Optics and Precision Engineering, Volume. 30, Issue 15, 1889(2022)
Semi-supervised dual path network for hyperspectral image classification
[1] DING Y, ZHAO X F, ZHANG Z L et al. Multiscale graph sample and aggregate network with context-aware learning for hyperspectral image classification[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 4561-4572(2021).
[2] [2] 2郑铁, 薛长斌, 宋金伟. 利用格型递归最小二乘滤波器组的高光谱图像压缩[J]. 光学 精密工程, 2021, 29(4): 896-905. doi: 10.37188/OPE.20212904.0896ZHENGT, XUEC B, SONGJ W. Lossless compression of hyperspectral images using recursive least square lattice filter group[J]. Optics and Precision Engineering, 2021, 29(4): 896-905.(in Chinese). doi: 10.37188/OPE.20212904.0896
[3] [3] 3叶珍, 白璘, 何明一. 高光谱图像空谱特征提取综述[J]. 中国图象图形学报, 2021, 26(8): 1737-1763. doi: 10.11834/jig.210198YEZH, BAIL, HEM Y. Review of spatial-spectral feature extraction for hyperspectral image[J]. Journal of Image and Graphics, 2021, 26(8): 1737-1763.(in Chinese). doi: 10.11834/jig.210198
[4] MU C H, ZENG Q Z, LIU Y et al. A two-branch network combined with robust principal component analysis for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 18, 2147-2151(2021).
[5] SAMAT A, GAMBA P, LIU S C et al. Jointly informative and manifold structure representative sampling based active learning for remote sensing image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 6803-6817(2016).
[6] WANG H, FAN Y Y, FANG B F et al. Generalized linear discriminant analysis based on euclidean norm for gait recognition[J]. International Journal of Machine Learning and Cybernetics, 9, 569-576(2018).
[7] XU D, YAN S C, TAO D C et al. Marginal Fisher analysis and its variants for human gait recognition and content- based image retrieval[J]. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society, 16, 2811-2821(2007).
[8] LUO F L, HUANG H, DUAN Y L et al. Local geometric structure feature for dimensionality reduction of hyperspectral imagery[J]. Remote Sensing, 9, 6197-6211(2017).
[9] [9] 9谭琨, 王雪, 杜培军. 结合深度学习和半监督学习的遥感影像分类进展[J]. 中国图象图形学报, 2019, 24(11): 1823-1841. doi: 10.11834/jig.190348TANK, WANGX, DUP J. Research progress of the remote sensing classification combining deep learning and semi-supervised learning[J]. Journal of Image and Graphics, 2019, 24(11): 1823-1841.(in Chinese). doi: 10.11834/jig.190348
[10] CAI D, HE X F, HAN J W. Semi-supervised discriminant analysis[C], 14, 222-228(2007).
[11] LIAO W Z, PIŽURICA A, SCHEUNDERS P et al. Semisupervised local discriminant analysis for feature extraction in hyperspectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 51, 184-198(2013).
[12] YANG S Y, JIN P L, LI B et al. Semisupervised dual-geometric subspace projection for dimensionality reduction of hyperspectral image data[J]. IEEE Transactions on Geoscience and Remote Sensing, 52, 3587-3593(2014).
[13] LUO F L, HUANG H, MA Z Z et al. Semisupervised sparse manifold discriminative analysis for feature extraction of hyperspectral images[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 6197-6211(2016).
[14] XUE Z H, DU P J, LI J et al. Simultaneous sparse graph embedding for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 53, 6114-6133(2015).
[15] [15] 15闫敬文, 陈宏达, 刘蕾. 高光谱图像分类的研究进展[J]. 光学 精密工程, 2019, 27(3): 680-693. doi: 10.3788/ope.20192703.0680YANJ W, CHENH D, LIUL. Overview of hyperspectral image classification[J]. Optics and Precision Engineering, 2019, 27(3): 680-693.(in Chinese). doi: 10.3788/ope.20192703.0680
[16] LI Y, ZHANG H K, SHEN Q. Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network[J]. Remote Sensing, 9, 67(2017).
[17] LI Z Y, HUANG H, DUAN Y L et al. DLPNet: a deep manifold network for feature extraction of hyperspectral imagery[J]. Neural Networks, 129, 7-18(2020).
[18] [18] 18黄鸿, 张臻, 李政英. 面向高光谱影像分类的深度流形重构置信网络[J]. 光学 精密工程, 2021, 29(8): 1985-1998. doi: 10.37188/OPE.20212908.1985HUANGH, ZHANGZH, LIZH Y. Deep manifold reconstruction belief network for hyperspectral remote sensing image classification[J]. Optics and Precision Engineering, 2021, 29(8): 1985-1998.(in Chinese). doi: 10.37188/OPE.20212908.1985
[19] [19] 19李丹, 孔繁锵, 朱德燕. 基于局部高斯混合特征提取的高光谱图像分类[J]. 光学学报, 2021, 41(6): 0610001. doi: 10.3788/aos202141.0610001LID, KONGF Q, ZHUD Y. Hyperspectral image classification based on local Gaussian mixture feature extraction[J]. Acta Optica Sinica, 2021, 41(6): 0610001.(in Chinese). doi: 10.3788/aos202141.0610001
[20] [20] 20肖青,闻建光.黑河生态水文遥感试验: 热红外高光谱航空遥感(2012年7月4日)[Z].黑河计划数据管理中心, 2013. doi: 10.3972/hiwater.006. 2013. db. doi: 10.1088/1475-7516/2013/10/006XIAO Q, WEN J G. HiWATER: Thermal-infrared hyperspectal radiometer(4th,July,2012) [Z].Heihe Plan Science Data Center, 2013. doi: 10.3972/hiwater.006. 2013. db. (in Chinese). doi: 10.1088/1475-7516/2013/10/006
[21] ZHONG Y F, HU X, LUO C et al. WHU-Hi: UAV-borne hyperspectral with high spatial resolution (H2) benchmark datasets and classifier for precise crop identification based on deep convolutional neural network with CRF[J]. Remote Sensing of Environment, 250, 112012(2020).
Get Citation
Copy Citation Text
Hong HUANG, Zhen ZHANG, Ling JI, Zhengying LI. Semi-supervised dual path network for hyperspectral image classification[J]. Optics and Precision Engineering, 2022, 30(15): 1889
Category: Information Sciences
Received: May. 7, 2022
Accepted: --
Published Online: Sep. 7, 2022
The Author Email: Hong HUANG (hhuang@cqu.edu.cn)