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纸质出版日期： 2022-02-07 ，
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张健，保文星.2022.生成式对抗网络的高光谱遥感图像分类方法研究.遥感学报，26（2）： 416-430
Zhang J and Bao W X. 2022. Research on classification method of hyperspectral remote sensing image based on Generative Adversarial Network. National Remote Sensing Bulletin， 26（2）：416-430
针对基于深度学习的分类模型在训练样本较少时所遭受的潜在过拟合问题，提出一种具备过拟合抑制的生成式对抗网络分类算法，并应用于高光谱图像分类。该算法在每次迭代时，首先，依据训练样本的标签信息使判别器网络拟合训练样本的数据分布；然后对训练样本的高维特征进行均值最小化，该过程会重新更新判别器网络参数，减小参数的值和方差，以抑制过拟合；最后，将本算法应用于针对高光谱图像所设计的光谱空间分类模型进行分类。实验结果表明，在标准数据集Indian Pines和Pavia University中随机选取1%标记样本进行训练，总体分类精度分别达到了89.61%和98.79%，相比于其他现有算法有明显的提高，较表现最好的分类方法，总体分类精度分别提升了5.17%和1.38%。在Indian Pines数据集取1%标记样本，Pavia University数据集取0.1%标记样本的情况下，本文算法对过拟合的抑制效果优于几种常用的过拟合抑制算法，较表现最好的Dropout算法，总体分类精度分别提升了5.60%和3.20%。
Deep learning has strong learning ability and has become a widely studied method in the hyperspectral image classification community. However
the deep learning-based classification model requires a large number of training samples to train a good model. Overfitting will occur when the training sample is small. The accuracy of the model on the test set is lower than the accuracy on the training set. Researchers have proposed overfitting suppression methods such as weight decay and dropout to suppress overfitting. However
these methods need to work in a specific environment and have limited suppression effect on overfitting. Thus
this study proposes an overfitting suppression algorithm based on generative adversarial networks to suppress the overfitting phenomenon of the model.
First
a spatial neighborhood block for the standard dataset is constructed
and the dataset is divided into labeled
unlabeled
and test samples. Then
the labeled and unlabeled samples are sent to the generative adversarial networks for training. During input
the pixels in the neighborhood block are independently fed into the fully connected network discriminator to extract the spectral features of each pixel. Finally
the spectral features of each pixel are fused by the average pooling
and they connected to the output layer to obtain the classification result. The overfitting is caused by the large value and variance of the network parameters. Thus
the large parameter values enable the model to fit more samples. Therefore
the network is first fitted to the data by labeled samples in each iteration
and then
the optimizer is used to minimize the mean of the high-dimensional features. This process will re-update the network parameters
reduce the value and variance of the parameters
and thus suppress the overfitting.
The algorithm was applied to two standard datasets
namely
Indian Pines and Pavia University datasets. The 1% labeled samples were randomly selected for training. The overall classification accuracy rates were 89.61% and 98.79%
which were better than those of several algorithms. Compared with several commonly used overfitting suppression methods such as batch normalization
L2 regularization
and dropout
the proposed overfitting suppression algorithm obtains 5.60% and 3.20% higher results on randomly selected 1% labeled samples from the Indian Pines dataset and randomly selected 0.1% labeled samples from Pavia University dataset.
The model of generative adversarial networks designed for the characteristics of hyperspectral data can fully utilize the spectral and spatial features of hyperspectral images. The proposed overfitting suppression algorithm can significantly improve the classification performance of the model. However
the overfitting suppression effect of the algorithm is not obvious when the number of labeled samples is large. Thus
further research is needed.
遥感高光谱图像分类小样本过拟合生成式对抗网络光谱空间特征特征提取
remote sensinghyperspectral image classificationsmall training samplesoverfittinggenerative adversarial networkspectral-spatial featurefeature extraction
Arjovsky M, Chintala S and Bottou L. 2017. Wasserstein generative adversarial networks//Proceedings of the 34th International Conference on Machine Learning. Sydney, Australia: PMLR: 214-223
Burden F and Winkler D. 2008. Bayesian regularization of neural networks/ (/Livingstone D J, ed. Artificial Neural Networks. [s.l.]: Humana Press: 23-42) [DOI: 10.1007/978-1-60327-101-1_3]
Burnham K P and Anderson D R. 2002. Model Selection and Multimodel Inference. New York: Springer [DOI: 10.1007/b97636http://dx.doi.org/10.1007/b97636]
Camps-Valls G, Gomez-Chova L, Muñoz-Marí J, Vila-Francés J and Calpe-Maravilla J. 2006. Composite kernels for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 3(1): 93-97 [DOI: 10.1109/LGRS.2005.857031http://dx.doi.org/10.1109/LGRS.2005.857031]
