Small sample bare soil segmentation based on semi supervised learning

Accurate extraction of bare soil is crucial for land management, soil conservation, and assessment of natural disaster risks. With the continuous development of remote sensing technology, remote sensing imagery has become an important means of acquiring information about the Earth's surface. Its characteristics of multi-source data, and global coverage provide efficient methods for bare soil extraction. Although deep convolutional networks have made significant progress in image semantic segmentation, they typically require a large number of densely labeled images for training. Therefore, we propose a segmentation network called PAN-Net, which combines unsupervised learning with few-shot segmentation to address the problem of insufficient training samples for the encoder to effectively extract bare soil features. The encoder of PAN-Net is trained on a large number of unlabeled images using unsupervised learning, and the trained encoder performs well in extracting bare soil features. Finally, we apply it to downstream few-shot segmentation tasks, improving the capability of bare soil feature extraction in few-shot segmentation. To better evaluate the model performance, we validate it on our self-made dataset of over 1000 samples. Our model achieves an IoU score of 75% and an F1 score of 80% on the self-made test set, surpassing most existing methods.