Semi-Supervised Remote Sensing Image Semantic Segmentation Method Based on Deep Learning

In this paper, we study the semi-supervised semantic segmentation problem via limited labeled samples and a large number of unlabeled samples. We propose a self-learning semi-supervised approach for the semantic segmentation of high-resolution remote sensing images. Our approach uses two networks (UNet and DeepLabV3) to predict the labels of the same unlabeled sample, and the pseudo labels samples with high prediction consistency are added to the training samples to improve the accuracy of semantic segmentation under the condition of limited labeled samples. Our method expands training data samples by using unlabeled data samples with pseudo labels. In order to verify the effectiveness of the proposed method, some experiments were conducted on the improved ISPRS Vaihingen 2D Semantic Labeling dataset using the method that we proposed. We focus on the extraction of forest and vegetation information and focus on the impact of a large number of unlabeled samples on the precision of semantic segmentation, we combine water, surface, buildings, cars, and background into one category and named others, and we call the changed dataset the improved ISPRS Vaihingen dataset. The experimental results show that the proposed method can effectively improve the semantic segmentation accuracy of high-scoring remote sensing images with limited samples than common deep semi-supervised learning.