Cross-Scale MAE: A Tale of Multi-Scale Exploitation in Remote Sensing

Maofeng Tang; Andrei Cozma; Konstantinos Georgiou; Hairong Qi

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arXiv · 2401.15855

Cross-Scale MAE: A Tale of Multi-Scale Exploitation in Remote Sensing

Maofeng Tang·Andrei Cozma·Konstantinos Georgiou·Hairong Qi

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Abstract

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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Maofeng Tang, Andrei Cozma, Konstantinos Georgiou, Hairong Qi. 2024-01-29. Cross-Scale MAE: A Tale of Multi-Scale Exploitation in Remote Sensing. https://arxiv.org/abs/2401.15855

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