Remote sensing image change detection is a core task of remote sensing image analysis; its purpose is to identify and quantify land cover changes in different periods. However, when the existing methods deal with complex features and subtle changes in buildings, vegetation, water bodies, roads, and other ground objects, there are often problems of false detection and missing detection, which affect the detection accuracy. To improve the accuracy of change detection, a multi-scale feature fusion network based on difference enhancement (FEDNet) is proposed. The FEDNet consists of a difference enhancement module (DEM) and a multi-scale feature fusion module (MFM). By summing the variation features of two-phase remote sensing images, the DEM enhances pixel-level differences, captures subtle changes, and aggregates features. The MFM fully integrates the multi-stage deep semantic information, which enables better extraction of changing features in complex scenes. Experiments on the LEVIR-CD, CLCD, WHU, NJDS, and GBCNR datasets show that the FEDNet significantly improves the detection efficiency of changes in buildings, cities, and vegetation. In terms of F1 value, IoU (Intersection over Union), precision, and recall rate, the FEDNet is superior to existing methods, which verifies its excellent performance.

Accurate identification of weed species is a fundamental task for promoting efficient farmland management. Existing recognition approaches are typically based on either conventional Convolutional Neural Networks (CNNs) or the more recent Vision Transformers (ViTs). CNNs demonstrate strong capability in capturing local spatial patterns, yet they are often limited in modeling long-range dependencies. In contrast, ViTs can effectively capture global contextual information through self-attention, but they may neglect fine-grained local features. These inherent shortcomings restrict the recognition performance of current models. To overcome these limitations, we propose a lightweight hybrid architecture, termed RepEfficientViT,which integrates convolutional operations with Transformer-based self-attention. This design enables the simultaneous aggregation of both local details and global dependencies. Furthermore, we employ a structural re-parameterization strategy to enhance the representational capacity of convolutional layers without introducing additional parameters or computational overhead. Experimental evaluations reveal that RepEfficientViT consistently surpasses state-of-the-art CNN and Transformer baselines. Specifically, the model achieves an accuracy of 94.77%, a precision of 94.75%, a recall of 94.93%, and an F1-score of 94.84%. In terms of efficiency, RepEfficientViT requires only 223.54 M FLOPs and 1.34 M parameters, while attaining an inference latency of merely 25.13 ms on CPU devices. These results demonstrate that the proposed model is well-suited for deployment in edge-computing scenarios subject to stringent computational and storage constraints.

Recognizing mangrove species is a challenging task in coastal wetland ecological monitoring due to the complex environment, high species similarity, and the inherent symmetry within the structural features of mangrove species. Many species coexist, exhibiting only subtle differences in leaf shape and color, which increases the risk of misclassification. Additionally, mangroves grow in intertidal environments with varying light conditions and surface reflections, further complicating feature extraction. Small species are particularly hard to distinguish in dense vegetation due to their symmetrical features that are difficult to differentiate at the pixel level. While hyperspectral imaging offers some advantages in species recognition, its high equipment costs and data acquisition complexity limit its practical application. To address these challenges, we propose MHAGFNet, a segmentation-based mangrove species recognition network. The network utilizes easily accessible RGB remote sensing images captured by drones, ensuring efficient data collection. MHAGFNet integrates a Multi-Scale Feature Fusion Module (MSFFM) and a Multi-Head Attention Guide Module (MHAGM), which enhance species recognition by improving feature capture across scales and integrating both global and local details. In this study, we also introduce MSIDBG, a dataset created using high-resolution UAV images from the Shankou Mangrove National Nature Reserve in Beihai, China. Extensive experiments demonstrate that MHAGFNet significantly improves accuracy and robustness in mangrove species recognition.

