Cross-supervised learning for cloud detection

ABSTRACT

We present a new learning paradigm, that is, cross-supervised learning, and explore its use for cloud detection. The cross-supervised learning paradigm is characterized by both supervised training and mutually supervised training, and is performed by two base networks. In addition to the individual supervised training for labeled data, the two base networks perform the mutually supervised training using prediction results provided by each other for unlabeled data. Specifically, we develop In-extensive Nets for implementing the base networks. The In-extensive Nets consist of two Intensive Nets and are trained using the cross-supervised learning paradigm. The Intensive Net leverages information from the labeled cloudy images using a focal attention guidance module (FAGM) and a regression block. The cross-supervised learning paradigm empowers the In-extensive Nets to learn from both labeled and unlabeled cloudy images, substantially reducing the number of labeled cloudy images (that tend to cost expensive manual effort) required for training. Experimental results verify that In-extensive Nets perform well and have an obvious advantage in the situations where there are only a few labeled cloudy images available for training. The implementation code for the proposed paradigm is available at https://gitee.com/kang_wu/in-extensive-nets.

KEYWORDS:

• Cloud detection
• cross-supervised learning
• remote sensing
• deep learning

Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Project No. 61971444, in part by the National Key R&D Program of China under Project No. 2019YFC1408400, and in part by the Innovative Research Team Program for Young Scholars at Universities in Shandong Province under Project No. 2020KJN010.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

The GF1-WFV data that support the findings of this study are openly available at https://doi.org/10.1016/j.rse.2017.01.026, reference number li2017multi. For the HY1C CZI data, raw data were collected from https://osdds.nsoas.org.cn/. The training and testing data with cloud masks of this study are available along with our code at https://gitee.com/kang_wu/in-extensive-nets.

Additional information

Funding

This work was supported in part by the National Natural Science Foundation of China under Project No. 61971444, in part by the National Key R&D Program of China under Project No. 2019YFC1408400, and in part by the Innovative Research Team Program for Young Scholars at Universities in Shandong Province under Project No. 2020KJN010; The Innovative Research Team Program for Young Scholars at Universities in Shandong Province; National Key Research and Development Program of China National Key Research and Development Program of China [2019YFC1408400];