Single-scale Feature Research Articles

A Multi-Scale Convolutional Neural Network with Self-Knowledge Distillation for Bearing Fault Diagnosis

Efficient bearing fault diagnosis not only extends the operational lifespan of rolling bearings but also reduces unnecessary maintenance and resource waste. However, current deep learning-based methods face significant challenges, particularly due to the scarcity of fault data, which impedes the models’ ability to effectively learn parameters. Additionally, many existing methods rely on single-scale features, hindering the capture of global contextual information and diminishing diagnostic accuracy. To address these challenges, this paper proposes a Multi-Scale Convolutional Neural Network with Self-Knowledge Distillation (MSCNN-SKD) for bearing fault diagnosis. The MSCNN-SKD employs a five-stage architecture. Stage 1 uses wide-kernel convolution for initial feature extraction, while Stages 2 through 5 integrate a parallel multi-scale convolutional structure to capture both global contextual information and long-range dependencies. In the final two stages, a self-distillation process enhances learning by allowing deep-layer features to guide shallow-layer learning, improving performance, especially in data-limited scenarios. Extensive experiments on multiple datasets validate the model’s high diagnostic accuracy, computational efficiency, and robustness, demonstrating its suitability for real-time industrial applications in resource-limited environments.

DMANet: Dual-branch multiscale attention network for real-time semantic segmentation

The dual-branch structure has made impressive achievements in the field of semantic segmentation. However, the size of feature map tends to be kept constant in the single branch of dual-branch network, which leads to the lack of multiscale feature information. Previous self-attention mechanisms were often limited to processing singlescale feature maps, which restricted the ability of multiscale feature extraction. To alleviate these two problems, in this paper we propose a dual-branch multiscale attention network (DMANet) for real-time semantic segmentation. In order to make full use of the multiscale feature information, our DMANet utilizes the multiscale external attention block (EAB) to handle feature maps at different scales. In addition, our proposed dual-branch cascade attention (DCA) module can capture more local information in some sense and enable information interaction across branches. Our DCA module is built and based on the intra-branch cascade fusion (IBCF) structure. Extensive experiments demonstrate the effectiveness of our proposed DMANet. On Cityscapes and CamVid datasets, DMANet achieves 78.5% and 78.7% mIoU respectively and the inference speed reaches the real-time requirement.

Fault diagnosis method of rotating machinery based on MSResNet feature fusion and CAM

To solve the problem of noise interference, it is difficult to extract multi-scale information from complex vibration signals in fault diagnosis with the single-scale convolution kernel of classical deep learning model convolutional neural network (CNN). Therefore, a fault diagnosis method of rotating machinery based on MSResNet feature fusion and CAM is proposed. The residual network (ResNet) and multi-scale convolutional neural network (MSCNN) are combined to extract multi-scale feature information according to convolution kernels with different sizes, so as to avoid the loss of single-scale feature extraction. Make full use of the advantages of the residual network to skip the connection and prevent the feature information extracted by the multi-scale convolution kernel from being lost when the convolution layer propagates forward. In addition, in order to avoid the interference of invalid features after multi-scale information feature fusion, a channel attention mechanism module (CAM) is introduced to screen important features adaptively. The effectiveness of MSResNet-CAM is verified by the bearing data set of Western Reserve University (CWRU) and the data set of QPZZ-II gearbox, and the anti-noise ability is verified by adding noise to the two data sets. The experimental results show that MSResNet-CAM has the characteristics of high fault classification accuracy, good robustness and strong anti-noise ability.

