Local-global feature fusion network for hyperspectral image classification DOI
Yuquan Gan, Hao Zhang, Weihua Liu

и другие.

International Journal of Remote Sensing, Год журнала: 2024, Номер unknown, С. 1 - 28

Опубликована: Окт. 4, 2024

Язык: Английский

DHHNN: A Dynamic Hypergraph Hyperbolic Neural Network based on variational autoencoder for multimodal data integration and node classification DOI

Zhangyu Mei,

Xiao Bi,

Dianguo Li

и другие.

Information Fusion, Год журнала: 2025, Номер unknown, С. 103016 - 103016

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1
Deep learning model for drought prediction based on large-scale spatial causal network in the Yangtze River Basin DOI

Huihui Dai,

Lihua Xiong, Qiumei Ma

и другие.

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 132808 - 132808

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1
A survey of unmanned aerial vehicles and deep learning in precision agriculture DOI
Dashuai Wang,

Minghu Zhao,

Zhuolin Li

и другие.

European Journal of Agronomy, Год журнала: 2024, Номер 164, С. 127477 - 127477

Опубликована: Дек. 17, 2024

Язык: Английский

Процитировано

5
Fascore: Adaptive Feature Augmentation with Spatial-Geometrical Coordination for Efficient Robustness Representation DOI
Pin Liu, Bin Shi, Rui Wang

и другие.

Опубликована: Янв. 1, 2025

Download This Paper Open PDF in Browser Add to My Library Share: Permalink Using these links will ensure access this page indefinitely Copy URL DOI

Язык: Английский

Процитировано

0
Invariant semantic domain generalization shuffle network for cross-scene hyperspectral image classification DOI
Jingpeng Gao, Xiangyu Ji, Fang Ye

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126818 - 126818

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Antarctic Sea Ice Extraction for Remote Sensing Images via Modified U-Net Based on Feature Enhancement Driven by Graph Convolution Network DOI Creative Commons
Feng Wu, Xiulin Geng, Xiaoyu He

и другие.

Journal of Marine Science and Engineering, Год журнала: 2025, Номер 13(3), С. 439 - 439

Опубликована: Фев. 25, 2025

Antarctic true-color imagery synthesized using multispectral remote sensing data is effective in reflecting sea ice conditions, which crucial for monitoring. Deep learning has been explored extraction, but traditional convolutional neural network models are constrained by a limited perceptual field, making it difficult to obtain global contextual information from images. A novel model named GEFU-Net, modification of U-Net, presented. The self-established graph reconstruction module employed convert features into and construct the adjacency matrix adaptive average similarity threshold. Graph networks utilized aggregate at each pixel, enabling rapid capture context, enhancing semantic richness features, improving accuracy extraction through reconstruction. Experimental results dataset Ross Sea Antarctic, produced Sentinel-2, demonstrate that our GEFU-Net achieves best performance compared other commonly used segmentation models. Specifically, an 97.52%, Intersection over Union 95.66%, F1-Score 97.78%. Additionally, fewer parameters good inference speed demonstrated, indicating strong potential practical mapping applications.

Язык: Английский

Процитировано

0
Geospatial mapping of large-scale electric power grids: A graph convolutional network-based approach with attention mechanism DOI Creative Commons
Razzaqul Ahshan, Md. Shadman Abid, Mohammed Al‐Abri

и другие.

Energy and AI, Год журнала: 2025, Номер unknown, С. 100486 - 100486

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Research Advances on Identification and Detection Methods for Aflatoxigenic Fungi DOI Creative Commons
Qingyu Shang, Fa Zhang, Qi Zhang

и другие.

Food Frontiers, Год журнала: 2025, Номер unknown

Опубликована: Март 3, 2025

ABSTRACT Aflatoxin contamination in food and feed is a global concern for safety, posing great threat to human environmental health. It the secondary metabolite mainly produced by Aspergillus flavus ( A. ) parasiticus (A. ). Therefore, it very urgent develop rapid sensitive identification detection methods aflatoxigenic fungi realize early warning risk of aflatoxin from source. This article reviews latest research progress strategy identifying detecting agricultural recent years. The principles applications different techniques determination, including morphological identification, nucleic acid amplification techniques, spectral analysis technology, biosensing, are presented this review. Finally, challenges trends future also discussed. Through fungi, there will be relatively sufficient time do corresponding prevention control measures reduce or even prevent production stage, which beneficial greatly improve safety.

Язык: Английский

Процитировано

0
Pioneering precision in lumbar spine MRI segmentation with advanced deep learning and data enhancement DOI Creative Commons

Iman Ahmed,

Md. Tanzim Hossain,

Md. Zahirul Islam Nahid

и другие.

Machine Learning with Applications, Год журнала: 2025, Номер unknown, С. 100635 - 100635

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
A review of hyperspectral image classification based on graph neural networks DOI Creative Commons
Xiaofeng Zhao,

Junyi Ma,

Lei Wang

и другие.

Artificial Intelligence Review, Год журнала: 2025, Номер 58(6)

Опубликована: Март 17, 2025

Язык: Английский

Процитировано

0