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Pine wilt disease (PWD) is a highly destructive worldwide forest quarantine disease that has the potential to destroy entire pine forests in a relatively brief period, resulting in significant economic losses and environmental damage. Manual monitoring, biochemical detection and satellite remote sensing are frequently inadequate for the timely detection and control of pine wilt disease. This paper presents a fusion model, which integrates the Mamba model and the attention mechanism, for deployment on unmanned aerial vehicles (UAVs) to detect infected pine trees. The experimental dataset presented in this paper comprises images of pine trees captured by UAVs in mixed forests. The images were gathered primarily during the spring of 2023, spanning the months of February to May. The images were subjected to a preprocessing phase, during which they were transformed into the research dataset. The fusion model comprised three principal components. The initial component is the Mamba backbone network with State Space Model (SSM) at its core, which is capable of extracting pine wilt features with a high degree of efficacy. The second component is the attention network, which enables our fusion model to center on PWD features with greater efficacy. The optimal configuration was determined through an evaluation of various attention mechanism modules, including four attention modules. The third component, Path Aggregation Feature Pyramid Network (PAFPN), facilitates the fusion and refinement of data at varying scales, thereby enhancing the model’s capacity to detect multi-scale objects. Furthermore, the convolutional layers within the model have been replaced with depth separable convolutional layers (DSconv), which has the additional benefit of reducing the number of model parameters and improving the model’s detection speed. The final fusion model was validated on a test set, achieving an accuracy of 90.0%, a recall of 81.8%, a map of 86.5%, a parameter counts of 5.9 Mega, and a detection speed of 40.16 FPS. In comparison to Yolov8, the accuracy is enhanced by 7.1%, the recall by 5.4%, and the map by 3.1%. These outcomes demonstrate that our fusion model is appropriate for implementation on edge devices, such as UAVs, and is capable of effective detection of PWD.

Semantic segmentation is primarily employed to generate accurate prediction labels for each pixel of the input image, and then classify the images according to the generated labels. Semantic segmentation of building and water in remote sensing images helps us to conduct reasonable land planning for a city. However, many current mature networks face challenges in simultaneously attending to both contextual and spatial information when performing semantic segmentation on remote sensing imagery. This often leads to misclassifications and omissions. Therefore, this paper proposes a Dual-Branch Network with Spatial Supplementary Information (SPNet) to address the aforementioned issues. We introduce a Context-aware Spatial Feature-Extractor Unit (CSF) to extract contextual and spatial information, followed by the Feature-Interaction Module (FIM) to supplement contextual semantic information with spatial details. Additionally, incorporating the Goal-Oriented Attention Mechanism helps in handling noise. Finally, to obtain more detailed branches, a Multichannel Deep Feature-Extraction Module (MFM) is introduced to extract features from shallow-level network layers. This branch guides the fusion of low-level semantic information with high-level semantic information. Experiments were conducted on building and water datasets, respectively. The results indicate that the segmentation accuracy of the model proposed in this paper surpasses that of other existing mature models. On the building dataset, the mIoU reaches 87.57, while on the water dataset, the mIoU achieves 96.8, which means that the model introduced in this paper demonstrates strong generalization capabilities.

Segmenting clouds and their shadows is a critical challenge in remote sensing image processing. The shape, texture, lighting conditions, and background of clouds and their shadows impact the effectiveness of cloud detection. Currently, architectures that maintain high resolution throughout the entire information-extraction process are rapidly emerging. This parallel architecture, combining high and low resolutions, produces detailed high-resolution representations, enhancing segmentation prediction accuracy. This paper continues the parallel architecture of high and low resolution. When handling high- and low-resolution images, this paper employs a hybrid approach combining the Transformer and CNN models. This method facilitates interaction between the two models, enabling the extraction of both semantic and spatial details from the images. To address the challenge of inadequate fusion and significant information loss between high- and low-resolution images, this paper introduces a method based on ASMA (Axial Sharing Mixed Attention). This approach establishes pixel-level dependencies between high-resolution and low-resolution images, aiming to enhance the efficiency of image fusion. In addition, to enhance the effective focus on critical information in remote sensing images, the AGM (Attention Guide Module) is introduced, to integrate attention elements from original features into ASMA, to alleviate the problem of insufficient channel modeling of the self-attention mechanism. Our experimental results on the Cloud and Cloud Shadow dataset, the SPARCS dataset, and the CSWV dataset demonstrate the effectiveness of our method, surpassing the state-of-the-art techniques for cloud and cloud shadow segmentation.

