Semi-Supervised Semantic Generative Networks For Remote Sensing Image Segmentation

Semi-supervised remote sensing semantic segmentation is an efficient way to increase the use of unlabeled data and cut labelling costs. The unlabeled-to-labeled data ratio is employed in more recent methods, which is very different from what is really used in practise. In this paper, we propose a semi-supervised semantic generative network for remote sensing images, introducing a self-supervised learning method to enhance the feature representation of the model when the data ratio is high. Specifically, we design a new branch for unlabeled data, which includes modules for both semantic reconstruction and appearance reconstruction. It can effectively alleviate the category confusion in complicated remote sensing image when there are few labeled data. Comprehensive experiments on the ISPRS POTSDAM dataset demonstrate that the proposed method achieves promising results.