Chen C, Chen N and Peng J T. 2016a. Nearest regularized joint sparse representation for hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 13(3): 424-428 [DOI: 10.1109/LGRS.2016.2517095http://dx.doi.org/10.1109/LGRS.2016.2517095]
Chen C, Li W, Su H J and Liu K. 2014a. Spectral-spatial classification of hyperspectral image based on kernel extreme learning machine. Remote Sensing, 6(6): 5795-5814 [DOI: 10.3390/rs6065795http://dx.doi.org/10.3390/rs6065795]
Chen X, Duan Y, Houthooft R, Schulman J, Sutskever I and Abbeel P. 2016b. Infogan: interpretable representation learning by information maximizing generative adversarial nets//Proceedings of the 30th Conference on Neural Information Processing Systems. Barcelona, Spain: NIPS: 2172-2180
Chen Y, Nasrabadi N M and Tran T D. 2013. Hyperspectral image classification via kernel sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 51(1): 217-231 [DOI: 10.1109/TGRS.2012.2201730http://dx.doi.org/10.1109/TGRS.2012.2201730]
Chen Y, Zhu L, Ghamisi P, Jia X P, Li G Y and Tang L. 2017. Hyperspectral images classification with Gabor filtering and convolutional neural network. IEEE Geoscience and Remote Sensing Letters, 14(12): 2355-2359 [DOI: 10.1109/LGRS.2017.2764915http://dx.doi.org/10.1109/LGRS.2017.2764915]
Chen Y S, Jiang H L, Li C Y, Jia X P and Ghamisi P. 2016c. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 54(10): 6232-6251 [DOI: 10.1109/TGRS.2016.2584107http://dx.doi.org/10.1109/TGRS.2016.2584107]
Chen Y S, Lin Z H, Zhao X, Wang G and Gu Y F. 2014b. Deep learning-based classification of hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2094-2107 [DOI: 10.1109/JSTARS.2014.2329330http://dx.doi.org/10.1109/JSTARS.2014.2329330]
Cogswell M, Ahmed F, Girshick R B, Zitnick L and Batra D. 2016. Reducing overfitting in deep networks by decorrelating representations//Proceedings of the 4th International Conference on Learning Representations. San Juan: ICLR
Cui B G, Ma X D and Xie X Y. 2017. Hyperspectral image de-noising and classification with small training samples. Journal of Remote Sensing, 21(5): 728-738
崔宾阁, 马秀丹, 谢小云. 2017. 小样本的高光谱图像降噪与分类. 遥感学报, 21(5): 728-738 [DOI: 10.11834/jrs.20176239http://dx.doi.org/10.11834/jrs.20176239]
Demarez V. 1999. Seasonal variation of leaf chlorophyll content of a temperate forest. Inversion of the PROSPECT model. International Journal of Remote Sensing, 20(5): 879-894 [DOI: 10.1080/014311699212975http://dx.doi.org/10.1080/014311699212975]
Du P J, Xia J S, Xue Z H, Tan K, Su H J and Bao R. 2016. Review of hyperspectral remote sensing image classification. Journal of Remote Sensing, 20(2): 236-256
杜培军, 夏俊士, 薛朝辉, 谭琨, 苏红军, 鲍蕊. 2016. 高光谱遥感影像分类研究进展. 遥感学报, 20(2): 236-256 [DOI: 10.11834/jrs.20165022http://dx.doi.org/10.11834/jrs.20165022]
Giacinto G, Roli F and Fumera G. 2000. Design of effective multiple classifier systems by clustering of classifiers//Proceedings of the 15th International Conference on Pattern Recognition. Barcelona, Spain: IEEE: 160-163 [DOI: 10.1109/ICPR.2000.906039http://dx.doi.org/10.1109/ICPR.2000.906039]
Goodfellow I J, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A and Bengio Y. 2014. Generative adversarial nets//Proceedings of the 27th International Conference on Neural Information Processing Systems. Montreal, Canada: NIPS: 2672-2680
Hammer B. 2001. Neural smithing—supervised learning in feedforward artificial neural networks. Pattern Analysis and Applications, 4(1): 73-74 [DOI: 10.1007/s100440170029http://dx.doi.org/10.1007/s100440170029]
He N J, Paoletti M E, Haut J M, Fang L Y, Li S T, Plaza A and Plaza J. 2019. Feature extraction with multiscale covariance maps for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 57(2): 755-769 [DOI: 10.1109/TGRS.2018.2860464http://dx.doi.org/10.1109/TGRS.2018.2860464]
Ioffe S. 2017. Batch renormalization: towards reducing minibatch dependence in batch-normalized models//Proceedings of the 31st Conference on Neural Information Processing Systems. Long Beach, USA: NIPS: 1945-1953
Ioffe S and Szegedy C. 2015. Batch normalization: accelerating deep network training by reducing internal covariate shift//Proceedings of the 32nd International Conference on Machine Learning. Lille, France: ICML: 448-456
Krizhevsky A, Sutskever I and Hinton G E. 2012. Imagenet classification with deep convolutional neural networks //Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada, USA: NIPS: 1097-1105
Lever J, Krzywinski M and Altman N. 2016. Points of significance: model selection and overfitting. Nature Methods, 13(9): 703-704 [DOI: 10.1038/nmeth.3968http://dx.doi.org/10.1038/nmeth.3968]
