectively.