Yunfei Zhang,∗ Yuelong Zhu,∗ Hexuan Hu,∗ and Hongyan Wang∗∗


  1. [1] P.S. Thenkabail, I. Mariotto, M.K. Gumma, E.M. Middleton,D.R. Landis, and K.F. Huemmrich, Selection of Hyperspec-tral Narrowbands (HNBs) and Composition of HyperspectralTwoband Vegetation Indices (HVIs) for biophysical character-ization and discrimination of crop types using field reflectanceand hyperion/EO-1 data, IEEE Journal of Selected Topics inApplied Earth Observations and Remote Sensing, 6(2), 2013,427–439. doi: 10.1109/JSTARS.2013.2252601.
  2. [2] C. Chion, J. Landry, and L. Da Costa, A genetic-programming-based method for hyperspectral data information extrac-tion: Agricultural applications, IEEE Transactions on Geo-science and Remote Sensing, 46(8), 2008, 2446–2457. doi:10.1109/TGRS.2008.922061.
  3. [3] M. Wang, K. Gao, L. Wang, and X. Miu, A novel hyperspectralclassification method based on C5.0 decision tree of multiplecombined classifiers, 2012 Fourth International Conference onComputational and Information Sciences, Chongqing, 2012,373–376. doi: 10.1109/ICCIS.2012.33.
  4. [4] Y. Chen, Z. Lin, and X. Zhao, Riemannian manifold learningbased k-nearest-neighbor for hyperspectral image classification,2013 IEEE International Geoscience and Remote SensingSymposium–IGARSS, Melbourne, VIC, 2013, 1975–1978. doi:10.1109/IGARSS.2013.6723195.
  5. [5] W. Liu, J.E. Fowler, and C. Zhao, Spatial logistic regression forsupport-vector classification of hyperspectral imagery, IEEEGeoscience and Remote Sensing Letters, 14(3), 2017, 439–443.doi: 10.1109/LGRS.2017.2648515.
  6. [6] S. Zhong, C. Chang, and Y. Zhang, Iterative support vectormachine for hyperspectral image classification, 2018 25th IEEEInternational Conference on Image Processing (ICIP), Athens,2018, 3309–3312. doi: 10.1109/ICIP.2018.8451145.
  7. [7] C. Yu, R. Han, M. Song, C. Liu, and C. Chang, A simplified2D-3D CNN architecture for hyperspectral image classificationbased on spatial–spectral fusion, IEEE Journal of SelectedTopics in Applied Earth Observations and Remote Sensing,13, 2020, 2485–2501. doi: 10.1109/JSTARS.2020.2983224.
  8. [8] J. Yang, Y. Zhao, and J.C. Chan, Hyperspectral image super-resolution based on multi-scale wavelet 3D convolutional neuralnetwork, IGARSS 2019–2019 IEEE International Geoscienceand Remote Sensing Symposium, Yokohama, Japan, 2019,2770–2773. doi: 10.1109/IGARSS.2019.8898813.
  9. [9] X. Han, B. Shi, and Y. Zheng, SSF-CNN: Spatial andspectral fusion with CNN for hyperspectral image super-resolution, 2018 25th IEEE International Conference onImage Processing (ICIP), Athens, 2018, 2506–2510. doi:10.1109/ICIP.2018.8451142.
  10. [10] L. Zhuang, L. Gao, L. Ni, and B. Zhang, An improvedExpectation Maximization algorithm for hyperspectral im-age classification, 2013 5th Workshop on Hyperspectral Im-age and Signal Processing: Evolution in Remote Sensing(WHISPERS), Gainesville, FL, 2013, 1–4. doi: 10.1109/WHIS-PERS.2013.8080631.
  11. [11] H. Yan, et al., Event-triggered distributed fusion estimation ofnetworked multisensor systems with limited information, IEEETransactions on Systems, Man, and Cybernetics: Systems,50(12), 2018, 5330–5337.
