سابِق|تال

Relative water content retrieval and refined classification of hyperspectral images based on a symbiotic neural network

  • role： First author第一作者
  • Affiliation：

    School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China

  • Email：liangxuejian@hit.edu.cn
  • Introduction：梁雪剑，1993年生，男，博士研究生，研究方向为高光谱物化参数反演、精细分类和人工智能。E-mail： liangxuejian@hit.edu.cn
LIANG Xuejian,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China

  • Email：zhye@hit.edu.cn
  • Introduction：张晔，1960年生，男，教授，研究方向为高光谱遥感图像处理与智能应用。E-mail： zhye@hit.edu.cn
ZHANG Ye*,  
  • Affiliation：

    School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China

ZHANG Junping

ملخص

The application of deep learning methods in the hyperspectral research field has been developed continuously, and hyperspectral image classification models based on deep learning methods achieve high classification accuracy compared with others. Current classification models mostly use hyperspectral graphic-spectral features but lack diagnostic features and prior information, which makes it difficult to extract spectral-spatial features collaboratively and implement refined classification further. The results are not refined for intraclass classification. To solve the abovementioned issues, we proposed a Symbiotic Neural Network (SNN) for hyperspectral refined classification, which regards a multilabeled data set as input. SNN can fuse spectrum diagnostic features with graphic-spectral features to retrieve relative water content and implement refined classification simultaneously. First, we proposed a new spectral index, Red Edge Sloped (RES), to characterize the relative water content and used RES to label the relative water content on all the objects in data sets via an adaptive grading algorithm. We then built a multilabeled data set with original object labels. Second, we established the SNN architecture and dimensionality-varied feature extraction module to extract fusion information of space, spectra, and relative water content in hyperspectral data, which was able to improve the cooperative expression capability for features and the discrimination capability for the water content of various ground objects. It was also able to reduce the structural complexity and amount of computation of the deep model. Moreover, the dimensionality-varied module was able to extract more accurate and abstract features at a high level and improved the extensibility to build a deeper network. After the aforementioned progress, we implemented hyperspectral image refined classification with the guide of relative water content retrieval by using the SNN and completed interclass and intraclass classification simultaneously. On the basis of the relative water content retrieval in the classification, the interclass and intraclass distances can be further expanded over traditional classification methods. Experiments were conducted using one hyperspectral data set collected in the laboratory and four open hyperspectral data sets, namely, Lopex, Indian Pines, Pavia University, and Salinas, to validate the effectiveness of RES and the superiority of the SNN model. The experimental results demonstrated that the RES index proposed in this paper was able to represent the relative water content of objects in data effectively. The classification accuracy and discrimination capability for the water content of SNN were improved evidently with the guide of relative water content retrieval. In addition, we compared SNN with other state-of-the-art methods, and SNN obtained higher classification accuracy. Therefore, the SNN model proposed in this paper, which implements refined classification with the guide of relative water content retrieval, can enhance the feature extraction capability of the model for hyperspectral image classification and improve the classification performance effectively.

مفهوم

hyperspectral remote sensing;image classification;deep learning;symbiotic neural network;relative water content retrieval;dimensionality-varied feature extraction