Accurate snow depth estimation is of significant importance, particularly for preventing avalanche disasters and predicting flood seasons. The predominant approaches for such snow depth estimation, based on deep learning methods, typically rely on passive microwave remote sensing data. However, due to the low resolution of passive microwave remote sensing data, it often results in low-accuracy outcomes, posing considerable limitations in application. To further improve the accuracy of snow depth estimation, in this paper, we used active microwave remote sensing data. We fused multi-spectral optical satellite images, synthetic aperture radar (SAR) images and land cover distribution images to generate a snow remote sensing dataset (SRSD). It is a first-of-its-kind dataset that includes active microwave remote sensing images in high-latitude regions of Asia. Using these novel data, we proposed a multi-scale feature perception and aggregation neural network (MFPANet) that focuses on improving feature extraction from multi-source images. Our systematic analysis reveals that the proposed approach is not only robust but also achieves high accuracy in snow depth estimation compared to existing state-of-the-art methods, with RMSE of 0.360 and with MAE of 0.128. Finally, we selected several representative areas in our study region and applied our method to map snow depth distribution, demonstrating its broad application prospects.

In remote sensing image processing, the segmentation of clouds and their shadows is a fundamental and vital task. For cloud images, traditional deep learning methods often have weak generalization capabilities and are prone to interference from ground objects and noise, which not only results in poor boundary segmentation but also causes false and missed detections of small targets. To address these issues, we proposed a multi-branch attention fusion network (MAFNet). In the encoder section, the dual branches of ResNet50 and the Swin transformer extract features together. A multi-branch attention fusion module (MAFM) uses positional encoding to add position information. Additionally, multi-branch aggregation attention (MAA) in the MAFM fully fuses the same level of deep features extracted by ResNet50 and the Swin transformer, which enhances the boundary segmentation ability and small target detection capability. To address the challenge of detecting small cloud and shadow targets, an information deep aggregation module (IDAM) was introduced to perform multi-scale deep feature aggregation, which supplements high semantic information, improving small target detection. For the problem of rough segmentation boundaries, a recovery guided module (RGM) was designed in the decoder section, which enables the model to effectively allocate attention to complex boundary information, enhancing the network’s focus on boundary information. Experimental results on the Cloud and Cloud Shadow dataset, HRC-WHU dataset, and SPARCS dataset indicate that MAFNet surpasses existing advanced semantic segmentation techniques.

Remote sensing image change detection (CD) is an important means in remote sensing data analysis tasks, which can help us understand the surface changes in high-resolution (HR) remote sensing images. Traditional pixel-based and object-based methods are only suitable for low- and medium-resolution images, and are still challenging for complex texture features and detailed image detail processing in HR images. At present, the method based on deep learning has problems such as inconsistent fusion and difficult model training in the combination of the difference feature information of the deep and shallow layers and the attention mechanism, which leads to errors in the distinction between the changing region and the invariant region, edge detection and small target detection. In order to solve the above problems of inconsistent fusions of feature information aggregation and attention mechanisms, and indistinguishable change areas, we propose a multi-scale feature fusion Siamese network based on attention mechanism (ABMFNet). To tackle the issues of inconsistent fusion and alignment difficulties when integrating multi-scale fusion and attention mechanisms, we introduce the attention-based multi-scale feature fusion module (AMFFM). This module not only addresses insufficient feature fusion and connection between different-scale feature layers, but also enables the model to automatically learn and prioritize important features or regions in the image. Additionally, we design the cross-scale fusion module (CFM) and the difference feature enhancement pyramid structure (DEFPN) to assist the AMFFM module in integrating differential information effectively. These modules bridge the spatial disparity between low-level and high-level features, ensuring efficient connection and fusion of spatial difference information. Furthermore, we enhance the representation and inference speed of the feature pyramid by incorporating a feature enhancement module (FEM) into DEFPN. Finally, the BICD dataset proposed by the laboratory and public datasets LEVIR-CD and BCDD are compared and tested. We use F1 score and MIoU values as evaluation metrics. For AMBMFNet, the F1 scores on the three datasets are 77.69%, 81.57%, and 77.91%, respectively, while the MIoU values are 84.65%, 85.84%, and 84.54%, respectively. The experimental results show that ABMFNet has better effectiveness and robustness.

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.

Change detection is widely used in the field of building monitoring. In recent years, the progress of remote sensing image technology has provided high-resolution data. However, unlike other tasks, change detection focuses on the difference between dual-input images, so the interaction between bi-temporal features is crucial. However, the existing methods have not fully tapped the potential of multi-scale bi-temporal features to interact layer by layer. Therefore, this paper proposes a multi-scale feature interaction network (MFINet). The network realizes the information interaction of multi-temporal images by inserting a bi-temporal feature interaction layer (BFIL) between backbone networks at the same level, guides the attention to focus on the difference region, and suppresses the interference. At the same time, a double temporal feature fusion layer (BFFL) is used at the end of the coding layer to extract subtle difference features. By introducing the transformer decoding layer and improving the recovery effect of the feature size, the ability of the network to accurately capture the details and contour information of the building is further improved. The F1 of our model on the public dataset LEVIR-CD reaches 90.12%, which shows better accuracy and generalization performance than many state-of-the-art change detection models.

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.

