Classification of Metageosystems by Ensembles of Machine Learning Models

Classification of Metageosystems by Ensembles of Machine Learning Models
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-9
Year of Publication : 2022
Authors : Stanislav Yamashkin, Anatoliy Yamashkin, Milan Radovanović, Marko Petrović, Ekaterina Yamashkina
DOI : 10.14445/22315381/IJETT-V70I9P226

How to Cite?

Stanislav Yamashkin, Anatoliy Yamashkin, Milan Radovanović, Marko Petrović, Ekaterina Yamashkina, "Classification of Metageosystems by Ensembles of Machine Learning Models" International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 258-268, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I9P226

Abstract
The article describes geoinformation methods and algorithms for interpreting Earth remote sensing data based on forming an ensemble of shallow classifiers based on the Ensemble Learning methodology. The proposed solution can be used to assess the stability of geosystems and predict natural processes. The difference between the created approach is determined by the new organization scheme of the metaclassifier as a decision-making unit and the use of a geosystem approach to preparing data for automated analysis through deep learning models. The article shows that the use of ensembles built according to the proposed method makes it possible to carry out an automated operational analysis of spatial data for solving the problem of the thematic mapping of metageosystems and natural processes to provide conditions for the sustainable development of regions. At the same time, combining models into an ensemble based on the proposed architecture of the metaclassifier makes it possible to increase the stability of the analyzing system: the accuracy of decisions made by the ensemble tends to the accuracy of the most efficient monoclassifier of the system.

Keywords
Machine learning, Deep learning, Artificial neural network, Spatial data, Ensembles, Sustainable development.