Local fusion generative adversarial network with dual-discriminator and parallel multipath and its application in machinery fault diagnosis with imbalanced data

Abstract Diagnosing faults in critical machinery components is imperative for effective condition monitoring and real-world datasets often suffer from data imbalance. To address this issue, numerous data generation methods have been developed, such as improved local fusion generative adversarial network (ILoFGAN), variational autoencoding GAN (VAEGAN), etc. However, the existing data generation methods primarily concentrate on global and single-scale features and often ignore local or multi-scale features, which leads to the omission of key features or nuances in the generated data. Therefore, a novel approach called the Local Fusion Generative Adversarial Network with Dual-discriminator and Parallel multipath (LoFGAN-DP) is designed to enhance the fault diagnosis performance in the context of imbalanced data. The LoFGAN-DP features a Parallel Multi-Path (PMP) module along with a dual-discriminator scheme, in which the multipath module facilitates feature extraction at various scales through convolution across paths of diverse sizes, and the dual-discriminator scheme can better improve the quality and diversity of the samples generated by the generator. The PMP module and dual-discriminator scheme enhance the proposed method's robustness against variations in input data. After generating data by LoFGAN-DP, a two-dimensional capsule network is further used to achieve the efficient recognition of fault features. To validate the proposed LoFGAN-DP in the machinery fault diagnosis with imbalanced data, the gear dataset and the self-constructed bearing dataset were utilized. Experimental results show that LoFGAN-DP significantly improves Structural Similarity Index (SSIM), Fréchet Inception Distance (FID), and fault classification accuracy compared to several advanced methods.

Multi-scale feature extraction and fusion with attention interaction for RGB-T tracking

RGB-T single-object tracking aims to track objects utilizing both RGB images and thermal infrared(TIR) images. Though the siamese-based RGB-T tracker shows its advantage in tracking speed, its accuracy still cannot be compared with other state-of-the-art trackers (e.g., MDNet). In this study, we revisit the existing siamese-based RGB-T tracker and find that such fall behind comes from insufficient feature fusion between RGB image and TIR image, as well as incomplete interactions between template frame and search frame. Inspired by this, we propose a multi-scale feature extraction and fusion network with Temporal-Spatial Memory (MFATrack). Instead of fusing RGB image and TIR image with the single-scale feature map or only high-level features from the multi-scale feature map, MFATrack proposes a new fusion strategy by fusing features from all scales, which can capture contextual information in shallow layers and details in the deep layer. To learn the feature better for tracking tasks, MFATrack fuses the features via several consecutive frames. In addition, we also propose a self-attention interaction module specifically designed for the search frame, highlighting the features in the search frame that are relevant to the target and thus facilitating rapid convergence for target localization. Experimental results demonstrate the proposed MFATrack is not only fast, but also can obtain better tracking accuracy compared with other competing methods including MDNet-based methods and other siamese-based trackers.

Gearbox Fault Diagnosis Based on MSCNN-LSTM-CBAM-SE.

Ensuring the safety of mechanical equipment, gearbox fault diagnosis is crucial for the stable operation of the whole system. However, existing diagnostic methods still have limitations, such as the analysis of single-scale features and insufficient recognition of global temporal dependencies. To address these issues, this article proposes a new method for gearbox fault diagnosis based on MSCNN-LSTM-CBAM-SE. The output of the CBAM-SE module is deeply integrated with the multi-scale features from MSCNN and the temporal features from LSTM, constructing a comprehensive feature representation that provides richer and more precise information for fault diagnosis. The effectiveness of this method has been validated with two sets of gearbox datasets and through ablation studies on this model. Experimental results show that the proposed model achieves excellent performance in terms of accuracy and F1 score, among other metrics. Finally, a comparison with other relevant fault diagnosis methods further verifies the advantages of the proposed model. This research offers a new solution for accurate fault diagnosis of gearboxes.

GLGFormer: Global Local Guidance Network for Mucosal Lesion Segmentation in Gastrointestinal Endoscopy Images.

