Remote sensing images present unique challenges to image analysis due to the
extensive geographic coverage, hardware limitations, and misaligned multi-scale
images. This paper revisits the classical multi-scale representation learning
problem but under the general framework of self-supervised learning for remote
sensing image understanding. We present Cross-Scale MAE, a self-supervised
model built upon the Masked Auto-Encoder (MAE).During pre-training, Cross-Scale
MAE employs scale augmentation techniques and enforces cross-scale consistency
constraints through both contrastive and generative losses to ensure consistent
and meaningful representations well-suited for a wide range of downstream
tasks. Further, our implementation leverages the xFormers library to accelerate
network pre-training on a single GPU while maintaining the quality of learned
representations. Experimental evaluations demonstrate that Cross-Scale MAE
exhibits superior performance compared to standard MAE and other
state-of-the-art remote sensing MAE methods.

远程感知图像理解中，该研究通过自监督学习的通用框架重新探讨了经典的多尺度表示学习问题，提出了一种基于掩码自编码器（MAE）的 Cross-Scale MAE 自监督模型，通过尺度增强技术以及对比损失和生成损失的交叉尺度一致性约束来确保一致且有意义的表示，进一步利用 xFormers 库在单个 GPU 上加速网络预训练并保持所学表示的质量，实验证明 Cross-Scale MAE 相较于标准 MAE 和其他最先进的远程感知 MAE 方法具有优越的性能。