Reference
[1] M. F. Goodchild, “Citizens as Voluntary Sensors: Spatial Data Infrastructure in the World of Web 2.0,” International Journal of Spatial Data Infrastructures Research , vol. 2, no. 2, pp. 24-32, 2004.
[2] X. X. Zhu, D. Tuia, L. Mou, G. S. Xia, L. Zhang, F. Xu, and F. Fraundorfer, “Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources,” IEEE Geoscience and Remote Sensing Magazine, vol. 5, no. 4, pp. 8-36, 2017.
[3] L. Zhang, L. Zhang, and B. Du, “Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art,” IEEE Geoscience and Remote Sensing Magazine, vol. 4, no. 4, pp. 22-40, 2016.
[4] C. Tao, H. Pan, Y. Li, and Z. Zou, “Unsupervised Spectral–Spatial Feature Learning With Stacked Sparse Autoencoder for Hyperspectral Imagery Classification,” IEEE Geoscience and Remote Sensing Let, vol. 12, no. 12, pp. 2438-2442, 2015.
[5] Y. Lecun, Y. Bengio, and G. Hinton, “Deep Learning,” Nature, vol. 521, no. 7553, pp. 436, 2015.
[6] S. A. Yamashkin, A. A. Yamashkin, V. V. Zanozin, M. M. Radovanovic, and A. N. Barmin, “Improving the Efficiency of Deep Learning Methods in Remote Sensing Data Analysis: Geosystem Approach,” IEEE Access, vol. 8 , pp. 179516-179529, 2020.
[7] Shiji S K, Dr. S.H Krishnaveni, "An Analytical Approach for Reconstruction of Cosmetic Surgery Images Using Euclbp and Sift," SSRG International Journal of Electrical and Electronics Engineering, vol. 9, no. 8, pp. 60-71, 2022. Crossref, https://doi.org/10.14445/23488379/IJEEE-V9I8P107.
[8] W. Li, H. Liu, Y. Wang, Z. Li, Y. Jia, and G. Gui, “Deep Learning-Based Classification Methods for Remote Sensing Images in Urban Built-Up Areas,” IEEE Access, vol. 7, pp. 36274-36284, 2019.
[9] R. A. Schowengerdt, “Remote Sensing: Models and Methods for Image Processing,” 3rd Ed. Orlando, Fl, Usa: Academic Press , pp. 387-456, 2006.
[10] G. Cheng, C. Yang, X. Yao, L. Guo, and J. Han, “When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification Via Learning Discriminative Cnns,” IEEE Transactions on Geoscience and Remote Sensing , vol. 56, no. 5, pp. 2811- 2821, 2018.
[11] J. G. Lee, and M. Kang, “Geospatial Big Data: Challenges and Opportunities,” Big Data Research , vol. 2, no. 2, pp. 74-81, 2015.
[12] D. D. Cham, N. T. Son, N. Q. Minh, N. T. Thanh, and T. T. Dung, “An Analysis of Shoreline Changes Using Combined Multitemporal Remote Sensing and Digital Evaluation Model,” Civil Engineering Journal , vol. 6, no. 1, pp. 1-10, 2020.
[13] S. Damuluri, K. Islam, P. Ahmadi, and N. Qureshi, “Analyzing Navigational Data and Predicting Student Grades Using Support Vector Machine,” Emerging Science Journal, vol. 4, no. 4, pp. 243-252, 2020.
[14] S. Hammal, N. Bourahla, and N. Laouami, “Neural-Network Based Prediction of Inelastic Response Spectra,” Civil Engineering Journal, vol. 6, no. 6, pp. 1124-1135, 2020.
[15] S. A. Yamashkin, A. A. Kamaeva, A. A. Yamashkin, and E. O. Yamashkina, “Matters of Neural Network Repository Designing for Analyzing and Predicting of Spatial Processes,” International Journal of Advanced Computer Science and Applications, vol. 12, no. 5, pp. 17-22, 2021.
[16] Y. Bengio, and Y. Lecun, “Scaling Learning Algorithms Towards Ai,” Large-Scale Kernel Machines, vol. 34, no. 5, pp. 1-41, 2007.
[17] W. Zhao, and S. Du, “Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach,” IEEE Trans. Geoscience and Remote Sensing, vol. 54, no. 8, pp. 4544-4554, 2016.
[18] X. Hu, and Q. Weng, “Estimating Impervious Surfaces From Medium Spatial Resolution Imagery Using the Self-Organizing Map and Multi-Layer Perceptron Neural Networks,” Remote Sensing of Environment, vol. 113, no. 10, pp. 2089-2102, 2009.
[19] N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov, “Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data,” IEEE Geoscience and Remote Sensing Let., vol. 14, no. 5, pp. 778-782, 2017.
[20] C. Zhao, X. Li, and H. Zhu, “Hyperspectral Anomaly Detection Based on Stacked Denoising Autoencoders,” Journal of Applied Remote Sensing, vol. 11, no. 4, pp. 042605, 2017.
[21] R. Dong, X. Pan, and F. Li, “Dense U-Net-Based Semantic Segmentation of Small Objects in Urban Remote Sensing Images,” IEEE Access, vol. 7, pp. 65347-65356, 2019.
[22] W. Li, H. Fu, L. Yu, P. Gong, D. Feng, C. Li, and N. Clinton, “Stacked Autoencoder-Based Deep Learning for Remote-Sensing Image Classification: A Case Study of African Land-Cover Mapping,” International Journal of Remote Sensing, vol. 37, no. 23, pp. 5632- 5646, 2016.
[23] H. Wu, and S. Prasad, “Convolutional Recurrent Neural Networks for Hyperspectral Data Classification,” Remote Sensing, vol. 9, no. 3, pp. 298.
[24] D. Ienco, R. Gaetano, C. Dupaquier, and P. Maurel, “Land Cover Classification Via Multitemporal Spatial Data By Deep Recurrent Neural Networks,” IEEE Geoscience and Remote Sensing Let., vol. 14, no. 10, pp. 1685-1689, 2017.
[25] D. E. Kim, P. Gourbesville, and S. Y. Liong, “Overcoming Data Scarcity in Flood Hazard Assessment Using Remote Sensing and Artificial Neural Network, Smart Water,” vol. 4, no. 1, pp. 2, 2019.
[26] M. C. Shanker, M. Vadivel, "Hybrid Transfer Learning of Mammogram Images for Screening of Micro-Calcifications," SSRG International Journal of Electrical and Electronics Engineering, vol. 9, no. 8, pp. 40-47, 2022. Crossref, https://doi.org/10.14445/23488379/IJEEE-V9I8P105.
[27] S. A. Yamashkin, A. A. Yamashkin, V. V. Zanozin, M. M. Radovanovic, and A. N. Barmin, “Improving the Efficiency of Deep Learning Methods in Remote Sensing Data Analysis: Geosystem Approach,” IEEE Access, vol. 8, pp. 179516-179529, 2020.
[28] L. Miklós, E. Kočická, Z. Izakovičová, D. Kočický, A. Špinerová, A. Diviaková, and V. Miklósová, “Landscape as a Geosystem. Cham,” Switzerland: Springer, pp. 11-42, 2019.
[29] A. A. Yamashkin, S. A. Yamashkin, M. M. Radovanovic, N. S. Muchkaeva, and I. S. Lyamzina, “Development of the Regional Water Balance Regulation Concept Based on the Geosystem Approach,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 26, no. 3, pp. 1672-1683, 2022.