RanPaste: Paste Consistency and Pseudo Label for Semisupervised Remote Sensing Image Semantic Segmentation

• Published in: IEEE transactions on geoscience and remote sensing Vol. 60; pp. 1 - 16
• Main Authors: Wang, Jia-Xin, Chen, Si-Bao, Ding, Chris H. Q., Tang, Jin, Luo, Bin
• Format: Journal Article
• Language: English
• Published: New York IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Consistency; Convolutional neural network; Datasets; Deep learning; Image annotation; Image processing; Image segmentation; Regularization; Remote sensing; remote sensing (RS); Roads; Semantic segmentation; Semantics; semisupervised learning; Sensors; Task analysis; Training
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2021.3102026

With the development of deep learning, remote sensing (RS) image segmentation has been applied with marked success. However, in the process of model training, the large number of labeled images required more expensive annotation. A key challenge is how to make full use of extensive unlabeled images available to improve the segmentation model. In this article, we propose a semisupervised remote sensing image semantic segmentation method defined as RanPaste, which combines labeled images with unlabeled images to improve segmentation performance. First, we obtain pseudo label by randomly pasting part of the ground truth label into the predicted segmentation map. Then, we combine the labeled and unlabeled images to generate rough predictions after strong augmentation. Finally, by using the semisupervised loss, we achieve better performance on remote sensing image segmentation. Our method combines consistency regularization and pseudo label and then utilizes thresholds to gradually improve the model performance. RanPaste enables the model to learn more underlying information in the unlabeled data. Experimental results on six datasets show that RanPaste can learn more latent information from unlabeled data to improve segmentation performance. Besides, our approach achieves better segmentation results on different network structures and datasets.