Deploying high-performance convolutional neural network (CNN) models on low-earth orbit (LEO) satellites for rapid remote sensing image processing has attracted significant interest from industry and academia. However, the limited resources available on LEO satellites contrast with the demands of resource-intensive CNN models, necessitating the adoption of ground-station server assistance for training and updating these models. Existing approaches often require large floating-point operations (FLOPs) and substantial model parameter transmissions, presenting considerable challenges. To address these issues, this paper introduces a ground-station server-assisted framework. With the proposed framework, each layer of the CNN model contains only one learnable feature map (called the seed feature map) from which other feature maps are generated based on specific rules. The hyperparameters of these rules are randomly generated instead of being trained, thus enabling the generation of multiple feature maps from the seed feature map and significantly reducing FLOPs. Furthermore, since the random hyperparameters can be saved using a few random seeds, the ground station server assistance can be facilitated in updating the CNN model deployed on the LEO satellite. Experimental results on the ISPRS Vaihingen, ISPRS Potsdam, UAVid, and LoveDA datasets for semantic segmentation services demonstrate that the proposed framework outperforms existing state-of-the-art approaches. In particular, the SineFM-based model achieves a higher mIoU than the UNetFormer on the UAVid dataset, with 3.3x fewer parameters and 2.2x fewer FLOPs.

翻译：在低地球轨道（LEO）卫星上部署高性能卷积神经网络（CNN）模型以实现快速遥感图像处理，已引起工业界与学术界的广泛关注。然而，LEO 卫星上有限的资源与资源密集型 CNN 模型的需求形成鲜明对比，因此需要借助地面站服务器辅助来训练和更新这些模型。现有方法通常需要大量浮点运算（FLOPs）和参数传输，带来显著挑战。为解决这些问题，本文提出了一种地面站服务器辅助框架。在该框架下，CNN 模型的每一层仅包含一个可学习特征图（称为种子特征图），其他特征图基于特定规则从其生成。这些规则的超参数通过随机生成而非训练获得，从而能够从种子特征图生成多个特征图，显著降低 FLOPs。此外，由于随机超参数可通过少量随机种子保存，地面站服务器辅助可以便捷地更新部署在 LEO 卫星上的 CNN 模型。在 ISPRS Vaihingen、ISPRS Potsdam、UAVid 和 LoveDA 数据集上的语义分割服务实验结果表明，所提框架优于现有最先进方法。特别地，基于 SineFM 的模型在 UAVid 数据集上相比 UNetFormer 取得了更高的 mIoU，同时参数量减少 3.3 倍，FLOPs 减少 2.2 倍。