Liu Z, Tang B, He X F, Qiu Q C and Liu F. 2017. Class-specific random forest with cross-correlation constraints for spectral-spatial hyperspectral image classification. IEEE Geoscience and Remote Sensing Letters, 14(2): 257-261 [DOI: 10.1109/LGRS.2016.2637561http://dx.doi.org/10.1109/LGRS.2016.2637561]
Mao X D, Li Q, Xie H R, Lau R Y K, Wang Z and Paul Smolley S. 2017. Least squares generative adversarial networks//Proceedings of 2017 IEEE International Conference on Computer Vision. Venice, Italy: IEEE: 2813-2821 [DOI: 10.1109/ICCV.2017.304http://dx.doi.org/10.1109/ICCV.2017.304]
Pan B, Shi Z W and Xu X. 2017. R-VCANet: a new deep-learning-based hyperspectral image classification method. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5): 1975-1986 [DOI: 10.1109/JSTARS.2017.2655516http://dx.doi.org/10.1109/JSTARS.2017.2655516]
Qureshi R, Uzair M, Khurshid K and Yan H. 2019. Hyperspectral document image processing: applications, challenges and future prospects. Pattern Recognition, 90: 12-22 [DOI: 10.1016/j.patcog.2019.01.026http://dx.doi.org/10.1016/j.patcog.2019.01.026]
Salimans T, Goodfellow I, Zaremba W, Cheung V, Radford A and Chen X. 2016. Improved techniques for training GANs//Proceedings of the 30th Conference on Neural Information Processing Systems. Barcelona, Spain: NIPS: 2234-2242
Soltani-Farani A, Rabiee H R and Hosseini S A. 2015. Spatial-aware dictionary learning for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 53(1): 527-541 [DOI: 10.1109/TGRS.2014.2325067http://dx.doi.org/10.1109/TGRS.2014.2325067]
Srivastava N, Hinton G, Krizhevsky A, Sutskever I and Salakhutdinov R. 2014. Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research, 15(1): 1929-1958
Teke M, Deveci H S, Haliloğlu O, Gürbüz S Z and Sakarya U. 2013. A short survey of hyperspectral remote sensing applications in agriculture//Proceedings of the 2013 6th International Conference on Recent Advances in Space Technologies. Istanbul, Turkey: IEEE: 171-176 [DOI: 10.1109/RAST.2013.6581194http://dx.doi.org/10.1109/RAST.2013.6581194]
Wu B, Zhu Y, Huang X and Li J Y. 2016. Generalization of spectral fidelity with flexible measures for the sparse representation classification of hyperspectral images. International Journal of Applied Earth Observation and Geoinformation, 52: 275-283 [DOI: 10.1016/j.jag.2016.06.006http://dx.doi.org/10.1016/j.jag.2016.06.006]
Yuen P W and Richardson M. 2010. An introduction to hyperspectral imaging and its application for security, surveillance and target acquisition. The Imaging Science Journal, 58(5): 241-253 [DOI: 10.1179/174313110x12771950995716http://dx.doi.org/10.1179/174313110x12771950995716]
Zhan Y, Hu D, Wang Y T and Yu X C. 2018. Semisupervised hyperspectral image classification based on generative adversarial networks. IEEE Geoscience and Remote Sensing Letters, 15(2): 212-216 [DOI: 10.1109/LGRS.2017.2780890http://dx.doi.org/10.1109/LGRS.2017.2780890]
Zhang K, Hei B Q, Zhou Z and Li S Y. 2018. CNN with coefficient of variation-based dimensionality reduction for hyperspectral remote sensing images classification. Journal of Remote Sensing, 22(1): 87-96
张康, 黑保琴, 周壮, 李盛阳. 2018. 变异系数降维的CNN高光谱遥感图像分类. 遥感学报, 22(1): 87-96 [DOI: 10.11834/jrs.20187075http://dx.doi.org/10.11834/jrs.20187075]
Zhao W Z and Du S H. 2016. Learning multiscale and deep representations for classifying remotely sensed imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 113: 155-165 [DOI: 10.1016/j.isprsjprs.2016.01.004http://dx.doi.org/10.1016/j.isprsjprs.2016.01.004]
Zhong Z L and Li J. 2018. Generative adversarial networks and probabilistic graph models for hyperspectral image classification//Proceedings of the 32nd AAAI Conference on Artificial Intelligence. New Orleans: AAAI: 8191-8193
Zhong Z L, Li J, Luo Z M and Chapman M. 2018. Spectral-spatial residual network for hyperspectral image classification: a 3-D deep learning framework. IEEE Transactions on Geoscience and Remote Sensing, 56(2): 847-858 [DOI: 10.1109/TGRS.2017.2755542http://dx.doi.org/10.1109/TGRS.2017.2755542]
Zhu L, Chen Y S, Ghamisi P and Benediktsson J A. 2018. Generative adversarial networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 56(9): 5046-5063 [DOI: 10.1109/TGRS.2018.2805286http://dx.doi.org/10.1109/TGRS.2018.2805286]
Zur R M, Jiang Y L, Pesce L L and Drukker K. 2009. Noise injection for training artificial neural networks: a comparison with weight decay and early stopping. Medical Physics, 36(10): 4810-4818 [DOI: 10.1118/1.3213517http://dx.doi.org/10.1118/1.3213517]
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