L
Leyuan Fang
Researcher at Hunan University
Publications - 152
Citations - 10941
Leyuan Fang is an academic researcher from Hunan University. The author has contributed to research in topics: Hyperspectral imaging & Computer science. The author has an hindex of 42, co-authored 118 publications receiving 6575 citations. Previous affiliations of Leyuan Fang include Duke University.
Papers
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Pixel-level image fusion
TL;DR: It is concluded that although various image fusion methods have been proposed, there still exist several future directions in different image fusion applications and the researches in the image fusion field are still expected to significantly grow in the coming years.
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Deep Learning for Hyperspectral Image Classification: An Overview
TL;DR: In this paper, the authors present a systematic review of deep learning-based hyperspectral image classification literatures and compare several strategies for this topic, which can provide some guidelines for future studies on this topic.
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Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search.
TL;DR: A novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images is presented.
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Hyperspectral Image Classification With Deep Feature Fusion Network
TL;DR: A deep feature fusion network (DFFN) is proposed for HSI classification that fuses the outputs of different hierarchical layers, which can further improve the classification accuracy and outperforms other competitive classifiers.
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Fusing Hyperspectral and Multispectral Images via Coupled Sparse Tensor Factorization
TL;DR: In the proposed CSTF method, an HR-HSI is considered as a 3D tensor and the fusion problem is redefined as the estimation of a core Tensor and dictionaries of the three modes, which demonstrates the superiority of this algorithm over the current state-of-the-art HSI-MSI fusion approaches.