AUTOMATIC HYPERSPECTRAL IMAGE CLASSIFICATION BASED ON DEEP FEATURE FUSION NETWORK

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

References

  1. [1] P.S. Thenkabail, I. Mariotto, M.K. Gumma, E.M. Middleton, D.R. Landis, and K.F. Huemmrich, Selection of Hyperspectral Narrowbands (HNBs) and Composition of Hyperspectral Twoband Vegetation Indices (HVIs) for biophysical characterization and discrimination of crop types using field reflectance and hyperion/EO-1 data, IEEE Journal of Selected Topics in Applied 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-programmingbased method for hyperspectral data information extraction: Agricultural applications, IEEE Transactions on Geoscience 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 hyperspectral classification method based on C5.0 decision tree of multiple combined classifiers, 2012 Fourth International Conference on Computational and Information Sciences, Chongqing, 2012, 373–376. doi: 10.1109/ICCIS.2012.33.
  4. [4] Y. Chen, Z. Lin, and X. Zhao, Riemannian manifold learning based k-nearest-neighbor for hyperspectral image classification, 2013 IEEE International Geoscience and Remote Sensing Symposium–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 for support-vector classification of hyperspectral imagery, IEEE Geoscience 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 vector machine for hyperspectral image classification, 2018 25th IEEE International 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 simplified 2D-3D CNN architecture for hyperspectral image classification based on spatial–spectral fusion, IEEE Journal of Selected Topics 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 superresolution based on multi-scale wavelet 3D convolutional neural network, IGARSS 2019–2019 IEEE International Geoscience and 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 and spectral fusion with CNN for hyperspectral image superresolution, 2018 25th IEEE International Conference on Image Processing (ICIP), Athens, 2018, 2506–2510. doi: 10.1109/ICIP.2018.8451142.
  10. [10] L. Zhuang, L. Gao, L. Ni, and B. Zhang, An improved Expectation Maximization algorithm for hyperspectral image classification, 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Gainesville, FL, 2013, 1–4. doi: 10.1109/WHISPERS.2013.8080631.
  11. [11] H. Yan, et al., Event-triggered distributed fusion estimation of networked multisensor systems with limited information, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 50(12), 2018, 5330–5337.
  12. [12] Z. You, et al., Reliable control for flexible spacecraft systems with aperiodic sampling and stochastic actuator failures, in IEEE Transactions on Cybernetics, 2020. doi: 10.1109/TCYB.2020.3008045.
  13. [13] P. Samudre, P. Shende, and V. Jaiswal, Optimizing Performance of Convolutional Neural Network Using Computing Technique, 2019 IEEE 5th International Conference for Convergence 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 recurrent neural network by combining convolution neural network and a simple initialization of the weights, 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS), Chongqing, 2016, 150–154. doi: 10.1109/ICOACS.2016.7563068.
  15. [15] M. Yang, B. Li, H. Fan, and Y. Jiang, Randomized spatial pooling in deep convolutional networks for scene recognition, 2015 IEEE International Conference on Image Processing (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 attention mechanism and convolutional neural network, 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation 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 vector machine and softmax classifiers in computer vision, 2017 Second International Conference on Mechanical, Control and Computer Engineering (ICMCCE), Harbin, 2017, 151–155. doi: 10.1109/ICMCCE.2017.49.
  18. [18] A. Namozov and Y.I. Cho, Convolutional Neural Network Algorithm with Parameterized Activation Function for Melanoma Classification, 2018 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, 2018, 417–419. doi: 10.1109/ICTC.2018. 8539451.
  19. [19] Z. Zhang, Z. Yang, Y. Sun, Y. Wu, and Y. Xing, Lenet5 convolution neural network with mish activation function and fixed memory step gradient descent method, 2019 16th International Computer Conference on Wavelet Active 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 research on improving activation function of convolutional neural networks, 2019 Chinese Control And Decision Conference (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-valued convolutional neural network with different activation functions in polarimetric SAR image classification, 2019 International Radar 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, Integrating local CNN and global CNN for script identification in nat374 ural 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 using fast Winograd algorithm, 2018 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), Turin, 2018, 1–2. doi: 10.1109/CODESISSS.2018.8525909.

Important Links:

Go Back