AP-Semi: Improving the Semi-Supervised Semantic Segmentation for VHR Images Through Adaptive Data Augmentation and Prototypical Sample Guidance

As a method that can incorporate unlabeled data into model training, semi-supervised semantic segmentation (SSS) can mitigate the burden of manual annotation in geographic mapping tasks with very-high-resolution (VHR) images. Various SSS approaches have been proposed for VHR images, but most of them either rely on complex models or additional training procedures, increasing the computation cost. In this study, we propose a simple-yet-effective SSS approach named AP-semi for VHR images, which neither increases the number of learnable parameters nor introduces additional training steps. First, since data augmentation can influence the training data and process significantly, we refine the traditional CutMix transformation, an important data augmentation strategy, by generating adaptively sized cut boxes, optimizing the intensity of perturbation and making it better suited for the training phase. Second, in order to make all the unlabeled samples contribute to the model training, we design a prototype-based feature-level consistency loss, which can enforce the consistency of model predictions in the feature space. By combining the adaptive data augmentation strategy and prototype-based loss, AP-semi can achieve impressive results with a limited number of labeled samples, surpassing several baseline models in extensive experiments. Through experiments, we find that AP-semi can adapt to both convolutional neural networks (CNNs)- and transformer-based segmentation models. In the simulation experiment mimicking the routine operations in geographic mapping tasks, AP-semi demonstrates significant time-saving benefits.