Fully supervised change detection (CD) methods in remote sensing (RS) perform well but depend on costly and time-consuming pixel-level annotations, which are impractical to obtain at scale. Therefore, it is essential to develop annotation-efficient alternatives that can narrow the performance gap with fully supervised methods. To this end, we propose a novel weakly supervised CD framework, named RCNet, which employs dual networks to implement reliable co-training using image-level annotations. Our framework is grounded in multi-view learning of co-training and the localization ability of class activation mapping (CAM). In our approach, two sub-nets with the same architecture perform image-level change classification and pixel-level segmentation from different views. Although CAM roughly localizes changes, ambiguity and noise in its pseudo labels may cause confirmation bias, limiting performance. Our approach mitigates this bias by introducing a feature discrepancy loss to enable cross-supervision between two sub-nets. Meanwhile, CAM tends to highlight a single object, but RS images commonly contain many dense and small changed objects with complexity, resulting in decreased reliability of pseudo labels. Therefore, we present an IoU-based reliable pseudo label screening (RPLS) strategy, which minimizes the likelihood of changed areas being misidentified as unchanged, enhancing the reliability of changed information obtained. Besides, to further improve boundary fineness and internal integrity of changed areas, we incorporate an additional strong perturbation branch for each sub-net and develop a consistency regularization loss. Extensive experiments on three challenging RS image CD datasets demonstrate that our RCNet achieves competitive performance with image-level labels. The source code is available athttps://github.com/Youzhihui/RCNet.

Deep learning models, particularly convolutional neural networks (CNNs), have demonstrated outstanding feature learning capabilities, leading to remarkable performance in remote sensing change detection (RSCD) tasks. However, their most critical drawback lies in the lack of effective modeling of global information. This deficiency affects the model’s understanding of the overall context and structure of the entire image, making it difficult to distinguish between background and target areas, thereby leading to the erroneous identification of change regions. Second, features extracted by traditional backbone networks contain a significant amount of noise, resulting in blurred boundaries of changed objects. The challenge of effectively fusing detailed and semantic information to accurately differentiate pseudo changes remains significant. Furthermore, how to fully exploit multiscale information is another issue worth considering. We propose a full-scale multidimensional interaction network called SDSN, which enhances feature representation by leveraging both detail and semantic branches. Initially, bi-temporal images are processed by the encoder to extract coarse multiscale features. The semantic branch guides shallow-scale features, while the detail branch focuses on deep-scale features. Multikernel receptive module (MRM) aggregates global information. The detail branch utilizes a diversity variance module (DVM) and differential operations to generate refined change maps with noise reduction and background suppression. A multidimensional cross-perception module (MCM) guides the fusion of these change maps, establishing multidimensional dependencies to enrich feature representation. Compared with previous methods, SDSN demonstrates greater performance under complex environmental conditions, particularly noteworthy for its fewer parameters (4.03 M) and lower computational costs (7.94 G). The code is publicly available athttps://github.com/dpt000121/dpt.

In the field of remote sensing (RS) image change detection (CD), the conventional encoder-decoder architecture networks often encounter three significant challenges. First, noise in the features extracted from traditional backbone networks leads to blurred boundaries of change objects. Second, upsampling techniques employed in the decoder, such as interpolation or deconvolution, are limited by their finite receptive fields, making it challenging to accurately distinguish pseudo-changes. Furthermore, how to merge encoder and decoder features with possible semantic gaps for the fine-grained details is a topic worth considering. To address these challenges, we introduce a prior guidance (PG) module that effectively aggregates prior high-level features as a semantic guidance map to guide encoder features for the enhancement of boundary detection. In addition, we design a principal attention (PA) module, which aggregates global information from principal regions through sparse operations and adaptively allocates this information to the upsampled and encoder features. This not only addresses the deficiency of global information in the upsampled features but also reduces the semantic gap between the encoder and decoder by establishing channel dependencies. PA does not divert attention to irrelevant regions, demonstrating excellent performance and computational efficiency. By integrating these two modules into our method, a novel PG and PA network (PGPANet) is elaborately designed. A wide range of experiments confirms the validity of our method, showcasing outstanding detection accuracy on three publicly available CD datasets: LEVIR-CD, SYSU-CD, and WHU-CD. The demo code of this work is publicly available athttps://github.com/DaGuangDaGuang/PGPANet.

