Hyperspectral Data Analysis based on Integrated Deep Learning

doi:10.23940/ijpe.20.08.p9.12251234

Abstract

Abstract: Due to the high dimensions of complex hyperspectral data features and the difficulty of feature selection and extraction, it is difficult to construction an inversion model. This paper briefly describes the common methods of hyperspectral data analysis and processing. To address the existing problems in the field of hyperspectral data analysis, an integrated deep learning framework combined with artificial neural network and large GAN is proposed in this paper. The application of this integrated deep learning framework in hyperspectral data analysis is discussed further. Results show that the integrated deep learning method combining neural network and large GAN provides direction for hyperspectral data analysis and processing, which has a broad application prospect.

Key words: hyperspectral data, deep learning, super-resolution reconstruction, generative adversarial nets (GAN), convolutional neural network (CNN)

Zhifeng Zhang, Xiao Cui, Pu Li, Jintao Jiang, and Xiaohui Ji. Hyperspectral Data Analysis based on Integrated Deep Learning [J]. Int J Performability Eng, 2020, 16(8): 1225-1234.

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References

1. L. He, J. Li, C. Y. Liu,S. T. Li, “Recent Advances on Spectral-Spatial Hyperspectral Image Classification: An Overview and New Guidelines,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 56, No. 3, pp. 1579-1597, 2017
2. X. F. Gu, Y. Tao, G. L. Tian, S. Y. Zhou, C. J. Wei, J. Li, et al., “Up to the Higher Altitude - the New “Three Campaigns” for the Development of China Spaceborne Remote Sensing Application,” Journal of Remote Sensing, Vol. 20, No. 5, pp. 781-793, 2016
3. M. Sowden, U. Mueller,D. Blake, “Review of Surface Particulate Monitoring of Dust Events using Geostationary Satellite Remote Sensing,”Atmospheric Environment, Vol. 183, pp. 154-164, 2018
4. “Resolution Earth Observation System Major Special Network,” (http://www.cheos.org.cn/, accessed March 2020
5. X. Cui, Y. Q. Zhao, J. T. Cui, Q. Zheng,B. Wang, “Synthesis, Characterization and Nitrite Ion Sensing Performance of Reclaimable Composite Samples Through a Core-Shell Structure,”Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, Vol. 191, pp. 442-453, 2018
6. C. Xiao and M. Z. He, “A Phosphorescent Rhenium Emitter with Minimal Fluorescent Component Induced by Heavy Atom: Synthetic Procedure, Molecular Structure, Photophysical Feature and Optoelectronic Application,”Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy, Vol. 118, pp. 888-896, 2014
7. C. Xiao and M. Z. He, “An “off-on” Fluorescence Probe for Hg(II) Detection using Upconversion Nanobars as the Excitation Source: Preparation, Characterization and Sensing Performance,” Journal of Luminescence, Vol. 145, No. 6, pp. 364-370, 2014
8. C. Xiao and M. Z. He, “Study on the Construction and Performance of a Hg(II)-Sensing System Having an “off-on” Probe Derived from Rhoda Mine and an up-Conversion Excitation Core,”Journal of Luminescence, Vol. 145, pp. 539-546, 2014
9. P. Ghamisi, N. Yokoya, J. Li, W. Z. Liao, S. C. Liu, J. Plaza, et al., “Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art,” IEEE Geoscience and Remote Sensing Magazine, Vol. 5, No. 4, pp. 37-78, 2018
10. M. Batz, A. Eichenseer, J. Seiler, et al., “Hybird Super-Resolution Combining Example-based Single-Image and Interpolation-based Multi-Image Reconstruction Approaches,” in Proceedings of2015 IEEE International Conference on Image Processing, pp. 58-62, 2015
11. K. I.Kim and Y. Kwon, “Single-Image Super-Resolution using Sparse Regression and Natural Image Prior,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 6, pp. 1127-1133, 2010
12. Q. S.Lian and W. Zhang, “Super-Resolution Reconstruction Algorithm based on Classification of Sparse Representations of Image Blocks,” Journal of Electronics, Vol. 40, No. 5, pp. 920-925, 2012
13. J. S. Xiao, E. Y. Liu, L. Zhu, et al., “Improved Image Super-Resolution Algorithm based on Convolutional Neural Network,” Acta Optica Sinica, Vol. 37, No. 3, pp. 0318011, 2017
