Semantic segmentation of remote sensing images is vital in remote sensing technology. High-quality models for this task require a vast amount of images, manual annotation is a process that is time-consuming and labor-intensive. Consequently, this has catalyzed the emergence of semi-supervised semantic segmentation methods. However, the complexity of foreground categories in remote sensing images poses a challenge to maintaining prediction consistency. Moreover, inherent characteristics such as intra-class variations and inter-class similarities result in a certain degree of confusion among features of different classes in the feature space. This impacts the final classification results. In order to improve the model’s consistency and optimize the classification of categories based on features, this paper proposes a new semi-supervised semantic segmentation framework that combines consistency regularization and contrastive learning. In terms of consistency regularization, the proposed method incorporates dual teacher networks, introduces ClassMix for image augmentation, and utilizes confidence levels to integrate the predictions from these networks. By introducing perturbations at both the network and image levels, while simultaneously maintaining consistency, the predictive prowess and generalization ability of the model are enhanced. For contrastive learning, Postive-Unlabeled Learning (PU-Learning) is employed to improve the problem of mis-sampling when selecting features. At the same time, higher biased weights are allocated to more challenging negative samples, thereby elevating the complexity of feature learning and enhancing the discriminative capability of the final feature representation space. Our extensive experiments on the ISPRS Vaihingen dataset and the challenging iSAID dataset have served to underscore the superior performance of our approach.

In the field of remote sensing technology, the semantic segmentation of remote sensing images carries substantial importance. The creation of high-quality models for this task calls for an extensive collection of image data. However, the manual annotation of these images can be both time-consuming and labor-intensive. This has catalyzed the advent of semi-supervised semantic segmentation methodologies. Yet, the complexities inherent within the foreground categories of these remote sensing images present challenges in preserving prediction consistency. Moreover, remote sensing images possess more complex features, and different categories are confused within the feature space, making optimization based on the feature space challenging. To enhance model consistency and to optimize feature-based class categorization, this paper introduces a novel semi-supervised semantic segmentation framework based on Mean Teacher (MT). Unlike the conventional Mean Teacher that only introduces perturbations at the image level, we incorporate perturbations at the feature level. Simultaneously, to maintain consistency after feature perturbation, we employ contrastive learning for feature-level learning. In response to the complex feature space of remote sensing images, we utilize entropy threshold to assist contrastive learning, selecting feature key-values more precisely, thereby enhancing the accuracy of segmentation. Extensive experimental results on the ISPRS Potsdam dataset and the challenging iSAID dataset substantiate the superior performance of our proposed methodology.

Semantic segmentation of aerial images is crucial yet resource-intensive. Inspired by human ability to learn rapidly, few-shot semantic segmentation offers a promising solution by utilizing limited labeled data for efficient model training and generalization. However, the intrinsic complexities of aerial images, compounded by scarce samples, often result in inadequate feature representation and semantic ambiguity, detracting from the model's performance. In this paper, we propose to tackle these challenging problems via dual semantic metric learning and multi-semantic features fusion and introduce a novel few-shot segmentation Network (DSMF-Net). On the one hand, we consider the inherent semantic gap between the feature of graph and grid structures and metric learning of few-shot segmentation. To exploit multi-scale global semantic context, we construct scale-aware graph prototypes from different stages of the feature layers based on graph convolutional networks (GCNs), while also incorporating prior-guided metric learning to further enhance context at the high-level convolution features. On the other hand, we design a pyramid-based fusion and condensation mechanism to adaptively merge and couple the multi-semantic information from support and query images. The indication and fusion of different semantic features can effectively emphasize the representation and coupling abilities of the network. We have conducted extensive experiments over the challenging iSAID-5 $^{i}$ and DLRSD benchmarks. The experiments have demonstrated our network's effectiveness and efficiency, yielding on-par performance with the state-of-the-art methods.