The rapid advancement of automated artificial intelligence algorithms and remote sensing instruments has benefited change detection (CD) tasks. However, there is still a lot of space to study for precise detection, especially the edge integrity and internal holes phenomenon of change features. In order to solve these problems, we design the Change Guiding Network (CGNet), to tackle the insufficient expression problem of change features in the conventional U-Net structure adopted in previous methods, which causes inaccurate edge detection and internal holes. Change maps from deep features with rich semantic information are generated and used as prior information to guide multi-scale feature fusion, which can improve the expression ability of change features. Meanwhile, we propose a self-attention module named Change Guide Module (CGM), which can effectively capture the long-distance dependency among pixels and effectively overcomes the problem of the insufficient receptive field of traditional convolutional neural networks. On four major CD datasets, we verify the usefulness and efficiency of the CGNet, and a large number of experiments and ablation studies demonstrate the effectiveness of CGNet. We're going to open-source our code at https://github.com/ChengxiHAN/CGNet-CD .

A high-precision feature extraction model is crucial for change detection. In the past, many deep learning-based supervised change detection methods learned to recognize change feature patterns from a large number of labelled bi-temporal images, whereas labelling bi-temporal remote sensing images is very expensive and often time-consuming. Therefore, we propose a coarse-to-fine semi-supervised change detection method based on consistency regularization (C2F-SemiCD), which includes a coarse-to-fine change detection network with a multi-scale attention mechanism(C2FNet) and a semi-supervised update method. Among them, the C2FNet network "gradually" completes the extraction of change features from coarse-grained to fine-grained through multi-scale feature fusion, channel attention mechanism, spatial attention mechanism, global context module, feature refine module, initial aggregation module, and final aggregation module. The semi-supervised update method uses the mean teacher method. The parameters of the student model are updated to the parameters of the teacher Model by using the exponential moving average (EMA) method. Through extensive experiments on three datasets and meticulous ablation studies, including crossover experiments across datasets, we verify the significant effectiveness and efficiency of the proposed C2F-SemiCD method. The code will be open at: https://github.com/ChengxiHAN/C2F-SemiCD-and-C2FNet.

Building extraction aims to segment building pixels from remote sensing images and plays an essential role in many applications, such as city planning and urban dynamic monitoring. Over the past few years, deep learning methods with encoder–decoder architectures have achieved remarkable performance due to their powerful feature representation capability. Nevertheless, due to the varying scales and styles of buildings, conventional deep learning models always suffer from uncertain predictions and cannot accurately distinguish the complete footprints of the building from the complex distribution of ground objects, leading to a large degree of omission and commission. In this paper, we realize the importance of uncertain prediction and propose a novel and straightforward Uncertainty-Aware Network (UANet) to alleviate this problem. Specifically, we first apply a general encoder–decoder network to obtain a building extraction map with relatively high uncertainty. Second, in order to aggregate the useful information in the highest-level features, we design a Prior Information Guide Module to guide the highest-level features in learning the prior information from the conventional extraction map. Third, based on the uncertain extraction map, we introduce an Uncertainty Rank Algorithm to measure the uncertainty level of each pixel belonging to the foreground and the background. We further combine this algorithm with the proposed Uncertainty-Aware Fusion Module to facilitate level-by-level feature refinement and obtain the final refined extraction map with low uncertainty. To verify the performance of our proposed UANet, we conduct extensive experiments on three public building datasets, including the WHU building dataset, the Massachusetts building dataset, and the Inria aerial image dataset. Results demonstrate that the proposed UANet outperforms other state-of-the-art algorithms by a large margin. The source code of the proposed UANet is available at https://github.com/Henryjiepanli/Uncertainty-aware-Network.

The extraction of large-scale farmland is essential for optimizing agricultural production and advancing sustainable development. To meet the urgent need for efficient farmland extraction and overcome existing technical challenges, we have developed a comprehensive farmland mapping framework that integrates advanced data, methodology, and cartographic techniques. Regarding data, we present the fine-grained farmland dataset (FGFD), which compiles high-quality, meticulously annotated very high-resolution (VHR) satellite images and captures distinct regional characteristics across eastern, southern, western, northern, and central China. Building on the FGFD, we propose the dual-branch boundary-aware network (DBBANet), which employs ResNet-50 as the encoder to extract multilayer encoded features and introduces two parallel decoding branches: a spatial-aware branch and a boundary-aware branch. The dual-branch architecture leverages both unique semantic information relevant to farmland and detailed boundary information, facilitating a more comprehensive and accurate representation of farmland areas. By combining this dataset with our innovative methodology, we further propose a farmland mapping framework designed for large-scale applications. The proposed framework enables the direct generation of high-precision vector maps from VHR images, providing crucial technical support for farmland management, resource assessment, and agricultural planning. Extensive experiments conducted on the FGFD have established benchmarks for 13 segmentation methods, demonstrating the state-of-the-art (SOTA) performance of our approach. In practical large-scale applications, our mapping framework produces high-precision vector maps with clear boundaries, bridging the gap in fine-grained farmland mapping and paving the way for further research and applications in this field. The source code of the proposed DBBANet and FGFD is available at: https://github.com/Henryjiepanli/DBBANet .