The tea industry, as one of the most globally important agricultural products, is characterized by pests and diseases that pose a serious threat to yield and quality. These diseases and pests often present different scales and morphologies, and some pest and disease target sizes can be tiny and difficult to detect. To solve these problems, we propose TeaViTNet, a multi-scale attention-based tea pest and disease detection model that combines CNNs and Transformers. First, MobileViT is used as the feature extraction backbone network. MobileViT captures and analyzes the tiny pest and disease features in the image via a self-attention mechanism and global feature extraction. Second, the EMA-PANet network is introduced to optimize the model’s learning and attention to the Apolygus lucorum and leaf blight regions via an efficient multi-scale attention module with cross-space learning, which improves the model’s ability to understand multi-scale information. In addition, RFBNet is embedded in the module to further expand the perceptual range and effectively capture the information of tiny features in tea leaf images. Finally, the ODCSPLayer convolutional block is introduced, aiming to focus on acquiring richer gradient flow information. The experimental results show that the TeaViTNet model proposed in this paper has an average accuracy of 89.1%, which is a significant improvement over the baseline network MobileViT and is capable of accurately detecting Apolygus lucorum and leaf blight of different scales and complexities.

The ground is typically hidden by cloud and snow in satellite images, which have a similar visible spectrum and complex spatial distribution characteristics. The detection of cloud and snow is important for increasing image availability and studying climate change. To address the issues of the low classification accuracy and poor generalization effect by the traditional threshold method, as well as the problems of the misdetection of overlapping regions, rough segmentation results, and a loss of boundary details in existing algorithms, this paper designed a Multi-level Attention Interaction Network (MAINet). The MAINet uses a modified ResNet50 to extract features and introduces a Detail Feature Extraction module to extract multi-level information and reduce the loss of details. In the last down-sampling, the Deep Multi-head Information Enhancement module combines a CNN and a Transformer structure to make deep semantic features more distinct and reduce redundant information. Then, the Feature Interactive and Fusion Up-sampling module enhances the information extraction of deep and shallow information and, then, guides and aggregates each to make the learned semantic features more comprehensive, which can better recover remote sensing images and increase the prediction accuracy. The MAINet model we propose performed satisfactorily in handling cloud and snow detection and segmentation tasks in multiple scenarios. Experiments on related data sets also showed that the MAINet algorithm exhibited the best performance.

Cloud and cloud shadow segmentation is one of the most critical challenges in remote sensing image processing. Because of susceptibility to factors such as disturbance from terrain features and noise, as well as a poor capacity to generalize, conventional deep learning networks, when directly used to cloud and cloud shade detection and division, have a tendency to lose fine features and spatial data, leading to coarse segmentation of cloud and cloud shadow borders, false detections, and omissions of targets. To address the aforementioned issues, a multi-scale strip feature attention network (MSFANet) is proposed. This approach uses Resnet18 as the backbone for obtaining semantic data at multiple levels. It incorporates a particular attention module that we name the deep-layer multi-scale pooling attention module (DMPA), aimed at extracting multi-scale contextual semantic data, deep channel feature information, and deep spatial feature information. Furthermore, a skip connection module named the boundary detail feature perception module (BDFP) is introduced to promote information interaction and fusion between adjacent layers of the backbone network. This module performs feature exploration on both the height and width dimensions of the characteristic pattern to enhance the recovery of boundary detail intelligence of the detection targets. Finally, during the decoding phase, a self-attention module named the cross-layer self-attention feature fusion module (CSFF) is employed to direct the aggregation of deeplayer semantic feature and shallow detail feature. This approach facilitates the extraction of feature information to the maximum extent while conducting image restoration. The experimental outcomes unequivocally prove the efficacy of our network in effectively addressing complex cloud-covered scenes, showcasing good performance across the cloud and cloud shadow datasets, the HRC_WHU dataset, and the SPARCS dataset. Our model outperforms existing methods in terms of segmentation accuracy, underscoring its paramount importance in the field of cloud recognition research.

Change detection is an important component in the field of remote sensing. At present, deep-learning-based change-detection methods have acquired many breakthrough results. However, current algorithms still present issues such as target misdetection, false alarms, and blurry edges. To alleviate these problems, this work proposes a network based on feature differences and attention mechanisms. This network includes a Siamese architecture-encoding network that encodes images at different times, a Difference Feature-Extraction Module (DFEM) for extracting difference features from bitemporal images, an Attention-Regulation Module (ARM) for optimizing the extracted difference features through attention, and a Cross-Scale Feature-Fusion Module (CSFM) for merging features from different encoding stages. Experimental results demonstrate that this method effectively alleviates issues of target misdetection, false alarms, and blurry edges.

