Change detection (CD) is one of the important applications of remote sensing and plays an important role in disaster assessment, land use detection, and urban sprawl tracking. High-accuracy fully supervised methods are the main methods for CD tasks at present. However, these methods require a large amount of labeled data consisting of bi-temporal images and their change maps. Moreover, creating change maps takes a lot of labor and time. To address this limitation, a simple semi-supervised change detection method based on consistency regularization and strong augmentation is proposed in this paper. First, we construct a Siamese nested UNet with graph attention mechanism (SANet) and pre-train it with a small amount of labeled data. Then, we feed the unlabeled data into the pre-trained SANet and confidence threshold filter to obtain pseudo-labels with high confidence. At the same time, we produce distorted images by performing strong augmentation on unlabeled data. The model is trained to make the CD results of the distorted images consistent with the corresponding pseudo-label. Extensive experiments are conducted on two high-resolution remote sensing datasets. The results demonstrate that our method can effectively improve the performance of change detection under insufficient labels. Our methods can increase the IoU by more than 25% compared to the state-of-the-art methods.

Remote sensing (RS) images change detection (CD) aims to obtain change information of the target area between multi-temporal RS images. With the modernization of cities, building change detection (BCD) plays a pivotal role in land resource planning, smart city construction and natural disaster assessment, and it is a typical application field of change detection task. Recently, deep learning based methods have shown their superiority in RS image change detection. However, the performance of the existing supervised change detection methods relies heavily on a large amount of high quality annotated bi-temporal RS image as training data, which is usually hard to obtain in practice. To address this issue, a semi-supervised BCD method using a pseudo bi-temporal data generator with consistency regularization was proposed. This method only needs a very small amount of single-temporal RS images with building extraction labels as labeled data. Firstly, with the help of the pseudo bi-temporal data generator, the model can generate a large number of pseudo bi-temporal images with CD labels from a small number of single-temporal images and corresponding building extraction labels automatically, which greatly augments the labeled data set for CD model training. Then, we proposed an error-prone data enhancement fine-tuning strategy to improve the learning effect of the proposed model to these synthesized training data. Finally, we enhance the robustness of the model by forcing the model to make consistent predictions on the images before and after perturbations. Extensive experimental results demonstrate that our method can effectively improve the BCD performance of the model even if labeled data are scare, and outperforms the state-of-the-art methods.

Digital Elevation Model (DEM) is the foundational data for geographical analysis and applications, but its low resolution limits its applications. Deep learning-based DEM super-resolution models offer powerful tools for deriving high-resolution DEMs from low-resolution ones, while the learning of terrain features and geographical knowledge by models is crucial for achieving high-precision DEM super-resolution. However, current methods overlook two critical issues: Firstly, the corresponding cells between low and high resolution DEMs exhibit significant spatial correlation, but current methods lack the utilization of this prior information on cross resolution spatial distance. Secondly, existing methods neglect targeted learning for challenging terrains during the optimization process, leading to insufficient reconstruction accuracy in some areas with frequent topographic relief. To address these two issues, this paper proposes a cross resolution distance aware DEM super resolution method with terrain difficulty perception loss, called CredDSR. First, we propose a cross-resolution attention mechanism. It calculates cross-resolution attention based on the distances between cells on low-resolution and high-resolution DEMs, which is then used to guide the generation of high-resolution DEM features. Then, during the learning process, we design a terrain difficulty perception loss to dynamically amplify the learning weights of high-error regions, enabling targeted learning of challenging terrains. Extensive experiments demonstrate that our model excels in DEM super-resolution accuracy while efficiently saving computational resources and parameters, with CredDSR achieving a 7.89% elevation RMSE reduction on the Nevada dataset and 3.46% on the ALOS-Heishui dataset compared to state-of-the-art models.

Remote sensing image building change detection aims to identify building changes that occur in remote sensing images of the same areas acquired at different times. In recent years, the development of deep learning has led to significant advancements in building change detection methods. However, these fully supervised methods require a large number of bi-temporal remote sensing images with pixel-wise change detection labels to train the model, which incurs substantial time and manpower for annotation. To address this issue, this study proposes a novel single-temporal semi-supervised joint learning framework for building change detection, called JointNet4BCD. Firstly, to reduce annotation costs, we design a semi-supervised learning manner to train our model using a small number of building extraction labels instead of a large amount of building change detection labels. Furthermore, to improve the semantic understanding capability of the model, we propose a joint learning approach for building extraction and change detection tasks. Lastly, a decision fusion block is designed to fuse the building extraction results into the building change detection results to further improve the accuracy of building change detection. Experimental results on the two widely used datasets demonstrate that the proposed JointNet4BCD achieves excellent building change detection performance while reducing the need for labels from thousands to dozens. Using only ten labeled images, JointNet4BCD achieved F1-Scores of 83.93% and 83.45% on the LEVIR2000 and WHU datasets, respectively.