This article explores the latest Convolutional Neural Networks (CNNs) for cloud detection aboard hyperspectral satellites. The performance of the latest 1D CNN (1D-Justo-LiuNet) and two recent 2D CNNs (nnU-net and 2D-Justo-UNet-Simple) for cloud segmentation and classification is assessed. Evaluation criteria include precision and computational efficiency for in-orbit deployment. Experiments utilize NASA's EO-1 Hyperion data, with varying spectral channel numbers after Principal Component Analysis. Results indicate that 1D-Justo-LiuNet achieves the highest accuracy, outperforming 2D CNNs, while maintaining compactness with larger spectral channel sets, albeit with increased inference times. However, the performance of 1D CNN degrades with significant channel reduction. In this context, the 2D-Justo-UNet-Simple offers the best balance for in-orbit deployment, considering precision, memory, and time costs. While nnU-net is suitable for on-ground processing, deployment of lightweight 1D-Justo-LiuNet is recommended for high-precision applications. Alternatively, lightweight 2D-Justo-UNet-Simple is recommended for balanced costs between timing and precision in orbit.

翻译：本文探究了用于高光谱卫星云探测的最新一代卷积神经网络（CNN）。评估了最新一维CNN（1D-Justo-LiuNet）和两种近期二维CNN（nnU-net和2D-Justo-UNet-Simple）在云分割与分类中的性能。评价指标包括在轨部署的精度与计算效率。实验采用NASA的EO-1 Hyperion数据，经主成分分析后频谱通道数各异。结果表明，1D-Justo-LiuNet在保持较大频谱通道集紧凑性的同时实现了最高精度，优于二维CNN，但推理时间增加。然而，当通道数显著减少时，一维CNN性能下降。在此背景下，综合考虑精度、内存与时间成本，2D-Justo-UNet-Simple为在轨部署提供了最佳平衡。尽管nnU-net适合地面处理，但在高精度应用中推荐部署轻量级1D-Justo-LiuNet。为均衡在轨时序与精度成本，则推荐选择轻量级2D-Justo-UNet-Simple。