对比14种模型，找出最适合卫星实时图像分类的高效低耗方案。

Remote sensing (RS) image classification is central to Earth observation, but onboard deployment requires models that are accurate, efficient, and robust to sensor and transmission degradation. Following a train-on-ground, infer-onboard workflow, we evaluate 14 backbones, including CNNs, ResNets, compact Transformers trained from scratch, and pre-trained Vision Transformers, on EuroSAT and PatternNet. We assess clean-data performance, computational cost, power consumption, and robustness to Gaussian noise, motion blur, and an end-to-end DVB-S2(X) transmission chain with channel impairments and JPEG compression. Pre-trained Vision Transformers generally outperform models trained from scratch while providing better efficiency and corruption resilience. MobileViTV2 achieves the highest clean EuroSAT accuracy at 99.09%, whereas EfficientViT-M2 provides the strongest overall trade-off. It attains 98.76% accuracy, precision, and recall on EuroSAT and 99.52% accuracy on PatternNet, with 203.53 MFLOPs, a 38.19 MB footprint, and the best overall robustness score of 0.79. It also degrades most gracefully under transmission loss and consumes 63.35% less power than MobileViTV2 and 73.33% less than Swin Transformer. These results identify EfficientViT-M2 as a strong backbone for reliable, energy-efficient onboard RS image classification. Code for data augmentation, corruption generation, training, and inference is publicly available.