Seed Feature Maps-based CNN Models for LEO Satellite Remote Sensing
  Services
Zhichao Lu Chuntao Ding Shangguang Wang Ran Cheng Felix Juefei-Xu Vishnu Naresh Boddeti
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.