  12. [12] Z. You, et al., Reliable control for flexible spacecraft sys-tems with aperiodic sampling and stochastic actuator fail-ures, in IEEE Transactions on Cybernetics, 2020. doi:10.1109/TCYB.2020.3008045.
  13. [13] P. Samudre, P. Shende, and V. Jaiswal, Optimizing Perfor-mance of Convolutional Neural Network Using ComputingTechnique, 2019 IEEE 5th International Conference for Con-vergence in Technology (I2CT), Bombay, India, 2019, 1–4. doi:10.1109/I2CT45611.2019.9033876.
  14. [14] X. Xu, H. Ge, and S. Li, An improvement on recur-rent neural network by combining convolution neural net-work and a simple initialization of the weights, 2016 IEEEInternational Conference of Online Analysis and Comput-ing Science (ICOACS), Chongqing, 2016, 150–154. doi:10.1109/ICOACS.2016.7563068.
  15. [15] M. Yang, B. Li, H. Fan, and Y. Jiang, Randomizedspatial pooling in deep convolutional networks for scenerecognition, 2015 IEEE International Conference on ImageProcessing (ICIP), Quebec City, QC, 2015, 402–406. doi:10.1109/ICIP.2015.7350829.
  16. [16] H. Li and J. Li, Recognition of robot based on attentionmechanism and convolutional neural network, 2019 IEEE 3rdInformation Technology, Networking, Electronic and Automa-tion Control Conference (ITNEC), Chengdu, China, 2019,2578–2584. doi: 10.1109/ITNEC.2019.8728976.
  17. [17] X. Qi, T. Wang, and J. Liu, Comparison of support vectormachine and softmax classifiers in computer vision, 2017Second International Conference on Mechanical, Control andComputer Engineering (ICMCCE), Harbin, 2017, 151–155.doi: 10.1109/ICMCCE.2017.49.
  18. [18] A. Namozov and Y.I. Cho, Convolutional Neural Net-work Algorithm with Parameterized Activation Functionfor Melanoma Classification, 2018 International Conferenceon Information and Communication Technology Conver-gence (ICTC), Jeju, 2018, 417–419. doi: 10.1109/ICTC.2018.8539451.
  19. [19] Z. Zhang, Z. Yang, Y. Sun, Y. Wu, and Y. Xing, Lenet-5 convolution neural network with mish activation func-tion and fixed memory step gradient descent method, 201916th International Computer Conference on Wavelet Ac-tive Media Technology and Information Processing, Chengdu,China, 2019, 196–199. doi:10.1109/ICCWAMTIP47768.2019.9067661.
  20. [20] Y. Guo, L. Sun, Z. Zhang, and H. He, Algorithm re-search on improving activation function of convolutional neu-ral networks, 2019 Chinese Control And Decision Con-ference (CCDC), Nanchang, China, 2019, 3582–3586. doi:10.1109/CCDC.2019.8833156.
  21. [21] Y. Zhang, Q. Hua, D. Xu, H. Li, and H. Mu, A complex-valuedconvolutional neural network with different activation functionsin polarimetric SAR image classification, 2019 InternationalRadar Conference (RADAR), Toulon, France, 2019, 1–4. doi:10.1109/RADAR41533.2019.171298.
  22. [22] L. Lu, Y. Yi, F. Huang, K. Wang, and Q. Wang, Integratinglocal CNN and global CNN for script identification in nat-374ural scene images, IEEE Access, 7, 2019, 52669–52679. doi:10.1109/ACCESS.2019.2911964.
  23. [23] X. Wang, C. Wang, and X. Zhou, Work-in-progress: WinoNN:Optimising FPGA-based neural network accelerators us-ing fast Winograd algorithm, 2018 International Confer-ence on Hardware/Software Codesign and System Synthe-sis (CODES+ISSS), Turin, 2018, 1–2. doi: 10.1109/CODE-SISSS.2018.8525909.

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