References

  1. 1.
    Alipourfard T, Arefi H and Mahmoudi S. 2018. A novel deep learning framework by combination of subspace-based feature extraction and convolutional neural networks for hyperspectral images classification//IEEE International Geoscience and Remote Sensing Symposium. Valencia: IEEE: 4780-4783
  2. 2.
    Chan T H, Jia K, Gao S H, Lu J W, Zeng Z N and Ma Y. 2015. PCANet: a simple deep learning baseline for image classification?. IEEE Transactions on Image Processing, 24(12): 5017-5032
  3. 3.
    Chen Y S, Lin Z H, Zhao X, Wang G and Gu Y F. 2014. Deep learning-based classification of hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2094-2107
  4. 4.
    Chen Y S, Zhao X and Jia X P. 2015. Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6): 2381-2392
  5. 5.
    Chen Z X, Ren J Q, Tang H J, Shi Y, Leng P, Liu J, Wang L M, Wu W B, Yao Y M and Hasiyuya. 2016. Progress and perspectives on agricultural remote sensing research and applications in China. Journal of Remote Sensing, 20(5): 748-767
  6. 6.
    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
  7. 7.
    Dutta D, Das P K, Alam K A, Safwan P, Paul S, Nanda M K and Dadhwal V K. 2016. Delta area at near infrared region (DANIR)—a novel approach for green vegetation fraction estimation using field hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(9): 3970-3981
  8. 8.
    Gevaert C M, Tang J, García-Haro F J, Suomalainen J and Kooistra L. 2014. Combining hyperspectral UAV and multispectral Formosat-2 imagery for precision agriculture applications//6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. Lausanne: IEEE: 2014: 1-4
  9. 9.
    Ghamisi P, Chen Y S and Zhu X X. 2016. A self-improving convolution neural network for the classification of hyperspectral data. IEEE Geoscience and Remote Sensing Letters, 13(10): 1537-1541
  10. 10.
    Hu W, Huang Y Y, Wei L, Zhang F and Li H C. 2015. Deep convolutional neural networks for hyperspectral image classification. Journal of Sensors, 2015: 258619
  11. 11.
    Jia P Y, Zhang M, Yu W B, Shen F and Shen Y. 2016. Convolutional neural network based classification for hyperspectral data//Geoscience and Remote Sensing Symposium. Beijing: IEEE: 5075-5078
  12. 12.
    Li T, Zhang J P and Zhang Y. 2014. Classification of hyperspectral image based on deep belief networks//IEEE International Conference on Image Processing (ICIP) 2014. Paris: IEEE: 5132-5136
  13. 13.
    Li Y, Zhang H K and Shen Q. 2017. Spectral-spatial classification of hyperspectral imagery with 3D convolutional neural network. Remote Sensing, 9(1): 67
  14. 14.
    Liu B, Wu C and Lin Y. 2016. Feature weighted-mixed kernel SVM for remote sensing wetland classification. Engineering of Surveying and Mapping, 25(7): 13-17
  15. 15.
    Makantasis K, Karantzalos K, Doulamis A and Doulamis N. 2015. Deep supervised learning for hyperspectral data classification through convolutional neural networks//IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Milan: IEEE: 4959-4962
  16. 16.
    Pan B, Shi Z W, Zhang N and Xie S B. 2016. Hyperspectral image classification based on nonlinear spectral–spatial network. IEEE Geoscience and Remote Sensing Letters, 13(12): 1782-1786
  17. 17.
    Paoletti M E, Haut J M, Fernandez-Beltran R, Plaza J, Plaza A, Li J and Pla F. 2019a. Capsule networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 57(4): 2145-2160
  18. 18.
    Paoletti M E, Haut J M, Fernandez-Beltran R, Plaza J, Pla za A J and Pla F. 2019b. Deep pyramidal residual networks for spectral-spatial hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 57(2): 740-754
  19. 19.
    Ren G B, Zhang J, Ma Y and Zheng R E. 2010. Generative model based semi-supervised learning method of remote sensing image classification. Journal of Remote Sensing, 14(6): 1097-1114
  20. 20.
    Rojo-Álvarez J L, Martínez-Ramón M, Muñoz-Marí J and Camps-Valls G. 2018. Support vector machine and kernel classification algorithms//Rojo-Álvarez J L, Martínez-Ramón M, Muñoz-Marí J and Camps-Valls G, eds. Digital Signal Processing with Kernel Methods. Hoboken: John Wiley and Sons Ltd.: 433-502
  21. 21.
    Tong G F, Li Y, Cao L H and Chen J. 2017. A DBN for hyperspectral remote sensing image classification//12th IEEE Conference on Industrial Electronics and Applications. Siem Reap: IEEE: 1757-1762
  22. 22.
    Wang S H, Lv Y D, Sui Y X, Liu S, Wang S J and Zhang Y D. 2018. Alcoholism detection by data augmentation and convolutional neural network with stochastic pooling. Journal of Medical Systems, 42(1): 2
  23. 23.
    Wu J F, Jiang Z G, Zhang H P, Cai B W and Luo P H. 2017. Hyperspectral remote sensing image classification based on semi-supervised conditional random field. Journal of Remote Sensing, 21(4): 588-603
  24. 24.
    Yao K, Guo X D, Nan Y, Li K, Jiang S F and Sun T T. 2016. Research progress of hyperspectral remote sensing monitoring of vegetation biomass assessment. Science of Surveying and Mapping, 41(8): 48-53
  25. 25.
    Zhang J P, Ma W J and Qi J G. 2012. Water quality model parameters inversion based on improved stochastic optimization//2012 IEEE International Geoscience and Remote Sensing Symposium. Munich: IEEE: 2032-2035
  26. 26.
    Zhao S H, Zhang F, Wang Q, Yao Y J, Wang Z T and You D A. 2013. Application of hyper-spectral remote sensing technology in environmental protection. Spectroscopy and Spectral Analysis, 33(12): 3343-3348
  27. 27.
    Zhao W Z and Du S H. 2016. Spectral-spatial feature extraction for hyperspectral image classification: a dimension reduction and deep learning approach. IEEE Transactions on Geoscience and Remote Sensing, 54(8): 4544-4554
  28. 28.
    Zhong S W, Chang C I and Zhang Y. 2018. Iterative support vector machine for hyperspectral image classification//IEEE International Conference on Image Processing (ICIP). Athens: IEEE: 3309-3312
  29. 29.
    Zhou G Y, Lin B J and Ma X J. 2013. A spatial clustering algorithm for line objects based on extended Hausdorff distance//21st International Conference on Geoinformatics. Kaifeng: IEEE: 1-4

قراءة النص الكامل

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website