STPGTN–A Multi-Branch Parameters Identification Method Considering Spatial Constraints and Transient Measurement Data

Zhang, Shuai; Weng, Liguo

doi:10.32604/cmes.2023.025405

Cited by 6 publications

(6 citation statements)

References 25 publications

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“…Deep learning techniques have advanced rapidly due to the ongoing increase in computer technology [18][19][20][21]. The powerful feature-extraction ability and end-to-end network structure of deep learning methods can be effectively applied to the field of remotesensing image-change detection.…”

Section: Change-detection Methods In Deep Learningmentioning

confidence: 99%

MBCNet: Multi-Branch Collaborative Change-Detection Network Based on Siamese Structure

et al. 2023

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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.

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Section: Change-detection Methods In Deep Learningmentioning

confidence: 99%

MBCNet: Multi-Branch Collaborative Change-Detection Network Based on Siamese Structure

et al. 2023

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“…For a set of transaction sample features u ∈ R C , we first group its feature channels to obtain two sets of sub-features u g ∈ R N×d and u l ∈ R d×N (N represents the number of groups, and d represents the feature dimension of each group). Then, feature computation with self-attention [35,36] is performed on two sets of sub-features separately to fully capture the global and local correlations among feature channels. Self-attention is widely used in the field of Natural Language Processing (NLP), which can effectively capture the dependencies between arbitrary features by computing the spatial distance of pairs of features.…”

Section: Proposed Modelmentioning

confidence: 99%

Credit Card Fraud Detection Based on Unsupervised Attentional Anomaly Detection Network

Jiang

Dong

Wang

et al. 2023

Systems

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In recent years, with the rapid development of Internet technology, the number of credit card users has increased significantly. Subsequently, credit card fraud has caused a large amount of economic losses to individual users and related financial enterprises. At present, traditional machine learning methods (such as SVM, random forest, Markov model, etc.) have been widely studied in credit card fraud detection, but these methods are often have difficulty in demonstrating their effectiveness when faced with unknown attack patterns. In this paper, a new Unsupervised Attentional Anomaly Detection Network-based Credit Card Fraud Detection framework (UAAD-FDNet) is proposed. Among them, fraudulent transactions are regarded as abnormal samples, and autoencoders with Feature Attention and GANs are used to effectively separate them from massive transaction data. Extensive experimental results on Kaggle Credit Card Fraud Detection Dataset and IEEE-CIS Fraud Detection Dataset demonstrate that the proposed method outperforms existing fraud detection methods.

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“…We chose to use a residual network (ResNet) as the encoder since its residual blocks help resolve issues with gradient explosion and disappearance resulting from network deepening [48]. Many researchers improved ResNet with different versions [49][50][51][52]. Merely replacing ResNet with the improved versions in our model framework can improve the experimental results.…”

Section: Network Architecturementioning

confidence: 99%

Multi-Supervised Feature Fusion Attention Network for Clouds and Shadows Detection

Xia

Zhang

et al. 2023

IJGI

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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.

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STPGTN–A Multi-Branch Parameters Identification Method Considering Spatial Constraints and Transient Measurement Data

Cited by 6 publications

References 25 publications

MBCNet: Multi-Branch Collaborative Change-Detection Network Based on Siamese Structure

MBCNet: Multi-Branch Collaborative Change-Detection Network Based on Siamese Structure

Credit Card Fraud Detection Based on Unsupervised Attentional Anomaly Detection Network

Multi-Supervised Feature Fusion Attention Network for Clouds and Shadows Detection

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