Feature pyramid network based on double filter feature fusion for hyperspectral image classification

Wang, Ge; Guo, Wenhui; Wang, Yanjiang; Wang, Wuli

doi:10.1109/icsp56322.2022.9965277

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…After processing, each point in the software's architecture includes a feature vector comprising labels for key or value, text content, and an edge feature vector that encompasses the relationship between the key-value pair, positional distance, and neighboring node information. In this study, dynamic graph convolution DGCNN (Dynamic Graph Convolutional Neural Network) is utilized 21 . The DGCNN network employs the EdgeConv operation, which has three notable characteristics.…”

Section: Extracting Structured Information From Complex Text Structur...mentioning

confidence: 99%

Research on intelligent customs declaration generation in Guangdong-Hong Kong cross-border road cargo clearance

Han,

Wang

2023

Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023)

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Due to the uniqueness of “one country, two systems” and “two customs territories” in Guangdong (GD) and Hong Kong (HK), cross-border road cargo clearance between the two regions requires the creation of several documents. These include the Road Cargo Manifest, Vehicle Declaration, Customs Declaration, HK Seamless, HK Bundle, and HK Customs Declaration, as well as additional documents such as Packing List, Contract, and more. The use of Artificial intelligence (AI) can streamline the process of generating these documents and ensure accuracy and efficiency in cross-border road cargo clearance operations. This paper focuses on four aspects: text scanning and recognition, extraction of commodity classification declaration elements, conversion of customs declaration documents, and intelligent commodity classification. These areas of research aim to address the time-consuming and labour-intensive process of document creation and verification. By leveraging AI technologies, such as optical character recognition (OCR), data extraction algorithms, and machine learning (ML), it is possible to automate and optimize the production and review of these documents, thereby reducing time and manpower costs in cross-border road cargo clearance operations between GD and HK. Based on big data analysis of customs declaration documents from both regions, AI technologies such as feature extraction and machine learning can be utilized to achieve automation in the identification of documents like road cargo manifests and customs declarations. This research applied to the China International Trade Single Window (Shenzhen) and significantly improved the efficiency and accuracy of customs declaration in GD and HK.

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Section: Extracting Structured Information From Complex Text Structur...mentioning

confidence: 99%

Research on intelligent customs declaration generation in Guangdong-Hong Kong cross-border road cargo clearance

Han,

Wang

2023

Fourth International Conference on Computer Science and Communication Technology (ICCSCT 2023)

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A Feature Embedding Network with Multiscale Attention for Hyperspectral Image Classification

et al. 2023

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In recent years, convolutional neural networks (CNNs) have been widely used in the field of hyperspectral image (HSI) classification and achieved good classification results due to their excellent spectral–spatial feature extraction ability. However, most methods use the deep semantic features at the end of the network for classification, ignoring the spatial details contained in the shallow features. To solve the above problems, this article proposes a hyperspectral image classification method based on a Feature Embedding Network with Multiscale Attention (MAFEN). Firstly, a Multiscale Attention Module (MAM) is designed, which is able to not only learn multiscale information about features at different depths, but also extract effective information from them. Secondly, the deep semantic features can be embedded into the low-level features through the top-down channel, so that the features at all levels have rich semantic information. Finally, an Adaptive Spatial Feature Fusion (ASFF) strategy is introduced to adaptively fuse features from different levels. The experimental results show that the classification accuracies of MAFEN on four HSI datasets are better than those of the compared methods.

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Feature pyramid network based on double filter feature fusion for hyperspectral image classification

Cited by 2 publications

References 23 publications

Research on intelligent customs declaration generation in Guangdong-Hong Kong cross-border road cargo clearance

Research on intelligent customs declaration generation in Guangdong-Hong Kong cross-border road cargo clearance

A Feature Embedding Network with Multiscale Attention for Hyperspectral Image Classification

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