Automatic mucosal lesion segmentation is a critical component in computer-aided clinical support systems for endoscopic image analysis. Image segmentation networks currently rely mainly on convolutional neural networks (CNNs) and Transformers, which have demonstrated strong performance in various applications. However, they cannot cope with blurred lesion boundaries and lesions of different scales in gastrointestinal endoscopy images. To address these challenges, we propose a new Transformer-based network, named GLGFormer, for the task of mucosal lesion segmentation. Specifically, we design the global guidance module to guide single-scale features patch-wise, enabling them to incorporate global information from the global map without information loss. Furthermore, a partial decoder is employed to fuse these enhanced single-scale features, achieving single-scale to multi-scale enhancement. Additionally, the local guidance module is designed to refocus attention on the neighboring patch, thus enhancing local features and refining lesion boundary segmentation. We conduct experiments on a private atrophic gastritis segmentation dataset and four public gastrointestinal polyp segmentation datasets. Compared to the current lesion segmentation networks, our proposed GLGFormer demonstrates outstanding learning and generalization capabilities. On the public dataset ClinicDB, GLGFormer achieved a mean intersection over union (mIoU) of 91.0% and a mean dice coefficient (mDice) of 95.0%. On the private dataset Gastritis-Seg, GLGFormer achieved an mIoU of 90.6% and an mDice of 94.6%.

FSH-DETR: An Efficient End-to-End Fire Smoke and Human Detection Based on a Deformable DEtection TRansformer (DETR).

Fire is a significant security threat that can lead to casualties, property damage, and environmental damage. Despite the availability of object-detection algorithms, challenges persist in detecting fires, smoke, and humans. These challenges include poor performance in detecting small fires and smoke, as well as a high computational cost, which limits deployments. In this paper, we propose an end-to-end object detector for fire, smoke, and human detection based on Deformable DETR (DEtection TRansformer) called FSH-DETR. To effectively process multi-scale fire and smoke features, we propose a novel Mixed Encoder, which integrates SSFI (Separate Single-scale Feature Interaction Module) and CCFM (CNN-based Cross-scale Feature Fusion Module) for multi-scale fire, smoke, and human feature fusion. Furthermore, we enhance the convergence speed of FSH-DETR by incorporating a bounding box loss function called PIoUv2 (Powerful Intersection of Union), which improves the precision of fire, smoke, and human detection. Extensive experiments on the public dataset demonstrate that the proposed method surpasses state-of-the-art methods in terms of the mAP (mean Average Precision), with mAP and mAP50 reaching 66.7% and 84.2%, respectively.

Joint learning strategy of multi-scale multi-task convolutional neural network for aero-engine prognosis

Remaining useful life (RUL) prediction and health status (HS) assessment are two key tasks in aero-engine prognostics and health management (PHM) system. However, existing deep learning-based prognostic models perform RUL prediction and HS assessment tasks separately, without considering the correlation between these two tasks. Secondly, traditional deep learning can only extract single-scale features, which limits the ability to extract complex degradation features from high-dimensional condition monitoring data. Therefore, this work proposes a multi-scale and multi-task convolutional neural network for joint learning of aero-engine RUL prediction and HS assessment. Firstly, multi-sensor data with multiple cycles are converted into image samples to integrate more condition monitoring information that is beneficial to prognosis. Then, the multi-scale feature fusion block is designed as the shared network for multi-task, utilizing convolutional layers with filters of different sizes to enhance the ability to extract complex degradation features from high-dimensional condition monitoring data. And a multi-layer concatenation block is constructed to integrate multi-scale features at different levels to fully utilize the important information at different levels. On this basis, a multi-task joint learning block is constructed and a joint loss function is developed for joint learning of RUL prediction and HS assessment. Finally, experiments on two engine degradation datasets, CMAPSS and N-CMAPSS, demonstrate that the proposed network has excellent RUL prediction and HS assessment performance, and outperforms other state-of-the-art methods.

