A Self Training Mechanism With Scanty and Incompletely Annotated Samples for Learning‐Based Cloud Detection in Whole Sky Images

Abstract

Abstract Cloud detection is one of important tasks in automatic ground‐based cloud observation systems with ground‐based cloud images. Most supervised methods need substantial annotated samples for model training, while the pixel‐level annotation costs a lot. In this letter, a self‐training mechanism is proposed to significantly reduce the requirement of annotated samples. With a number of original images, only a few images need to be annotated (even incompletely), and a local region classifier model can be initialized with the annotated samples. Then the model is retrained iteratively using unlabeled samples with high confidence pseudo labels given by a fusion decision. The finely trained model can classify the local regions into “cloud” or “sky”. The experiments show that the proposed mechanism is effective for several classifiers. The proposed method can outperform unsupervised methods and achieve comparable results with fully supervised learning methods but using much fewer annotated samples.

Keywords

• cloud detection
• whole sky image
• ground‐based cloud image
• semi‐supervised learning
• machine learning