跨尺度MAE：遥感图像中的多尺度利用故事
Cross-Scale MAE: A Tale of Multi-Scale Exploitation in Remote Sensing
AI总结：
本文提出Cross-Scale MAE，通过多尺度增强和对比生成损失确保一致的表示，优于标准MAE及其他遥感MAE方法。
摘要：
遥感图像由于地理覆盖广、硬件限制和多尺度图像不一致而给图像分析带来独特挑战。本文重新审视经典的多尺度表示学习问题，但基于自监督学习框架进行遥感图像理解。我们提出了Cross-Scale MAE，一种基于Masked Auto-Encoder（MAE）的自监督模型。在预训练过程中，Cross-Scale MAE采用尺度增强技术，并通过对比和生成损失强制执行跨尺度一致性约束，以确保适用于广泛下游任务的一致且有意义的表示。此外，我们的实现利用xFormers库加速单GPU上的网络预训练，同时保持学习表示的质量。实验评估显示，Cross-Scale MAE在标准MAE和其他最先进的遥感MAE方法上表现更优。
英文摘要：
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.
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