An Improved Lightweight Deep Learning Model and Implementation for Track Fastener Defect Detection with Unmanned Aerial Vehicles

Track fastener defect detection is an essential component in ensuring railway safety operations. Traditional manual inspection methods no longer meet the requirements of modern railways. The use of deep learning image processing techniques for classifying and recognizing abnormal fasteners is faster, more accurate, and more intelligent. With the widespread use of unmanned aerial vehicles (UAVs), conducting railway inspections using lightweight, low-power devices carried by UAVs has become a future trend. In this paper, we address the characteristics of track fastener detection tasks by improving the YOLOv4-tiny object detection model. We improved the model to output single-scale features and used the K-means++ algorithm to cluster the dataset, obtaining anchor boxes that were better suited to the dataset. Finally, we developed the FPGA platform and deployed the transformed model on this platform. The experimental results demonstrated that the improved model achieved an mAP of 95.1% and a speed of 295.9 FPS on the FPGA, surpassing the performance of existing object detection models. Moreover, the lightweight and low-powered FPGA platform meets the requirements for UAV deployment.

Granular3D: Delving into multi-granularity 3D scene graph prediction

This paper addresses the significant challenges in 3D Semantic Scene Graph (3DSSG) prediction, essential for understanding complex 3D environments. Traditional approaches, primarily using PointNet and Graph Convolutional Networks, struggle with effectively extracting multi-grained features from intricate 3D scenes, largely due to a focus on global scene processing and single-scale feature extraction. To overcome these limitations, we introduce Granular3D, a novel approach that shifts the focus towards multi-granularity analysis by predicting relation triplets from specific sub-scenes. One key is the Adaptive Instance Enveloping Method (AIEM), which establishes an approximate envelope structure around irregular instances, providing shape-adaptive local point cloud sampling, thereby comprehensively covering the contextual environments of instances. Moreover, Granular3D incorporates a Hierarchical Dual-Stage Network (HDSN), which differentiates and processes features of instances and their pairs at varying scales, leading to a targeted prediction of instance categories and their relationships. To advance the perception of sub-scene in HDSN, we design a Gather Point Transformer structure (GaPT) that enables the combinatorial interaction of local information from multiple point cloud sets, achieving a more comprehensive local contextual feature extraction. Extensive evaluations on the challenging 3DSSG benchmark demonstrate that our methods provide substantial improvements, establishing a new state-of-the-art in 3DSSG prediction, boosting the top-50 triplet accuracy by ＋2.8%.

Multi-scale recurrent attention gated fusion network for single image dehazing

The purpose of single image dehazing is to eliminate the bad influence of haze on images, so as to maintain more scene information of images. In recent years, the convolutional neural networks (CNN) have made significant contributions to single image dehazing. However, the visual quality of dehazed images still needs to be further improved. In view of the problems of single-scale shallow image feature extraction and the insufficient use of intermediate layer features in existing dehazing networks, we propose an end-to-end Multi-scale Recurrent Attention Gated Fusion Network (MRAGFN) to address the image dehazing task. We cascade three Dual Attention Fusion (DAF) modules to progressively form three haze-relevant features map, meanwhile, we adopt downsampling operation on the input to produce global feature map, which are used to weight the three feature maps to compensate for the missing of single-scale feature information. We present Feature Enhancement Module (FEM) to enhance the feature representation ability of these weighted feature maps. We design Recurrent Attention Gated Fusion (RAGF) module by adding attention mechanism and gating mechanism to gradually obtain more refined features based on these weighted features while eliminating redundant features. Experimental results on different hazy images demonstrate that the proposed dehazing network can restore the haze-free images and perform better than the state-of-the-art dehazing networks in terms of the objective indicators (such as PSNR, SSIM)and the subjective visual quality.

