Semi-supervised Deep Learning via Transformation Consistency Regularization for Remote Sensing Image Semantic Segmentation

Abstract

Deep convolutional neural networks (CNNs) have gotten a lot of press in the last several years, especially in domains like computer vision (CV) and remote sensing (RS). However, achieving superior performance with deep networks highly depends on a massive amount 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 is readily available. In this study, we offer a new semi-supervised deep semantic labeling framework for 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 (TCR) 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 semi-supervised semantic labeling technique by using a hybrid transformation consistency regularization (HTCR). 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 semi-supervised algorithms in terms of performance. Our experiments further demonstrated that our semi-supervised semantic labeling strategy has the potential to partially tackle the problem of limited labeled samples for high-resolution RS image land-cover segmentation.