Affinity Workshop: Women in Machine Learning

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.

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