Hyperspectral image classification based on hybrid convolutional neural network

LIU Cuilian; TAO Yuxiang; LUO Xiaobo; LI Qingyan

doi:10.7510/jgjs.issn.1001-3806.2022.03.009

LASER TECHNOLOGY > 2022 > 46(3): 355-361. > DOI: 10.7510/jgjs.issn.1001-3806.2022.03.009

LIU Cuilian, TAO Yuxiang, LUO Xiaobo, LI Qingyan. Hyperspectral image classification based on hybrid convolutional neural network[J]. LASER TECHNOLOGY, 2022, 46(3): 355-361. DOI: 10.7510/jgjs.issn.1001-3806.2022.03.009

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Hyperspectral image classification based on hybrid convolutional neural network

1.
School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
2.
Spatial Big Data Research Center, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

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Received Date: April 13, 2021
Revised Date: May 25, 2021
Published Date: May 24, 2022

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Abstract

The traditional convolutional neural network method can loss some feature information, which may lead to unsatisfied terrain classification accuracy in the field of hyperspectral. In order to solve the problem, a new hyperspectral images classification method based on the 2-D and the 3-D, named hybrid convolutional neural network, was proposed. This method mainly extracted features from the spatial enhancement aspect and the spectral-spatial aspect. Firstly, a 3-D-2-D convolutional neural network hybrid structure was proposed for enhance spatial information. Secondly, the 3-D convolutional neural network structure was used for joint feature extraction from the aspect of spectral-spatial, and then the spectral-spatial comprehensive separability information was obtained. Finally, the separately obtained information was feature fused and classified. This method was used for classification experiments on hyperspectral data sets and compared with other methods. The results show that the classification accuracy of this method is 99.36% and 99.95% respectively in Indian Pines and Pavia University data set, and its classification accuracy and kappa coefficient are also better than other methods. This method has a competitive advantage in the classification of hyperspectral images.

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[1]	LI X, DING M L, PIŽURICA A. Deep feature fusion via two-stream convolutional neural network for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(4): 2615-2629. DOI: 10.1109/TGRS.2019.2952758
[2]	GUO H, LIU J, XIAO Z, et al. Deep CNN-based hyperspectral image classification using discriminative multiple spatial-spectral feature fusion[J]. Remote Sensing Letters, 2020, 11(9): 827-836. DOI: 10.1080/2150704X.2020.1779374
[3]	ZHAO W Zh, DU Sh H. Spectral-spatial feature extraction for hyperspectral image classification: A dimension reduction and deep learning approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(8): 4544-4554. DOI: 10.1109/TGRS.2016.2543748
[4]	CHEN Y Sh, LIN Zh H, ZHAO X, et al. Deep learning-based classification of hyperspectral data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote, 2014, 7(6): 2094-2107. DOI: 10.1109/JSTARS.2014.2329330
[5]	LIU P, CHOO K K R, WANG L, et al. SVM or deep learning? A comparative study on remote sensing image classification[J]. Soft Computing, 2017, 21(23): 7053-7065. DOI: 10.1007/s00500-016-2247-2
[6]	ZHONG P, GONG Zh Q, LI Sh T, et al. Learning to diversify deep belief networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(6): 3516-3530. DOI: 10.1109/TGRS.2017.2675902
[7]	ZHANG M M, LI W, DU Q. Diverse region-based CNN for hyperspectral image classification[J]. IEEE Transactions on Image Processing, 2018, 27(6): 2623-2634. DOI: 10.1109/TIP.2018.2809606
[8]	XU Y H, ZHANG L P, DU B, et al. Spectral-spatial unified networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(10): 5893-5909.
[9]	PAOLETTI M E, HAUT J M, FERNANDEZ-BELTRAN R, et al. Capsule networks for hyperspectral image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2019, 57(4): 2145-2160. DOI: 10.1109/TGRS.2018.2871782
[10]	ZHANG H K, LI Y, ZHANG Y Zh, et al. Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network[J]. Remote Sensing Letters, 2017, 8(5): 438-447. DOI: 10.1080/2150704X.2017.1280200
[11]	ZHONG Z L, LI J, LUO Zh M, et al. Spectral-spatial residual network for hyperspectral image classification: A 3-D deep learning framework[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(2): 847-858. DOI: 10.1109/TGRS.2017.2755542
[12]	LI J Y, LIANG B X, WANG Y H. A hybrid neural network for hyperspectral image classification[J]. Remote Sensing Letters, 2020, 11(1): 96-105. DOI: 10.1080/2150704X.2019.1686780
[13]	LI W, WU G D, ZHANG F, et al. Hyperspectral image classification using deep pixel-pair features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2): 844-853. DOI: 10.1109/TGRS.2016.2616355
[14]	CHEN Y Sh, JIANG H L, LI Ch Y, et al. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10): 6232-6251. DOI: 10.1109/TGRS.2016.2584107
[15]	ROY S K, KRISHNA G, DUBEY S R, et al. HybridSN: Exploring 3-D-2-D CNN feature hierarchy for hyperspectral image classification[J]. IEEE Geoscience and Remote Sensing Letters, 2020, 17(2): 277-281. DOI: 10.1109/LGRS.2019.2918719
[16]	GUAN Sh H, YANG G, LI H, et al. Hyperspectral image classification based on 3-D convolutional recurrent neural network[J]. Laser Technology, 2020, 44(4): 485-491(in Chinese).
[17]	LI Y, ZHANG H K, SHEN Q. Spectral-spatial classification of hyperspectral imagery with 3-D convolutional neural network[J]. Remote Sensing, 2017, 9(1), 67. DOI: 10.3390/rs9010067
[18]	MAKANTASIS K, KARANTZALOS K, DOULAMIS A, et al. Deep supervised learning for hyperspectral data classification through convolutional neural networks[C]//2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). New York, USA: IEEE, 2015: 4959-4962.
[19]	HANG R L, LI Zh, GHAMISI P, et al. Classification of hyperspectral and LiDAR data using coupled CNNs[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(7): 4939-4950. DOI: 10.1109/TGRS.2020.2969024
[20]	HE M Y, LI B, CHEN H H. Multi-scale 3-D deep convolutional neural network for hyperspectral image classification[C]// 2017 IEEE International Conference on Image Processing (ICIP). New York, USA: IEEE, 2017: 3904-3908.
[21]	MELGANI F, BRUZZONE L. Classification of hyperspectral remote sensing images with support vector machines[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(8): 1778-1790. DOI: 10.1109/TGRS.2004.831865

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