Nanosatellites are being widely used in various missions, including remote sensing applications. However, the difficulty lies in mission operation due to downlink speed limitation in nanosatellites. Considering the global cloud fraction of 67%, retrieving clear images through the limited downlink capacity becomes a larger issue. In order to solve this problem, we propose an image prioritization method based on cloud coverage using CNN. The CNN is designed to be lightweight and to be able to prioritize RGB images for nanosatellite application. As previous CNNs are too heavy for onboard processing, new strategies are introduced to lighten the network. The input size is reduced, and patch decomposition is implemented for reduced memory usage. Replication padding is applied on the first block to suppress border ambiguity in the patches. The depth of the network is reduced for small input size adaptation, and the number of kernels is reduced to decrease the total number of parameters. Lastly, a multi-stream architecture is implemented to suppress the network from optimizing on color features. As a result, the number of parameters was reduced down to 0.4%, and the inference time was reduced down to 4.3% of the original network while maintaining approximately 70% precision. We expect that the proposed method will enhance the downlink capability of clear images in nanosatellites by 112%.

中文翻译：

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在纳米卫星微处理器上使用卷积神经网络对RGB图像进行优先排序

纳米卫星被广泛用于各种任务，包括遥感应用。然而，由于纳米卫星的下行链路速度限制，困难在于任务操作。考虑到67％的全球云比例，通过有限的下行链路容量检索清晰图像成为一个更大的问题。为了解决这个问题，我们提出了一种基于卷积神经网络的基于云覆盖的图像优先化方法。CNN的设计轻巧，能够为纳米卫星应用优先处理RGB图像。由于以前的CNN太重，无法进行车载处理，因此引入了新的策略来减轻网络负担。减少了输入大小，并实施了补丁分解以减少内存使用量。将复制填充应用于第一个块，以抑制补丁中的边界模糊性。对于较小的输入大小适应，网络的深度减小了，内核的数量减少了，从而减少了参数的总数。最后，实现了多流体系结构以抑制网络优化色彩特征。结果，参数数量减少到0.4％，推理时间减少到原始网络的4.3％，同时保持大约70％的精度。我们希望所提出的方法将使纳米卫星中清晰图像的下行链路能力提高112％。参数数量减少到0.4％，推理时间减少到原始网络的4.3％，同时保持大约70％的精度。我们希望所提出的方法将使纳米卫星中清晰图像的下行链路能力提高112％。参数数量减少到0.4％，推理时间减少到原始网络的4.3％，同时保持大约70％的精度。我们希望所提出的方法将使纳米卫星中清晰图像的下行链路能力提高112％。