Among the most difficult difficulties in contemporary satellite image-processing subjects is cloud and cloud shade segmentation. Due to substantial background noise interference, existing cloud and cloud shadow segmentation techniques would result in false detection and missing detection. We propose a Location Pooling Multi-Scale Network (LPMSNet) in this study. The residual network is utilised as the backbone in this method to acquire semantic info on various levels. Simultaneously, the Location Attention Multi-Scale Aggregation Module (LAMA) is introduced to obtain the image’s multi-scale info. The Channel Spatial Attention Module (CSA) is introduced to boost the network’s focus on segmentation goals. Finally, in view of the problem that the edge details of cloud as well as cloud shade are easily lost, this work designs the Scale Fusion Restoration Module (SFR). SFR can perform picture upsampling as well as the acquisition of edge detail information from cloud as well as cloud shade. The mean intersection over union (MIoU) accuracy of this network reached 94.36% and 81.60% on the Cloud and Cloud Shadow Dataset and the five-category dataset L8SPARCS, respectively. On the two-category HRC-WHU Dataset, the accuracy of the network on the intersection over union (IoU) reached 90.51%. In addition, in the Cloud and Cloud Shadow Dataset, our network achieves 97.17%, 96.83%, and 97.00% in precision (P), recall (R), and F1 score (F1) in cloud segmentation tasks, respectively. In the cloud shadow segmentation task, precision (P), recall (R), and F1 score (F1) reached 95.70%, 96.38%, and 96.04%, respectively. Therefore, this method has a significant advantage over the current cloud and cloud shade segmentation methods.

Recently, deep learning-based change detection methods for bitemporal remote sensing images have achieved promising results based on fully convolutional neural networks. However, due to the inherent characteristics of convolutional neural networks, if the previous block fails to correctly segment the entire target, erroneous predictions might accumulate in the subsequent blocks, leading to incomplete change detection results in terms of structure. To address this issue, we propose a bitemporal remote sensing image change detection network based on a Siamese-attention feedback architecture, referred to as SAFNet. First, we propose a global semantic module (GSM) on the encoder network, aiming to generate a low-resolution semantic change map to capture the changed objects. Second, we introduce a temporal interaction module (TIM), which is built through each encoding and decoding block, using the feature feedback between two temporal blocks to enhance the network’s perception ability of the entire changed target. Finally, we propose two auxiliary modules—the change feature extraction module (CFEM) and the feature refinement module (FRM)—which are further used to learn the fine boundaries of the changed target. The deep model we propose produced satisfying results in dual-temporal remote sensing image change detection. Extensive experiments on two remote sensing image change detection datasets demonstrate that the SAFNet algorithm exhibits state-of-the-art performance.

Forest fires pose significant hazards to ecological environments and economic society. The detection of forest fire smoke can provide crucial information for the suppression of early fires. Previous detection models based on deep learning have been limited in detecting small smoke and smoke with smoke-like interference. In this paper, we propose a lightweight model for forest fire smoke detection that is suitable for UAVs. Firstly, a smoke dataset is created from a combination of forest smoke photos obtained through web crawling and enhanced photos generated by using the method of synthesizing smoke. Secondly, the GSELAN and GSSPPFCSPC modules are built based on Ghost Shuffle Convolution (GSConv), which efficiently reduces the number of parameters in the model and accelerates its convergence speed. Next, to address the problem of indistinguishable feature boundaries between clouds and smoke, we integrate coordinate attention (CA) into the YOLO feature extraction network to strengthen the extraction of smoke features and attenuate the background information. Additionally, we use Content-Aware Reassembly of FEatures (CARAFE) upsampling to expand the receptive field in the feature fusion network and fully exploit the semantic information. Finally, we adopt SCYLLA-Intersection over Union (SIoU) loss as a replacement for the original loss function in the prediction phase. This substitution leads to improved convergence efficiency and faster convergence. The experimental results demonstrate that the LMDFS model proposed for smoke detection achieves an accuracy of 80.2% with a 5.9% improvement compared to the baseline and a high number of Frames Per Second (FPS)—63.4. The model also reduces the parameter count by 14% and Giga FLoating-point Operations Per second (GFLOPs) by 6%. These results suggest that the proposed model can achieve a high accuracy while requiring fewer computational resources, making it a promising approach for practical deployment in applications for detecting smoke.

Change detection (CD) is an important remote sensing (RS) data analysis technology. Existing remote sensing change detection (RS-CD) technologies cannot fully consider situations where pixels between bitemporal images do not correspond well on a one-to-one basis due to factors such as seasonal changes and lighting conditions. Existing networks construct two identical feature extraction branches through convolution, which share weights. The two branches work independently and do not merge until the feature mapping is sent to the decoder head. This results in a lack of feature information interaction between the two images. So, directing attention to the change area is of research interest. In complex backgrounds, the loss of edge details is very important. Therefore, this paper proposes a new CD algorithm that extracts multi-scale feature information through the backbone network in the coding stage. According to the task characteristics of CD, two submodules (the Feature Interaction Module and Detail Feature Guidance Module) are designed to make the feature information between the bitemporal RS images fully interact. Thus, the edge details are restored to the greatest extent while fully paying attention to the change areas. Finally, in the decoding stage, the feature information of different levels is fully used for fusion and decoding operations. We build a new CD dataset to further verify and test the model’s performance. The generalization and robustness of the model are further verified by using two open datasets. However, due to the relatively simple construction of the model, it cannot handle the task of multi-classification CD well. Therefore, further research on multi-classification CD algorithms is recommended. Moreover, due to the high production cost of CD datasets and the difficulty in obtaining them in practical tasks, future research will look into semi-supervised or unsupervised related CD algorithms.

