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Kuiliang Gao
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Publications (4)
Proceedings Article | 10 February 2023 Paper
Transformer-enhanced convolutional neural network with self-supervised learning for hyperspectral image classification
Yifan Sun, Bing Liu, Zhixiang Xue, Kuiliang Gao, Xibing Zuo, Mofan Dai
Proceedings Volume 12552, 1255238 (2023) https://doi.org/10.1117/12.2667329
KEYWORDS: Feature extraction, Education and training, Transformers, Image classification, Hyperspectral imaging, Convolutional neural networks, Support vector machines, Statistical modeling, Image processing
SPIE Journal Paper | 4 February 2022
Multiscale nested U-Net for small sample classification of hyperspectral images
Bing Liu, Anzhu Yu, Kuiliang Gao, Yusong Wang, Xuchu Yu, Pengqiang Zhang
JARS, Vol. 16, Issue 01, 016506, (February 2022) https://doi.org/10.1117/12.10.1117/1.JRS.16.016506
KEYWORDS: Image classification, Hyperspectral imaging, Classification systems, Gaussian filters, Image segmentation, Convolution, Performance modeling, Computer programming, Statistical modeling, Feature extraction
SPIE Journal Paper | 24 September 2021
Deep global-local transformer network combined with extended morphological profiles for hyperspectral image classification
Xiong Tan, Kuiliang Gao, Bing Liu, Yumeng Fu, Lei Kang
JARS, Vol. 15, Issue 03, 038509, (September 2021) https://doi.org/10.1117/12.10.1117/1.JRS.15.038509
KEYWORDS: Transformers, Data modeling, Visual process modeling, Feature extraction, Convolution, Image classification, 3D modeling, Performance modeling, Network architectures, Hyperspectral imaging
SPIE Journal Paper | 1 June 2020
Semisupervised graph convolutional network for hyperspectral image classification
Bing Liu, Kuiliang Gao, Anzhu Yu, Wenyue Guo, Ruirui Wang, Xibing Zuo
JARS, Vol. 14, Issue 02, 026516, (June 2020) https://doi.org/10.1117/12.10.1117/1.JRS.14.026516
KEYWORDS: Image classification, Hyperspectral imaging, Statistical modeling, Convolution, Feature extraction, Data modeling, 3D modeling, Convolutional neural networks, Neural networks, Optical filters
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