Remote sensing (RS) image change detection (CD) has been a research hotspot in recent years, which plays an important role in urban planning and disaster assessment. However, since CD labels are difficult to obtain, how to utilize semantic information in RS images to improve the change prediction performance is a problem worth exploring. To solve this problem, we propose a transfer learning-based CD method that utilizes a diffusion generation model to translate high-level semantic information into low-level change information. First, we propose a pseudo-change image pair generation method that utilizes semantic labels to guide the diffusion model to generate change images. Then, the refined loss (RL) is designed to improve the model’s ability to recognize change features based on the difference between pseudo-change image pairs and unlabeled image pairs. Experimental results on WHU-CD, LEVIR-CD, and GoogleGZ-CD datasets show that the proposed method effectively transfers the semantic information into change information and finally improves the model’s feature recognition ability for change objects. Compared with recent CD and transfer learning methods, the proposed transfer learning model (TLM) achieves the best performance. The source code is available athttps://github.com/VCISwang/STCD.

In the study of remote sensing images, the problem of change detection (CD) is crucial. Convolutional neural networks (CNNs) are well-liked feature extraction structures that are frequently used in CD. On the other hand, graph convolutional networks (GCNs) are effective in building contextual structure information. Compared with CNN, GCN can make full use of the graph structure information to capture the changing features between different areas in the graph by learning the connections and interactions between nodes. In contrast, traditional pixel-based CNNs may have difficulty modeling semantic relationships and temporal variations among features and are susceptible to noise interference. So in this article, we extract optimization information using a GCN structure. Due to the particularity of remote sensing images, edge information is often ignored, which is useful in the field of CD. In this article, we propose an attention-aware Sobel GCN (ASGCN) for remote sensing image CD. First, we use a Siamese CNN to extract primary multilevel features. Then, a dual-branch attention module (DAM) including coordinate attention and multiscale local attention module (MLAM) is proposed to focus on informative pixels, we use Sobel operator to construct graph, and the graph convolutional module can expand receptive field and extract edge information. Attention fusion module (AFM) is adopted at decoder to perform effective feature fusion. Extensive comparative experiments on three CD datasets, LEVIR-CD, WHU-CD, and DSIFN-CD, verify the effectiveness of the proposed ASGCN.

With the continuous progress of deep learning in remote sensing (RS) visual tasks, considerable advancements have been achieved in RS image change detection (CD). However, prevailing CD methods heavily rely on extensive sets of fully pixelwise hand-annotated training data, a time-consuming and costly process, and they fail to fully harness the potential benefits of deep feature representations within the deep feature domain. To tackle the mentioned issues, we propose a novel semi-supervised CD method called PDLCD, which strategically leverages useful information from massive unlabeled data to complement labeled data with just a few samples. Specifically, changed objects and unchanged backgrounds of bitemporal RS images are various and complex, our approach advocates dividing each category into multiple subclasses in the deep feature domain. In this scheme, the high-level feature of each subclass follows a Gaussian distribution. Then, the prototype discriminative learning (PDL) is introduced to explicitly encourage deep features of samples closer to the nearest prototype within their respective category, and away from all prototypes of other categories. We design feature discriminative loss (FDL) to implement PDL for constructing more pronounced intraclass compactness and interclass variability. Finally, we compute the supervised loss based on a limited set of labeled data, incorporate the unsupervised loss leveraging a substantial volume of unlabeled data, and include FDL within the deep feature domain to collectively optimize the model. Extensive experiments carried out on three challenging RS image CD datasets illustrate that our proposed semi-supervised CD method obtains better CD performance than previous counterparts. The source code is available at:https://github.com/Youzhihui/PDLCD.

The development of deep learning in remote sensing (RS) visual tasks has led to remarkable progress in RS image change detection (CD). However, RS bi-temporal images cover complex and confusing scenes due to natural environmental factors, which presents challenges for CD task. How to effectively exploit long-range dependencies and sensitively discriminate real-changes with various scales from pseudo-changes are urgent problems. It is especially obvious for the changes of building structures man-made. This paper presents a CD approach named CSViG, which utilizes Siamese Vision Graph neural network (SViG) with crossed feature fusion. SViG acts as a feature extractor to capture richer short- and long-range dependencies. Crossed feature fusion consists of a horizontal feature fusion module (HFFM) and a vertical feature fusion module (VFFM). HFFM designs cross-concatenation (CC) way to reveal real-changes from pseudo-change in the same horizontal stage, after which global and local features are extracted by using attention mechanism and multi-scale depth-wise separable convolution. VFFM further fuses complementary content from vertical multiple stages to effectively represent change regions of different sizes (tiny or huge) by using attention mechanism. Extensive comparative experiments conducted on three available building change detection datasets demonstrate that the proposed method achieves better CD performance than previous counterparts.