Cloud and cloud shadow detection are essential in remote sensing imagery applications. Few semantic segmentation models were designed specifically for clouds and their shadows. Based on the visual and distribution characteristics of clouds and their shadows in remote sensing imagery, this paper provides a multi-supervised feature fusion attention network. We design a multi-scale feature fusion block (FFB) for the problems caused by the complex distribution and irregular boundaries of clouds and shadows. The block consists of a fusion convolution block (FCB), a channel attention block (CAB), and a spatial attention block (SPA). By multi-scale convolution, FCB reduces excessive semantic differences between shallow and deep feature maps. CAB focuses on global and local features through multi-scale channel attention. Meanwhile, it fuses deep and shallow feature maps with non-linear weighting to optimize fusion performance. SPA focuses on task-relevant areas through spatial attention. With the three blocks above, FCB alleviates the difficulties of fusing multi-scale features. Additionally, it makes the network resistant to background interference while optimizing boundary detection. Our proposed model designs a class feature attention block (CFAB) to increase the robustness of cloud detection. The network achieves good performance on the self-made cloud and shadow dataset. This dataset is taken from Google Earth and contains remote sensing imagery from several satellites. The proposed model achieved a mean intersection over union (MIoU) of 94.10% on our dataset, which is 0.44% higher than the other models. Moreover, it shows high generalization capability due to its superior prediction results on HRC_WHU and SPARCS datasets.

The analysis of land cover types is helpful for detecting changes in land use categories and evaluating land resources. It is of great significance in environmental monitoring, land management, land planning, and mapping. At present, remote sensing imagery obtained by remote sensing is widely employed in the classification of land types. However, most of the existing methods have problems such as low classification accuracy, vulnerability to noise interference, and poor generalization ability. Here, a multi-scale contextual semantic guidance network is proposed for the classification of land cover types by deep learning. The whole model combines an attention mechanism with convolution to make up for the limitation that the convolution structure can only focus on local features. In the process of feature extraction, an interactive structure combining attention and convolution is introduced in the deep layer of the network to fully extract the abstract information. In this paper, the semantic information guidance module is introduced in the cross-layer connection part, ensuring that the semantic information between different levels can be used for mutual guidance, which is conducive to the classification process. A multi-scale fusion module is proposed at the decoder to fuse the features between different layers and avoid loss of information during the recovery process. Experiments on two public datasets demonstrate that the suggested approach has higher accuracy than existing models as well as strong generalization ability.

The change-detection task is essentially a binary semantic segmentation task of changing and invariant regions. However, this is much more difficult than simple binary tasks, as the changing areas typically include multiple terrains such as factories, farmland, roads, buildings, and mining areas. This requires the ability of the network to extract features. To this end, we propose a multi-branch collaborative change-detection network based on Siamese structure (MHCNet). In the model, three branches, the difference branch, global branch, and similar branch, are constructed to refine and extract semantic information from remote-sensing images. Four modules, a cross-scale feature-attention module (CSAM), global semantic filtering module (GSFM), double-branch information-fusion module (DBIFM), and similarity-enhancement module (SEM), are proposed to assist the three branches to extract semantic information better. The CSFM module is used to extract the semantic information related to the change in the remote-sensing image from the difference branch, the GSFM module is used to filter the rich semantic information in the remote-sensing image, and the DBIFM module is used to fuse the semantic information extracted from the difference branch and the global branch. Finally, the SEM module uses the similar information extracted with the similar branch to correct the details of the feature map in the feature-recovery stage.

The segmentation algorithm for buildings and waters is extremely important for the efficient planning and utilization of land resources. The temporal and space range of remote sensing pictures is growing. Due to the generic convolutional neural network’s (CNN) insensitivity to the spatial position information in remote sensing images, certain location and edge details can be lost, leading to a low level of segmentation accuracy. This research suggests a double-branch parallel interactive network to address these issues, fully using the interactivity of global information in a Swin Transformer network, and integrating CNN to capture deeper information. Then, by building a cross-scale multi-level fusion module, the model can combine features gathered using convolutional neural networks with features derived using Swin Transformer, successfully extracting the semantic information of spatial information and context. Then, an up-sampling module for multi-scale fusion is suggested. It employs the output high-level feature information to direct the low-level feature information and recover the high-resolution pixel-level features. According to experimental results, the proposed networks maximizes the benefits of the two models and increases the precision of semantic segmentation of buildings and waters.