Interpretable medical deep framework by logits-constraint attention guiding graph-based multi-scale fusion for Alzheimer’s disease analysis

Deep learning using structural MRI has been widely applied to early diagnosis study of Alzheimer’s disease. Among existing methods, attention-based 3D subject-level methods can not only provide diagnosis results but also interpret the significant brain regions, thereby attracting considerable attention. However, the performance of previous attention-based methods might be still restricted by: (i) the gap between attention scores and semantic significant regions; (ii) using only single-scale features or simply fusing multi-scale information by addition or concatenation for classification decision-making. To overcome these two issues, we propose an innovative dual-branch model called LA-GMF, which consists of two major modules: logits-constraint attention (LA) and graph-based multi-scale fusion (GMF). The LA module is designed to guide the model to focus on key areas to enhance the diagnostic performance of local lesions, by reducing the inconsistency between attention scores and class prediction probabilities. Meanwhile, by combining the graph neural network and the self-attention mechanism, the GMF module not only introduces the interaction between patches, but also explores the correlation and complementarity between features at different scales, thereby extracting feature representations more comprehensively. Experiments on the popular ADNI and AIBL datasets validate the potential of our model in boosting early AD diagnosis accuracy. Additionally, our interpretation experiments demonstrate the superior interpretability performance of the proposed method over recent state-of-the-art attention-based methods. Our source codes are released at: https://github.com/nollexu/LA-GMF.

A Satellite-Drone Image Cross-View Geolocalization Method Based on Multi-Scale Information and Dual-Channel Attention Mechanism

Satellite-Drone Image Cross-View Geolocalization has wide applications. Due to the pronounced variations in the visual features of 3D objects under different angles, Satellite-Drone cross-view image geolocalization remains an unresolved challenge. The key to successful cross-view geolocalization lies in extracting crucial spatial structure information across different scales in the image. Recent studies improve image matching accuracy by introducing an attention mechanism to establish global associations among local features. However, existing methods primarily focus on using single-scale features and employ a single-channel attention mechanism to correlate local convolutional features from different locations. This approach inadequately explores and utilizes multi-scale spatial structure information within the image, particularly lacking in the extraction and utilization of locally valuable information. In this paper, we propose a cross-view image geolocalization method based on multi-scale information and a dual-channel attention mechanism. The multi-scale information includes features extracted from different scales using various convolutional slices, and it extensively utilizes shallow network features. The dual-channel attention mechanism, through successive local and global feature associations, effectively learns depth discriminative features across different scales. Experimental results were conducted using existing satellite and drone image datasets, with additional validation performed on an independent self-made dataset. The findings indicate that our approach exhibits superior performance compared to existing methods. The methodology presented in this paper exhibits enhanced capabilities, especially in the exploitation of multi-scale spatial structure information and the extraction of locally valuable information.

A Multi-Information Fusion Algorithm to Fault Diagnosis of Power Converter in Wind Power Generation Systems

Power electronics-based converters are the major and most vulnerable components in wind power generation systems. Converter faults will affect power quality,damage expensive equipment such asgenerators, or even pose a massive threat to the entire power grid. Fault diagnosis is considered as a powerful means to improve system reliability and reduce maintenance costs. Existing data-driven fault diagnosis methods related to improving feature extraction approaches to obtain high diagnostic accuracy are mostly based on single-scale feature of signals; this neglects the potentially valuable information of other scales. This paper proposes an algorithm of Dempster-Shafer and Deng entropy fusion multi-scale approximate entropy (DSDEMAE) for wind power converter fault diagnosis. Firstly, it calculates the multi-scale approximate entropy of fault signals, mining more potentially valuable information. Secondly, Dempster-Shafer is used to fuse features of various scales and effectively handles the conflicts and uncertainties between different features. Finally,Deng entropyis adopted to measure the uncertainty to adjust weight distribution, reducing the influence of highly conflicting features and mutually benefitting features of different scales. Extensive experimental results on simulated and experimental data demonstrate the effectiveness of the proposed algorithm. Compared with advanced methods, this method can diagnose the faults with higher accuracy and stronger robustness.