14. R. G. Zhou, X. G. Liu,J. Luo, “Quantum Circuit Realization of the Bilinear Interpolation Method for GQIR,” International Journal of Theoretical Physics, Vol. 56, No. 9, 2017
15. R. Keys, “Cubic Convolution Interpolation for Digital Image Processing,” IEEE Transactions on Acoustics Speech and Signal Processing, Vol. 29, No. 6, pp. 1153-1160, 1981
16. M. Irani and S. Peleg, “Improving Resolution by Image Registration,” CVGIP: Graphical Models and Image Processing, Vol. 53, No. 3, pp. 231-239, 1991
17. H. Stark and P. Oskoui, “High-Resolution Image Recovery from Image-Plane Arrays, using Convex Projection,” JOSAA, Vol. 6, No. 11, pp. 1715-1726, 1989
18. R. Timofte and V. de Smet, “Van Gool A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution,” inProceedings of Asian Conference on Computer Vision, pp. 111-126, 2015
19. J. C. Yang, J. Wright, T. Huang,Y. Ma, “Image Super-Resolution as Sparse Representation of Raw Image Patches,” inProceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, Anchorage, Alaska, USA, 2008
20. Y. Sun, H. Song, K. Zhang,F. Yan, “Face Super-Resolution via Very Deep Convolutional Neural Network,” Journal of Computer Applications, Vol. 38, No. 4, pp. 1141-1145, 2018
21. C. Dong, C. C. Loy, K. He, et al., “Learning a Deep Convolutional Network for Image Super-Resolution,” inProceedings of European Conference on Computer Vision, pp. 184-199, 2014
22. J. Kim, K. J. Lee,M. K. Lee, “Accurate Image Super-Resolution using Very Deep Convolutional Networks,” inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1646-1654, 2016
23. B. Lim, S. Son, H. Kim, et al., “Enhanced Deep Residual Networks for Single Image Super - Resolution,” inProceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 136-144, 2017
24. S. T. Alvarado, T. Fornazari, A. Cóstola, et al., “Drivers of Fire Occurrence in a Mountainous Brazilian Cerrado Savanna: Tracking Long-Term Fire Regimes using Remote Sensing,”Ecological Indicators, Vol. 78, pp. 270-281, 2017
25. C. Gong, S. Yu, H. Joesting, et al., “Determining Socioeconomic Drivers of Urban Forest Fragmentation with Historical Remote Sensing Images,” Landscape and Urban Planning, Vol. 117, No. 9, pp. 57-65, 2013
26. M. Haris, G. Shakhnarovich,N. Ukita, “Deep Back-Projection Networks for Super-Resolution,” 2018
27. C. I. Chang, Q. Du, T. L. Sun, et al., “A Joint Band Prioritization and Band-Decorrelation Approach to Band Selection for Hyperspectral Image Classification,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 37, No. 6, pp. 2631-2641, 1999
28. Z. X. Feng, “Research on Hyperspectral Data Dimensionality Reduction and Classification based on Spatial Sparse Structure,”Journal of Xidian University, 2018
29. P. Bajcsy and P. Groves, “Methodology for Hyperspectral Band Selection,” Photogrammetric Engineering and Remote Sensing, Vol. 70, No. 7, pp. 793-802, 2004
30. Y. Yuan, J. Lin,Q. Wang, “Dual-Clustering-based Hyperspectral Band Selection by Contextual Analysis,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, No. 3, pp. 1431-1445, 2016
31. Y. Yuan, G. K. Zhu,Q. Wang, “Hyperspectral Band Selection by Multitask Sparsity Pursuit,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 53, No. 2, pp. 631-644, 2015
32. J. Feng, L. C. Jiao, F. Liu, T. Sun,X. R. Zhang, “Unsupervised Feature Selection based on Maximum Information and Minimum Redundancy for Hyperspectral Images,”Pattern Recognition, Vol. 51, pp. 295-309, 2016
33. H. Yang, Q. Du,G. S. Chen, “Unsupervised Hyperspectral Band Selection using Graphics Processing Units,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 4, No. 3, pp. 660-668, 2011
34. C. Peng, “Face Recognition based on Subspace Analysis and Semi Supervised Learning,” 2012
35. C. Pei,“Principal Component Analysis and its Application in Feature Extraction,” Shaanxi Normal University, Xi'an, 2014
36. K. Tanzawa, S. Koshita, M. Abe,M. Kawamata, “Analysis of Cost Function based on Kullback-Leibler Divergence in Independent Component Analysis for Two Uniformly Distributed Source Signals,” Electronics and Communications in Japan, Vol. 101, No. 8, pp. 282-286, 2018