Cloud detection is a critical task in remote sensing image tasks. Due to the influence of ground objects and other noises, the traditional detection methods are prone to miss or false detection and rough edge segmentation in the detection process. To avoid the defects of traditional methods, Cloud and Cloud Shadow Refinement Segmentation Networks are proposed in this paper. The network can correctly and efficiently detect smaller clouds and obtain finer edges. The model takes ResNet-18 as the backbone to extract features at different levels, and the Multi-scale Global Attention Module is used to strengthen the channel and spatial information to improve the accuracy of detection. The Strip Pyramid Channel Attention Module is used to learn spatial information at multiple scales to detect small clouds better. Finally, the high-dimensional feature and low-dimensional feature are fused by the Hierarchical Feature Aggregation Module, and the final segmentation effect is obtained by up-sampling layer by layer. The proposed model attains excellent results compared to methods with classic or special cloud segmentation tasks on Cloud and Cloud Shadow Dataset and the public dataset CSWV.

Water molecules were easy to combine with organic molecules and embed into the lattice of solid molecules to form a hydrate. Compared with anhydrous compounds, a hydrate has completely different physical and chemical properties. In this paper, terahertz (THz) spectra of five nucleosides in the solid and liquid phases were studied experimentally by Fourier-transform infrared spectroscopy (FTIR) in the frequency of 0.5–9 THz. In addition, the lattice energy, geometric structure, and vibration spectrum of the molecular crystal of the nucleosides were analyzed theoretically by the generalized energy-based fragmentation approach under periodic boundary conditions (denoted as PBC-GEBF). Furthermore, different nucleoside molecular morphology (monomer, polymer, and crystal), solvent (implicit and explicit water), and temperature/theoretical model effect on the THz spectra were mainly investigated. It was found that in the low-frequency band, the vibrational modes were generally originated from the collective vibration of all molecules involved (more than 99% of them were vibration; only less than 1% of them were rotation and translation), which can reflect the molecular structure and spatial distribution of different substances. The Gibbs free energy of thymidine monomer, dimer, tetramer, and crystal was studied. It was found that the cell-stacking energy had the greatest influence on the spectrum, indicating that only the crystal structure constrained by the periodic boundary conditions could well describe the experimental results. In addition, hydrophobic forces dominated the formation of new chemical bonds and strong inter-molecular interactions; the free water had little contribution to the THz spectrum of nucleosides, while crystalline water had a great influence on the spectrum.

Because clouds and snow block the underlying surface and interfere with the information extracted from an image, the accurate segmentation of cloud/snow regions is essential for imagery preprocessing for remote sensing. Nearly all remote sensing images have a high resolution and contain complex and diverse content, which makes the task of cloud/snow segmentation more difficult. A multi-branch convolutional attention network (MCANet) is suggested in this study. A double-branch structure is adopted, and the spatial information and semantic information in the image are extracted. In this way, the model’s feature extraction ability is improved. Then, a fusion module is suggested to correctly fuse the feature information gathered from several branches. Finally, to address the issue of information loss in the upsampling process, a new decoder module is constructed by combining convolution with a transformer to enhance the recovery ability of image information; meanwhile, the segmentation boundary is repaired to refine the edge information. This paper conducts experiments on the high-resolution remote sensing image cloud/snow detection dataset (CSWV), and conducts generalization experiments on two publicly available datasets (HRC_WHU and L8 SPARCS), and the self-built cloud and cloud shadow dataset. The MIOU scores on the four datasets are 92.736%, 91.649%, 80.253%, and 94.894%, respectively. The experimental findings demonstrate that whether it is for cloud/snow detection or more complex multi-category detection tasks, the network proposed in this paper can completely restore the target details, and it provides a stronger degree of robustness and superior segmentation capabilities.

Land cover semantic segmentation is an important technique in land. It is very practical in land resource protection planning, geographical classification, surveying and mapping analysis. Deep learning shows excellent performance in picture segmentation in recent years, but there are few semantic segmentation algorithms for land cover. When dealing with land cover segmentation tasks, traditional semantic segmentation networks often have disadvantages such as low segmentation precision and weak generalization due to the loss of image detail information and the limitation of weight distribution. In order to achieve high-precision land cover segmentation, this article develops a multi-scale feature aggregation network. Traditional convolutional neural network downsampling procedure has problems of detail information loss and resolution degradation; to fix these problems, a multi-scale feature extraction spatial pyramid module is made to assemble regional context data from different areas. In order to address the issue of incomplete information of traditional convolutional neural networks at multiple sizes, a multi-scale feature fusion module is developed to fuse attributes from various layers and several sizes to boost segmentation accuracy. Finally, a multi-scale convolutional attention module is presented to enhance the segmentation’s attention to the target in order to address the issue that the classic convolutional neural network has low attention capacity to the building waters in land cover segmentation. Through the contrast experiment and generalization experiment, it can be clearly demonstrated that the segmentation algorithm proposed in this paper realizes the high precision segmentation of land cover.