MFCEN: A lightweight multi-scale feature cooperative enhancement network for single-image super-resolution

<p>In recent years, significant progress has been made in single-image super-resolution with the advancements of deep convolutional neural networks (CNNs) and transformer-based architectures. These two techniques have led the way in the field of super-resolution technology research. However, performance improvements often come at the cost of a substantial increase in the number of parameters, thereby limiting the practical applications of super-resolution methods. Existing lightweight super-resolution methods, which primarily focus on single-scale feature extraction, lead to the issue of missing multi-scale features. This results in incomplete feature acquisition and poor reconstruction of the image. In response to these challenges, this paper proposed a lightweight multi-scale feature cooperative enhancement network (MFCEN). The network consists of three parts: shallow feature extraction, deep feature extraction, and image reconstruction. In the deep feature extraction part, a novel integrated multi-level feature module was introduced. Compared to existing CNN and transformer hybrid super-resolution networks, MFCEN significantly reduced the number of parameters while maintaining performance. This improvement was particularly evident at a scale factor of 3. The network introduced a novel comprehensive integrated multi-level feature module, leveraging the strong local perceptual capabilities of CNNs and the superior global information processing of transformers. It was designed with depthwise separable convolutions for extracting local information and a block-scale and global feature extraction module based on vision transformers (ViTs). While extracting the three scales of features, a satisfiability attention mechanism with a feed-forward network that can control the information was used to keep the network lightweight. Experiments demonstrated that the proposed model surpasses the reconstruction performance of the 498K-parameter SPAN model with a mere 488K parameters. Extensive experiments on commonly used image super-resolution datasets further validated the effectiveness of the network.</p>

MFAM-AD: an anomaly detection model for multivariate time series using attention mechanism to fuse multi-scale features.

Multivariate time series anomaly detection has garnered significant attention in fields such as IT operations, finance, medicine, and industry. However, a key challenge lies in the fact that anomaly patterns often exhibit multi-scale temporal variations, which existing detection models often fail to capture effectively. This limitation significantly impacts detection accuracy. To address this issue, we propose the MFAM-AD model, which combines the strengths of convolutional neural networks (CNNs) and bi-directional long short-term memory (Bi-LSTM). The MFAM-AD model is designed to enhance anomaly detection accuracy by seamlessly integrating temporal dependencies and multi-scale spatial features. Specifically, it utilizes parallel convolutional layers to extract features across different scales, employing an attention mechanism for optimal feature fusion. Additionally, Bi-LSTM is leveraged to capture time-dependent information, reconstruct the time series and enable accurate anomaly detection based on reconstruction errors. In contrast to existing algorithms that struggle with inadequate feature fusion or are confined to single-scale feature analysis, MFAM-AD effectively addresses the unique challenges of multivariate time series anomaly detection. Experimental results on five publicly available datasets demonstrate the superiority of the proposed model. Specifically, on the datasets SMAP, MSL, and SMD1-1, our MFAM-AD model has the second-highest F1 score after the current state-of-the-art DCdetector model. On the datasets NIPS-TS-SWAN and NIPS-TS-GECCO, the F1 scores of MAFM-AD are 0.046 (6.2%) and 0.09 (21.3%) higher than those of DCdetector, respectively(the value ranges from 0 to 1). These findings validate the MFAMAD model's efficacy in multivariate time series anomaly detection, highlighting its potential in various real-world applications.

MFAGNet: multi-scale frequency attention gating network for land cover classification

