Semi-supervised Deep Learning via Transformation Consistency Regularization for Remote Sensing Image Semantic Segmentation
Bin Zhang, Yongjun Zhang, Yansheng Li, Yongxiang Yao, Haoyu Guo, Zhi Zheng, Kun Yang
Abstract
Deep convolutional neural networks have gotten a lot of press in the last several years, especially in domains like computer vision and remote sensing (RS). However, achieving superior performance with deep networks highly depends on a massive number of accurately labeled training samples. In real-world applications, gathering a large number of labeled samples is time consuming and labor intensive, especially for pixel-level data annotation. This dearth of labels in land-cover classification is especially pressing in the RS domain because high-precision high-quality labeled samples are extremely difficult to acquire, but unlabeled data are readily available. In this study, we offer a new semisupervised deep semantic labeling framework for the semantic segmentation of high-resolution RS images to take advantage of the limited amount of labeled examples and numerous unlabeled samples. Our model uses transformation consistency regularization to encourage consistent network predictions under different random transformations or perturbations. We try three different transforms to compute the consistency loss and analyze their performance. Then, we present a deep semisupervised semantic labeling technique by using a hybrid transformation consistency regularization. A weighted sum of losses, which contains a supervised term computed on labeled samples and an unsupervised regularization term computed on unlabeled data, may be used to update the network parameters in our technique. Our comprehensive experiments on two RS datasets confirmed that the suggested approach utilized latent information from unlabeled samples to obtain more precise predictions and outperformed existing semisupervised algorithms in terms of performance. Our experiments further demonstrated that our semisupervised semantic labeling strategy has the potential to partially tackle the problem of limited labeled samples for high-resolution RS image land-cover segmentation.