In recent years, the resolution of remote sensing images, especially aerial images, has become higher and higher, and the spans of time and space have become larger and larger. The phenomenon in which one class of objects can produce several kinds of spectra may lead to more errors in detection methods that are based on spectra. For different convolution methods, downsampling can provide some advanced information, which will lead to rough detail extraction; too deep of a network will greatly increase the complexity and calculation time of a model. To solve these problems, a multifunctional feature extraction model called MSNet (multifunctional feature-sharing network) is proposed, which is improved on two levels: depth feature extraction and feature fusion. Firstly, a residual shuffle reorganization branch is proposed; secondly, linear index upsampling with different levels is proposed; finally, the proposed edge feature attention module allows the recovery of detailed features. The combination of the edge feature attention module and linear index upsampling can not only provide benefits in learning detailed information, but can also ensure the accuracy of deep feature extraction. The experiments showed that MSNet achieved 81.33% MIoU on the Landover dataset.

A forest fire susceptibility map generated with the fire susceptibility model is the basis of fire prevention resource allocation. A more reliable susceptibility map helps improve the effectiveness of resource allocation. Thus, further improving the prediction accuracy is always the goal of fire susceptibility modeling. This paper developed a forest fire susceptibility model based on an ensemble learning method, namely light gradient boosting machine (LightGBM), to produce an accurate fire susceptibility map. In the modeling, a subtropical national forest park in the Jiangsu province of China was used as the case study area. We collected and selected eight variables from the fire occurrence driving factors for modeling based on correlation analysis. These variables are from topographic factors, climatic factors, human activity factors, and vegetation factors. For comparative analysis, another two popular modeling methods, namely logistic regression (LR) and random forest (RF) were also applied to construct the fire susceptibility models. The results show that temperature was the main driving factor of fire in the area. In the produced fire susceptibility map, the extremely high and high susceptibility areas that were classified by LR, RF, and LightGBM were 5.82%, 18.61%, and 19%, respectively. The F1-score of the LightGBM model is higher than the LR and RF models. The accuracy of the model of LightGBM, RF, and LR is 88.8%, 84.8%, and 82.6%, respectively. The area under the curve (AUC) of them is 0.935, 0.918, and 0.868, respectively. The introduced ensemble learning method shows better ability on performance evaluation metrics.

Cloud and snow recognition technology is of great significance in the field of meteorology, and is also widely used in remote sensing mapping, aerospace, and other fields. Based on the traditional method of manually labeling cloud-snow areas, a method of labeling cloud and snow areas using deep learning technology has been gradually developed to improve the accuracy and efficiency of recognition. In this paper, from the perspective of designing an efficient and lightweight network model, a cloud snow recognition model based on a lightweight feature map attention network (Lw-fmaNet) is proposed to ensure the performance and accuracy of the cloud snow recognition model. The model is improved based on the ResNet18 network with the premise of reducing the network parameters and improving the training efficiency. The main structure of the model includes a shallow feature extraction module, an intrinsic feature mapping module, and a lightweight adaptive attention mechanism. Overall, in the experiments conducted in this paper, the accuracy of the proposed cloud and snow recognition model reaches 95.02%, with a Kappa index of 93.34%. The proposed method achieves an average precision rate of 94.87%, an average recall rate of 94.79%, and an average F1-Score of 94.82% for four sample recognition classification tasks: no snow and no clouds, thin cloud, thick cloud, and snow cover. Meanwhile, our proposed network has only 5.617M parameters and takes only 2.276 s. Compared with multiple convolutional neural networks and lightweight networks commonly used for cloud and snow recognition, our proposed lightweight feature map attention network has a better performance when it comes to performing cloud and snow recognition tasks.

Water area segmentation is an important branch of remote sensing image segmentation, but in reality, most water area images have complex and diverse backgrounds. Traditional detection methods cannot accurately identify small tributaries due to incomplete mining and insufficient utilization of semantic information, and the edge information of segmentation is rough. To solve the above problems, we propose a multi-scale feature aggregation network. In order to improve the ability of the network to process boundary information, we design a deep feature extraction module using a multi-scale pyramid to extract features, combined with the designed attention mechanism and strip convolution, extraction of multi-scale deep semantic information and enhancement of spatial and location information. Then, the multi-branch aggregation module is used to interact with different scale features to enhance the positioning information of the pixels. Finally, the two high-performance branches designed in the Feature Fusion Upsample module are used to deeply extract the semantic information of the image, and the deep information is fused with the shallow information generated by the multi-branch module to improve the ability of the network. Global and local features are used to determine the location distribution of each image category. The experimental results show that the accuracy of the segmentation method in this paper is better than that in the previous detection methods, and has important practical significance for the actual water area segmentation.

