EI-HCR: An Efficient End-to-End Hybrid Consistency Regularization Algorithm for Semisupervised Remote Sensing Image Segmentation

Abstract

Recently, remote sensing image (RSI) semantic segmentation technology has advanced greatly, with the fully supervised process achieving particularly strong performance. However, the technology depends heavily on dataset labels, leading to high annotation costs. To alleviate this problem, we propose a novel efficient end-to-end hybrid consistency regularization algorithm (EI-HCR) for the semi-supervised semantic segmentation of RSI, wherein only a few labeled images and a large number of unlabeled images are effectively used. First, we devise data perturbation (DP) consistency regularization (CR), which includes a data mix-up method to combine unlabeled and labeled images. Then, we employ teacher and student networks to conduct model perturbation (MP) CR. Both segmentation results are regarded as pseudo-labels for each other. In the end, the semi-supervised loss is composed of DP and MP consistency loss, and supervises network training along with the fully supervised loss. More importantly, we first combine the characteristics of knowledge distillation to make the student network more lightweight, efficiently reducing the model inference time. Experimental results demonstrate the effectiveness of EI-HCR on the ISPRS Vaihingen and Massachusetts Buildings datasets. With only 5% of the labeled images, EI-HCR can achieve the same accuracy as the fully supervised training with 50% of the labeled images, and the number of student model parameters is only 9.64 M, indicating the method’s great advantages over other algorithms.