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Multispectral Masked Autoencoder for Remote Sensing Representation Learning
Yibing Wei · Zhicheng Yang · Hang Zhou · Mei Han · Pedro Morgado · Jui-Hsin Lai

Remote sensing data plays an important role in monitoring global-scale challenges. To achieve automated analysis of it, learning useful features from the vast amount of unlabeled data is the key. Based on the unique characteristics of RS data - multispectrum, large resolution, dense object and complex background, we propose a multispectrum masked autoencoder framework to learn RS representation in a self-supervised way and verify its performance by transfer learning to a sense classification task, which achieves the best top-1 accuracy.

Author Information

Yibing Wei (University of Wisconsin-Madison)
Zhicheng Yang (PAII Inc.)
Hang Zhou (PAII, Inc.)
Mei Han (PAII Inc.)
Pedro Morgado (University of Wisconsin - Madison)
Jui-Hsin Lai (PAII Inc.)

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