ABSTRACT As a classic problem of high-resolution remote sensing image tasks, land cover classification has many challenges, for instance, variable scales and low-edge discrimination. Traditional spatial semantic segmentation methods based on single-scale feature extraction are challenging to model multi-scale features. They cannot effectively fit high-resolution remote sensing images with variable scales and are prone to misclassification issues. On the other hand, spatial multi-scale semantic segmentation methods lack edge constraints, which can easily lead to edge segmentation ambiguity issues. We propose a segmentation network based on a multi-scale frequency-domain attention gating mechanism (MFAGNet) to solve the above problems. Specifically, we obtain multi-scale features through multi-scale input and design the discrete cosine transform channel attention module (DCTCAM). In DCTCAM, we extract global low-frequency features and effectively alleviate misclassification caused by scale changes. The edge features of the image are obtained through the high-frequency feature extraction module (HFEM) by using edge detection operators to enhance the model’s ability to recognize edges. Additionally, no further pooling operations are employed except for a single max pooling layer in the backbone network. In this paper, we design a new gating mechanism to dynamically control the extraction ratio of various frequency features so that it can adapt to input images of different scales. This paper conducts comparative experiments on three high-resolution land cover datasets: GID, Vaihingen, and Potsdam. The new algorithm proposed compares with classic semantic segmentation models such as DANet, PSPNet, SegNet, UNet, DeepLabv3+, FcaNet, BiSeNet, AFN, and FastFCN. The experimental results show that MFAGNet is superior to the comparison models. With no significant increase in computing overhead, MFAGNet obtains MIoU of 69.05%, 73.40%, and 79.42% on the standard data sets GID, Vaihingen, and Potsdam, respectively.

GFCNet: Contrastive Learning Network with Geography Feature Space Joint Negative Sample Correction for Land Cover Classification

With the continuous improvement in the volume and spatial resolution of remote sensing images, the self-supervised contrastive learning paradigm driven by a large amount of unlabeled data is expected to be a promising solution for large-scale land cover classification with limited labeled data. However, due to the richness and scale diversity of ground objects contained in remote sensing images, self-supervised contrastive learning encounters two challenges when performing large-scale land cover classification: (1) Self-supervised contrastive learning models treat random spatial–spectral transformations of different images as negative samples, even though they may contain the same ground objects, which leads to serious class confusion in land cover classification. (2) The existing self-supervised contrastive learning models simply use the single-scale features extracted by the feature extractor for land cover classification, which limits the ability of the model to capture different scales of ground objects in remote sensing images. In this study, we propose a contrastive learning network with Geography Feature space joint negative sample Correction (GFCNet) for land cover classification. To address class confusion, we propose a Geography Feature space joint negative sample Correction Strategy (GFCS), which integrates the geography space and feature space relationships of different images to construct negative samples, reducing the risk of negative samples containing the same ground object. In order to improve the ability of the model to capture the features of different scale ground objects, we adopt a Multi-scale Feature joint Fine-tuning Strategy (MFFS) to integrate different scale features obtained by the self-supervised contrastive learning network for land cover classification tasks. We evaluate the proposed GFCNet on three public land cover classification datasets and achieve the best results compared to seven baselines of self-supervised contrastive learning methods. Specifically, on the LoveDA Rural dataset, the proposed GFCNet improves 3.87% in Kappa and 1.54% in mIoU compared with the best baseline.

Multistage residual network for intense distributed acoustic sensing background noise attenuation

Owing to its advantages in acquisition properties, distributed acoustic sensing (DAS) is gradually being applied in seismic exploration. Unfortunately, the acquired DAS records are usually contaminated by various unwanted interferences, which are considered one of the main obstacles for subsequent processing, such as inversion and imaging of the seismic data. In general, conventional signal processing methods cannot satisfy the requirements of DAS noise attenuation owing to the limitations in presumption and denoising accuracy. Therefore, deep learning, especially convolutional neural networks (CNNs), has been used to eliminate intense background noise and improve the quality of DAS records. Nevertheless, most CNN-based methods often rely on single-scale features and cannot guarantee denoising performance when dealing with complex DAS data. In this study, we develop a multistage residual network (MSR-Net) aiming to enhance denoising ability for complex DAS background noise. More precisely, the backbone of MSR-Net adopts a multiscale architecture to capture informative features within the DAS data. A modified ringed residual module is also used to enhance the representation ability of potential features. In addition, a feature aggregation spatial attention module is designed to refine and reinforce the primary features, thereby positively impacting the denoising performance. Meanwhile, an authentic training data set is generated based on field noise data and synthetic records obtained by the forward-modeling method. Compared with conventional methods and typical denoising networks, MSR-Net demonstrates superiority in weak signal recovery and intense DAS noise attenuation.