37. Y. Zhou, Y. Wang,X. H. Wang, “Face Recognition Algorithm based on Wavelet Transform and Local Linear Embedding,”Cluster Computing, No. 9, pp. 1-12, 2018
38. M. H.Wan and Z. H. Lai, “Generalized Discriminant Local Median Preserving Projections (GDLMPP) for Face Recognition,”Neural Processing Letters, No. 8, pp. 1-13, 2018
39. L. F. Zhang, L. P. Zhang, D. C. Tao,X. Huang, “A Modified Stochastic Neighbor Embedding for Multi-Feature Dimension Reduction of Remote Sensing Images,” ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 83, No. 9, pp. 30-39, 2013
40. W. Soh, H. Kim,B. J. Yum, “Application of Kernel Principal Component Analysis to Multi-Characteristic Parameter Design Problems,” Annals of Operations Research, Vol. 263, No.1-2, pp. 69-91, 2018
41. N. Varish and A. K. Pal, “A Novel Image Retrieval Scheme using Gray Level Co-Occurrence Matrix Descriptors of Discrete Cosine Transform based Residual Image,” Applied Intelligence, Vol. 48, No. 9, pp. 2930-2953, 2018
42. X. Kang, S. Li,J. A. Benediktsson, “Feature Extraction of Hyperspectral Images with Image Fusion and Recursive Filtering,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 6, pp. 3742-3752, 2014
43. W. Z. Liao, R. Bellens, A. Pižurica, W. Philips,Y. G. Pi, “Classification of Hyperspectral Data over Urban Areas using Directional Morphological Profiles and Semi-Supervised Feature Extraction,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 5, No. 4, pp. 1177-1190, 2012
44. Z. Y. Lv, P. L. Zhang, J. A. Benediktsson,W. Z. Shi, “Morphological Profiles based on Differently Shaped Structuring Elements for Classification of Images with Very High Spatial Resolution,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7, No. 12, pp. 4644-4652, 2014
45. B. Q. Song, J. Li, M. D. Mura, P. J. Li, A. Plaza. J. M. Bioucas-Dias, et al., “Remotely Sensed Image Classification using Sparse Representations of Morphological Attribute Profiles,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 52, No. 8, pp. 5122-5136, 2014
46. A. Krizhevsky, I. Sutskever,G. E. Hinton, “ImageNet Classification with Deep Convolutional Neural Networks,” inProceedings of International Conference on Neural Information Processing Systems, pp. 1097-1105, Lake Tahoe, NV, USA, 2012
47. Y. S. Chen, Z. H. Lin, X. Zhao, G. Wang,Y. F. Gu, “Deep Learning-based Classification of Hyperspectral Data,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 7, No. 6, pp. 2094-2107, 2017
48. X. Han, Y. Zhong,L. Zhang, “Spatial-Spectral Unsupervised Convolutional Sparse Auto-Encoder Classifier for Hyperspectral Imagery,” Photogrammetric Engineering and Remote Sensing, Vol. 83, No. 3, pp. 195-206, 2017
49. A. Romero, C. Gatta,G. Camps-Valls, “Unsupervised Deep Feature Extraction for Remote Sensing Image Classification,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 54, No. 3, pp. 1-14, 2015
50. W. Li, G. D. Wu, F. Zhang,Q. Du, “Hyperspectral Image Classification using Deep Pixel-Pair Features,” IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, No. 2, pp. 844-853, 2016
51. I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, et al., “Generative Adversarial Nets,” inProceedings of International Conference on Neural Information Processing Systems, pp. 2672-2680, Montreal, QC, Canada, 2014
52. C. JG, “Research on the Application Value of Deep Learning Technology in the Construction of Medical Image Standardization Platform,” Tianjin Medical University, 2018
53. T. Miyato, T. Kataoka, M. Koyama,Y. Yoshida, “Spectral Normalization for Generative Adversarial Networks,” inProceedings of International Conference on Learning Representations, 2018
54. H. Zhang, I. Goodfellow, D. Metaxas,A. Odena, “Self-Attention Generative Adversarial Networks,” inProceedings of the 36th International Conference on Machine Learning, pp. 7354-7363, 2019
55. A. Brock, J. Donahue,K. Simonyan, “Large Scale GAN Training for High Fidelity Natural Image Synthesis,” arXiv:1809.11096, 2019
56. R. S.Fard and H. R. Matinfar, “Capability of vis-NIR Spectroscopy and Landsat 8 Spectral Data to Predict Soil Heavy Metals in Polluted Agricultural Land (Iran),” Arabian Journal of Geosciences, Vol. 9, pp. 745, 2016

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