Semi-Supervised Adversarial Learning-Based Water Body Extraction from Remote Sensing Images

Abstract

Semantic segmentation techniques are widely used in water body extraction tasks in remote sensing images. However, the results of semantic segmentation considerably depend on the scale of the dataset. A semi-supervised adversarial semantic segmentation method for water body extraction is proposed herein to address the problems of fewer water body datasets and the high cost of obtaining accurate labeled data in remote sensing images. As a generator, the convolution operation of the segmentation network has a limited receptive field and lacks the ability to model long-range contextual relationships, whereas a Transformer can model the global information of the images. The method uses a Swin Transformer to model the global contextual information of deep features in the segmentation network, mining the semantic relationships between pixels, and improving the feature extraction ability of the network. Double convolution blocks are used to extract the local features of the image and retain the high-resolution detail information. A Feature Enhancement Module (FEM) is used to suppress background noise interference in the image, further improving the accuracy of water body extraction. The segmentation and discriminator networks are jointly trained to improve the performance of the model in extracting water bodies using a small amount of labeled data. Many experiments are conducted on the GID dataset, and the results indicate that the method improves the accuracy of water body extraction under different scales of labeled data. For example, under the condition of only 1/8 labeled data, the F1-Score and Intersection over Union (IoU) of the method achieves 90.02% and 81.86%, respectively, which is superior to other semantic segmentation networks such as U-Net and MWEN.