Cross-supervised learning for cloud detection

doi:10.1080/15481603.2022.2147298

Cross-supervised learning for cloud detection

2023-01-03

3

OA

AI

DOI：10.1080/15481603.2022.2147298

Original

Original request for help

Save

Abstract

En 中文

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.

Keywords:

Cloud detection

cross-supervised learning

remote sensing

deep learning

Cross-supervised learning for cloud detection

Abstract

Journal

Organization