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Xiaofeng Zhao
Publications - 14
Citations - 378
Xiaofeng Zhao is an academic researcher. The author has contributed to research in topics: Computer science & Pattern recognition (psychology). The author has an hindex of 2, co-authored 6 publications receiving 139 citations.
Papers
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Modeling and analyzing interference signal in a complex electromagnetic environment
TL;DR: It is a must to study electronic jamming technology in the complex electromagnetic environment, which will play an indispensable role in the construction of information team and victory in the countermeasure of information technology.
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Multi-feature fusion: Graph neural network and CNN combining for hyperspectral image classification
TL;DR: Wang et al. as mentioned in this paper proposed a multi-scale superpixel fusion network (MFGCN), where two different convolutional networks are utilized in two branches, separately, for hyperspectral image (HSI) classification.
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AF2GNN: Graph convolution with adaptive filters and aggregator fusion for hyperspectral image classification
TL;DR: In this paper , a graph convolution with adaptive filters and aggregator fusion (AF2GNN) is developed for hyperspectral image classification, which can deal with the problems of land cover discrimination, noise impaction, and spatial feature learning.
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Self-Supervised Locality Preserving Low-Pass Graph Convolutional Embedding for Large-Scale Hyperspectral Image Clustering
TL;DR: A spectral–spatial transformation HSI preprocessing mechanism is introduced to learn superpixel-level spectral-spatial features from HSI and reduce the number of graph nodes for subsequent network processing, and a locality preserving low-pass graph convolutional embedding autoencoder is proposed.
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Multireceptive field: An adaptive path aggregation graph neural framework for hyperspectral image classification
TL;DR: Wang et al. as discussed by the authors proposed a multi-adaptive receptive field-based graph neural framework (MARP) for hyperspectral image classification, where a graph attention (GAT) neural network is introduced to learn the importance of different-sized neighbourhoods and a long short-term memory (LSTM) method is adopted to update the nodes and preserve the local convolutional features of the nodes.