We report on the first passively mode-locked femtosecond-laser operation of a disordered Yb:Ca3Gd2(BO3)4 crystal using a SEmiconductor Saturable Absorber Mirror (SESAM). Pumping with a single-transverse mode fiber-coupled laser diode at 976 nm, nearly Fourier-transform-limited pulses as short as 96 fs are generated at 1045 nm with an average output power of 205 mW and a pulse repetition rate of ~67.3 MHz. In the continuous-wave regime, high slope efficiency up to 59.2% and low laser thresholds down to 25 mW are obtained. Continuous wavelength tuning between 1006–1074 nm (a tuning range of 68 nm) is demonstrated. Yb:Ca3Gd2(BO3)4 crystals are promising for the development of ultrafast lasers at ~1 μm.

Fire risk prediction is significant for fire prevention and fire resource allocation. Fire risk maps are effective methods for quantifying regional fire risk. Laoshan National Forest Park has many precious natural resources and tourist attractions, but there is no fire risk assessment model. This paper aims to construct the forest fire risk map for Nanjing Laoshan National Forest Park. The forest fire risk model is constructed by factors (altitude, aspect, topographic wetness index, slope, distance to roads and populated areas, normalized difference vegetation index, and temperature) which have a great influence on the probability of inducing fire in Laoshan. Since the importance of factors in different study areas is inconsistent, it is necessary to calculate the significance of each factor of Laoshan. After the significance calculation is completed, the fire risk model of Laoshan can be obtained. Then, the fire risk map can be plotted based on the model. This fire risk map can clarify the fire risk level of each part of the study area, with 16.97% extremely low risk, 48.32% low risk, 17.35% moderate risk, 12.74% high risk and 4.62% extremely high risk, and it is compared with the data of MODIS fire anomaly point. The result shows that the accuracy of the risk map is 76.65%.

Desert segmentation of remote sensing images is the basis of analysis of desert area. Desert images are usually characterized by large image size, large-scale change, and irregular location distribution of surface objects. The multi-scale fusion method is widely used in the existing deep learning segmentation models to solve the above problems. Based on the idea of multi-scale feature extraction, this paper took the segmentation results of each scale as an independent optimization task and proposed a multi-resolution supervision network (MrsSeg) to further improve the desert segmentation result. Due to the different optimization difficulty of each branch task, we also proposed an auxiliary adaptive weighted loss function (AWL) to automatically optimize the training process. MrsSeg first used a lightweight backbone to extract different-resolution features, then adopted a multi-resolution fusion module to fuse the local information and global information, and finally, a multi-level fusion decoder was used to aggregate and merge the features at different levels to get the desert segmentation result. In this method, each branch loss was treated as an independent task, AWL was proposed to calculate and adjust the weight of each branch. By giving priority to the easy tasks, the improved loss function could effectively improve the convergence speed of the model and the desert segmentation result. The experimental results showed that MrsSeg-AWL effectively improved the learning ability of the model and has faster convergence speed, lower parameter complexity, and more accurate segmentation results.

Building and road extraction from remote sensing images is of great significance to urban planning. At present, most of building and road extraction models adopt deep learning semantic segmentation method. However, the existing semantic segmentation methods did not pay enough attention to the feature information between hidden layers, which led to the neglect of the category of context pixels in pixel classification, resulting in these two problems of large-scale misjudgment of buildings and disconnection of road extraction. In order to solve these problem, this paper proposes a Non-Local Feature Search Network (NFSNet) that can improve the segmentation accuracy of remote sensing images of buildings and roads, and to help achieve accurate urban planning. By strengthening the exploration of hidden layer feature information, it can effectively reduce the large area misclassification of buildings and road disconnection in the process of segmentation. Firstly, a Self-Attention Feature Transfer (SAFT) module is proposed, which searches the importance of hidden layer on channel dimension, it can obtain the correlation between channels. Secondly, the Global Feature Refinement (GFR) module is introduced to integrate the features extracted from the backbone network and SAFT module, it enhances the semantic information of the feature map and obtains more detailed segmentation output. The comparative experiments demonstrate that the proposed method outperforms state-of-the-art methods, and the model complexity is the lowest.

Wildfire is a sudden and hazardous natural disaster. Currently, many schemes based on optical spectrum analysis have been proposed to detect wildfire, but obstacles in forest areas can decrease the efficiency of spectral monitoring, resulting in a wildfire detection system not being able to monitor the occurrence of wildfire promptly. In this paper, we propose a novel wildfire detection system using sound spectrum analysis based on the Internet of Things (IoT), which utilizes a wireless acoustic detection system to probe wildfire and distinguish the difference in the sound between the crown and the surface fire. We also designed a new power supply unit: tree-energy device, which utilizes the biological energy of the living trees to generate electricity. We implemented sound spectrum analysis on the data collected by sound sensors and then combined our classification algorithms. The results describe that the sound frequency of the crown fire is about 0−400 Hz, while the sound frequency of the surface fire ranges from 0 to 15,000 Hz. However, the accuracy of the classification method is affected by some factors, such as the distribution of sensors, the loss of energy in sound transmission, and the delay of data transmission. In the simulation experiments, the recognition rate of the method can reach about 70%.