Although some progress has been made in deep learning-based remote sensing image change detection, the complexity of scenes and the diversity of changes in remote sensing images lead to challenges related to background interference. For instance, remote sensing images typically contain numerous background regions, while the actual change regions constitute only a small proportion of the overall image. To address these challenges in remote sensing image change detection, this paper proposes a Dynamic Adaptive Context Attention Network (DACA-Net) based on an exchanging dual encoder–decoder (EDED) architecture. The core innovation of DACA-Net is the development of a novel Dynamic Adaptive Context Attention Module (DACAM), which learns attention weights and automatically adjusts the appropriate scale according to the features present in remote sensing images. By fusing multi-scale contextual features, DACAM effectively captures information regarding changes within these images. In addition, DACA-Net adopts an EDED architectural design, where the conventional convolutional modules in the EDED framework are replaced by DACAM modules. Unlike the original EDED architecture, DACAM modules are embedded after each encoder unit, enabling dynamic recalibration of T1/T2 features and cross-temporal information interaction. This design facilitates the capture of fine-grained change features at multiple scales. This architecture not only facilitates the extraction of discriminative features but also promotes a form of structural symmetry in the processing pipeline, contributing to more balanced and consistent feature representations. To validate the applicability of our proposed method in real-world scenarios, we constructed an Unmanned Aerial Vehicle (UAV) remote sensing dataset named the Guangxi Beihai Coast Nature Reserves (GBCNR). Extensive experiments conducted on three public datasets and our GBCNR dataset demonstrate that the proposed DACA-Net achieves strong performance across various evaluation metrics. For example, it attains an F1 score (F1) of 72.04% and a precision(P) of 66.59% on the GBCNR dataset, representing improvements of 3.94% and 4.72% over state-of-the-art methods such as semantic guidance and spatial localization network (SGSLN) and bi-temporal image Transformer (BIT), respectively. These results verify that the proposed network significantly enhances the ability to detect critical change regions and improves generalization performance.

Change detection (CD) is essential for Earth observation tasks, as it identifies alterations in specific geographic areas over time. The advancement of deep learning has significantly improved the accuracy of CD. However, encoder–decoder architectures often struggle to effectively capture temporal differences. Encoders may lose critical spatial details, while decoders can introduce bias due to inconsistent receptive fields across layers. To address these limitations, this paper proposes an enhanced symmetric network, termed FDRF (feature difference and receptive field), which incorporates two novel components: the multibranch feature difference extraction (MFDE) module and the adaptive ensemble decision (AED) module. MFDE independently extracts differential features from bitemporal images at each encoder layer, using multiscale fusion to retain image content and improve the quality of feature difference modeling. AED assigns confidence weights to predictions from different decoder layers based on their receptive field sizes and then combines them adaptively to reduce scale-related bias. To validate the effectiveness and robustness of FDRF, experiments were conducted on five public datasets (SYSU, LEVIR-CD, WHU, NJDS, and CLCD), as well as a UAV-based dataset collected from two national coastal nature reserves in Guangxi Beihai, China. The results demonstrate that FDRF consistently outperforms existing methods in accuracy and robustness across diverse scenarios.

Magnetic induction imaging technology, as a non-invasive detection method based on the principle of electromagnetic induction, has a wide range of applications in the field of materials science and engineering with the advantages of no radiation and fast imaging. However, it has not been improved to address the problems of high contact measurement interference and low spatial resolution of traditional strain detection methods in bulk materials engineering. For this reason, this study proposes a magnetic induction detection technique incorporating metal particle assistance and designs a hardware detection system based on an eight-coil sensor to improve the sensitivity and accuracy of strain detection. Through finite element simulation and an image reconstruction algorithm, the conductivity distribution reconstruction was realized. Taking asphalt concrete as the research object, particle-reinforced composite specimens with added metal particles were prepared. On this basis, a hardware detection system with eight-coil sensors was designed and constructed, and the functionality and stability of the system were verified. Using finite element analysis technology, two-dimensional and three-dimensional simulation models were established to focus on analyzing the effects of different coil turns and excitation parameters on the induced voltage signal. The method proposed in this study provides a new technical approach for non-contact strain detection in road engineering and can also be applied to other composite materials.