Scale-MAE: A Scale-Aware Masked Autoencoder for Multiscale Geospatial Representation Learning
It addresses scale-dependent challenges in remote sensing, offering a novel pretraining approach for improved performance in geospatial tasks.
The paper tackles the problem of learning multiscale representations for remote sensing imagery by introducing Scale-MAE, a pretraining method that explicitly models relationships between data at different scales, achieving a 2.4-5.6% kNN classification improvement across eight datasets and a 0.9-1.7 mIoU gain on building segmentation.
Large, pretrained models are commonly finetuned with imagery that is heavily augmented to mimic different conditions and scales, with the resulting models used for various tasks with imagery from a range of spatial scales. Such models overlook scale-specific information in the data for scale-dependent domains, such as remote sensing. In this paper, we present Scale-MAE, a pretraining method that explicitly learns relationships between data at different, known scales throughout the pretraining process. Scale-MAE pretrains a network by masking an input image at a known input scale, where the area of the Earth covered by the image determines the scale of the ViT positional encoding, not the image resolution. Scale-MAE encodes the masked image with a standard ViT backbone, and then decodes the masked image through a bandpass filter to reconstruct low/high frequency images at lower/higher scales. We find that tasking the network with reconstructing both low/high frequency images leads to robust multiscale representations for remote sensing imagery. Scale-MAE achieves an average of a $2.4 - 5.6\%$ non-parametric kNN classification improvement across eight remote sensing datasets compared to current state-of-the-art and obtains a $0.9$ mIoU to $1.7$ mIoU improvement on the SpaceNet building segmentation transfer task for a range of evaluation scales.