Remote sensing image change detection plays an important role in urban planning and environmental monitoring. However, the existing change detection algorithms have limited ability in feature extraction, feature relationship understanding, and capture of small target features and edge detail features, which leads to the loss of some edge detail information and small target features. To this end, a new dual attention-guided multiscale feature aggregation network is proposed. In encoding stage, the fully convolutional dual-branch structure is used to extract the semantic features of different scales, and then the multi-scale adjacent semantic information aggregation module is used to aggregate the adjacent semantic features at different scales, which can better capture and fuse the features of different scales, thereby improving the accuracy and robustness of change detection. In decoding stage, the dual attention fusion module is proposed to guide and fuse the features extracted from different scales along the spatial and channel directions, and reduce the background noise interference. In addition, this paper also proposes a three-branch feature fusion module and a global semantic information enhancement module to make the network better integrate global semantics and differential semantics and further integrate high-level semantic features. We also introduce an auxiliary classifier in decoding stage to provide additional supervision signals, and fuse the output of the three auxiliary classifiers with the output of the main decoder to further achieve multi-scale feature fusion. The comparative experiments on three remote sensing data sets show that the proposed method is superior to the existing change detection methods.

With the rapid advancement of remote sensing technology, change detection using bitemporal remote sensing images has significant applications in land use planning and environmental monitoring. The emergence of Convolutional Neural Networks (CNNs) has accelerated the development of deep learning-based change detection. However, existing deep learning algorithms exhibit limitations in understanding bitemporal feature relationships and accurately identifying change region boundaries. Moreover, they inadequately explore feature interactions between bitemporal images before extracting differential features. To address these issues, this paper proposes a novel Interactive and Supervised Dual-Mode Attention Network (ISDANet). In the feature encoding stage, we employ the lightweight MobileNetV2 as the backbone to extract bitemporal features. Additionally, we design the Neighbor Feature Aggregation Module (NFAM) to aggregate semantic features from adjacent scales within the dual-branch backbone, enhancing the representation of temporal features. We further introduce the Interactive Attention Enhancement Module (IAEM), which effectively integrates self-attention and cross-attention mechanisms. This establishes deep interactions between bitemporal features, suppresses irrelevant noise, and ensures precise focus on true change regions. In the feature decoding stage, the Supervised Attention Module (SAM) reweights differential features and leverages supervisory signals to guide the learning of attention mechanisms, significantly improving boundary detection accuracy. SAM dynamically aggregates multi-level features, balancing high-level semantics and low-level details to capture subtle changes in complex scenes. The proposed model achieves F1 scores that are 0.28%, 1.6%, and 0.76% higher than the best comparative method, SEIFNet, on three CD datasets (LEVIR-CD, GZ-CD, and SYSU-CD), respectively, while maintaining a lightweight design with only 6.93M parameters and 3.46G FLOPs. The code is available at https://github.com/RenHongjin6/ISDANet.

Change detection is crucial for evaluating land use, land cover changes, and sustainable development, constituting a significant component of Earth observation tasks. The difficulty in extracting features from high-resolution images, coupled with the complexity of image content, poses challenges for traditional change detection algorithms in terms of accuracy and applicability. The recent emergence of deep learning methods has led to substantial progress in the field of change detection. However, existing frameworks often involve the simplistic integration of bi-temporal features in specific areas, lacking the fusion of temporal information and semantic details in the images. In this paper, we propose an attention-guided multi-scale fusion network (AMFNet), which effectively integrates bi-temporal image features and diverse semantics at both the encoding and decoding stages. AMFNet utilizes a unique attention-guided mechanism to dynamically adjust feature fusion, enhancing adaptability and accuracy in change detection tasks. Our method intelligently incorporates temporal information into the deep learning model, considering the temporal dependency inherent in these tasks. We decode based on an interactive feature map, which improves the model’s understanding of evolving patterns over time. Additionally, we introduce multi-level supervised training to facilitate the learning of fused features across multiple scales. In comparison with different algorithms, our proposed method achieves F1 values of 0.9079, 0.8225, and 0.8809 in the LEVIR-CD, GZ-CD, and SYSU-CD datasets, respectively. Our model outperforms the SOTA model, SAGNet, by 0.69% in terms of F1 and 1.15% in terms of IoU on the LEVIR-CD dataset, by 2.8% in terms of F1 and 1.79% in terms of IoU on the GZ-CD dataset, and by 0.54% in terms of F1 and 0.38% in terms of IoU on the SYSU-CD dataset. The method proposed in this study can be applied to various complex scenarios, establishing a change detection method with strong model generalization capabilities.

In the domains of geographic information systems and remote sensing image analysis, change detection is vital for examining surface variations in high-resolution remote sensing pictures. However, the intricate texture characteristics and rich details found in high-resolution remote sensing photos are difficult for conventional change detection systems to deal with. Target misdetection, missed detections, and edge blurring are further problems with current deep learning-based methods. This research proposes a high-resolution city change detection network based on difference and attention mechanisms under multi-scale feature fusion (MDANet) to address these issues and improve the accuracy of change detection. First, to extract features from dual-temporal remote sensing pictures, we use the Siamese architecture as the encoder network. The Difference Feature Module (DFM) is employed to learn the difference information between the dual-temporal remote sensing images. Second, the extracted difference features are optimized with the Attention Refinement Module (ARM). The Cross-Scale Fusion Module (CSFM) combines and enhances the optimized attention features, effectively capturing subtle differences in remote sensing images and learning the finer details of change targets. Finally, thorough tests on the BTCDD dataset, LEVIR-CD dataset, and CDD dataset show that the MDANet algorithm performs at a cutting-edge level.

Atopic dermatitis (AD) is a prevalent inflammatory skin disease. Ample evidence has shown that non-coding RNAs play important roles in the progression of AD, however, the function of plasma microRNAs in AD is poorly understood. To identify key plasma microRNAs and explore their potential roles in AD. Plasma microRNAs from five children with AD and five control children were sequenced by microRNA sequencing (miRNA-seq) and five differentially expressed microRNAs were verified by RT-qPCR in 30 AD and 15 control children. The most differentially expressed microRNA, hsa-miR-194-5p, was selected for further analysis. Human epidermal keratinocytes were subjected to RNA sequencing following over-expression of hsa-miR-194-5p, and down-regulated genes were detected by RT-qPCR. The diagnostic potential of hsa-miR-194-5p in AD was evaluated based on receiver operating characteristic (ROC) curve analysis. Further hsa-miR-194-5p-regulated expression and gene-hsa-miR-194-5p interactions were evaluated by western blotting and luciferase assays, respectively. We identified 40 differentially expressed microRNAs, 26 up-regulated and 14 down-regulated, in children with AD compared with controls. Among the five verified plasma microRNAs, the most significant change was down-regulated hsa-miR-194-5p, which was shown to potentially serve as a biomarker for AD based on ROC analysis. Twenty-two down-regulated genes were observed following hsa-miR-194-5p over-expression, which were significantly associated with keratinocyte differentiation and establishment of the skin barrier. Moreover, HS3ST2 protein expression was down-regulated following over-expression of hsa-miR-194-5p, and the 3'-UTR of HS3ST2 was shown to bind to hsa-miR-194-5p. Hsa-miR-194-5p might be involved in the pathogenesis of AD by regulating HS3ST2 expression.