Currently, many countries have built geo-information databases and gathered large amounts of geographic data. However, with the extensive construction of infrastructure and rapid expansion of cities, road updating process is imperative to maintain the high quality of current basic geographic information. Currently, road extraction and change detection are two commonly used methods to solve road updating problems. Most of the existing methods rely on a large number of accurate road labels to generate road information, while ignoring the use of quantities of available but incomplete road maps. In our work, we proposed a semi-supervised road extraction method specifically for road-updating applications (SRUNet). In this approach, historical road maps are fused with the latest remote sensing images, and state of the roads are updated directly. A multi-branch network is the core of the method, which consists of three noteworthy parts: Map Encoding Branch (MEB) proposed for representation learning, Boundary Enhancement Module (BEM) for improving the accuracy of boundary prediction, and Residual Refinement Module (RRM) for further optimizing the prediction results. We applied our method to two datasets: the DeepGlobe public dataset and our self-constructed dataset from Zhengzhou and Nanjing. Experimental results shows that our method achievs an improvement of 14.37% over the baseline approach. Notably, the addition of historical maps improved the model’s performance by 12.4%. Promising results were obtained on two cities’ large-scale road networks. With the reliable prediction results and improved performance, we believe SRUNet is meaningful for a wide range of road renewal applications.

In the existing unsupervised domain adaptation (UDA) methods for remote sensing images (RSIs) semantic segmentation, class symmetry is a widely followed ideal assumption, where the source and target RSIs have exactly the same class space. In practice, however, it is often very difficult to find a source RSI with exactly the same classes as the target RSI. More commonly, there are multiple source RSIs available. And there is always an intersection or inclusion relationship between the class spaces of each source–target pair, which can be referred to as class asymmetry. Nevertheless, the class asymmetry domain adaptation segmentation of RSIs with multiple sources has not yet been explored. To this end, a novel class asymmetry RSIs domain adaptation method is proposed for the first time in this article, which consists of four key components. First, a multibranch segmentation network is built to learn an expert for each source RSI. Second, a novel collaborative learning method with the cross-domain mixing strategy is proposed, to supplement the class information for each source while achieving the domain adaptation of each source–target pair. Third, a pseudolabel generation strategy is proposed to effectively combine the strengths of different experts, which can be flexibly applied to two cases where the source class union is equal to or includes the target class set. Fourth, a multiview-enhanced knowledge integration module is developed for high-level knowledge routing and transfer from multiple domains to target predictions. The experimental results of six different class settings on airborne and spaceborne RSIs show that the proposed method can effectively perform the multisource domain adaptation in the case of class asymmetry, and the obtained segmentation performance of target RSIs is significantly better than the existing relevant methods.

Extracting buildings from very high-resolution satellite images is a challenging yet important task for applications such as urban monitoring. Multiscale feature learning proves to be a potential solution toward accurate extraction of buildings. This study exploits a powerful multiscale feature learning module, a hierarchical vision transformer by shifted windows (swin), as a backbone within a building extraction network. To this end, we first designed a general structure for building extraction, consisting of a backbone to extract multiscale features and a head network to fuse and refine features. Then, we integrated swin into the structure as a backbone and utilized channel-wise and spatial-wise enhancement in a head network. Experimental results show that our method achieves improvements regarding both F1-score and intersection over union (IoU) compared to the multiple attending path neural network (MAP-Net), which is the current state-of-the-art (SOTA) algorithm for building extraction from remote sensing images. Our study thus confirms the potential of swin transformers as backbones for semantic segmentation tasks based on satellite images.

The building extraction from remote sensed images ash been a challenging yet vital task for applicable purposes such as urban monitoring and cartography. Most of the existing learning based approaches focus on the supervised building extraction methods, of which the models should be trained with images and the corresponding labels. This research exploits a self-supervised approach for building extraction, which could train the backbone within a building extraction network without annotations. Specifically, the backbone is initially trained with a pixel-level self-supervised module instead of commonly used supervised approaches or instance-level self-supervised modules. Next, the pretrained backbone is embedded into a task-specific network followed by tuning with limited annotations. The experiments were conducted on three popular datasets and the results show that our method achieves improvements regarding both intersection over union (IoU) and F1-score compared to supervised approach and instance-level self-supervised methods. Our study thus confirms the potential of pixel-level self-supervised approach for semantic segmentation for remote sensing images.

Recently, the field of hyperspectral image (HSI) classification is dominated by deep learning-based methods. However, training deep learning models usually needs a large number of labeled samples to optimize thousands of parameters. In this article, a deep multiview learning method is proposed to deal with the small sample problem of HSI. First, two views of an HSI scene are constructed by applying principal component analysis to different bands. Second, a deep residual network is designed to embed the different views of a sample to a latent space. The designed deep residual network is trained by maximizing agreement between differently augmented views of the same data sample via a contrastive loss in the latent space. Note that the training procedure of the designed deep residual network does not use labeled information. Therefore, the proposed method belongs to the category of unsupervised learning, which could alleviate the lack of labeled training samples. Finally, a conventional machine learning method (e.g., support vector machine) is used to complete the classification task in the learned latent space. To demonstrate the effectiveness of the proposed method, extensive experiments are carried on four widely used hyperspectral data sets. The experimental results demonstrate that the proposed method could improve the classification accuracy with small samples.