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Big Data Mining and Analytics 2023, 6(1): 44-54 https://doi.org/10.26599/BDMA.2022.9020027
Open Access | Issue | Published: 24 November 2022

Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural Network

Show Author's Information Amit Kumar Rai1,2( ),  Nirupama Mandal1,  Krishna Kant Singh3,  Ivan Izonin4
Department of Electronics Engineering, Indian Institute of Technology, Dhanbad (ISM, Dhanbad), Dhanbad 826004, India
Department of Electronics and Communication Engineering, Asansol Engineering College, Asansol 713305, India
Department of CSE, ASET, Amity University, Noida 201301, India
Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv 79000, Ukraine
Keywords:
classification, planning, change detection, Radial Basis Function Neural Network (RBFNN), Manta Ray Foraging Optimization algorithm (MRFO), Landsat 8, disaster mitigation
Cite this article:
Rai AK, Mandal N, Singh KK, et al. Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural Network. Big Data Mining and Analytics, 2023, 6(1): 44-54. https://doi.org/10.26599/BDMA.2022.9020027
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Abstract Full text About this article

A semi supervised image classification method for satellite images is proposed in this paper. The satellite images contain enormous data that can be used in various applications. The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data. Thus, in this paper, a Radial Basis Function Neural Network (RBFNN) trained using Manta Ray Foraging Optimization algorithm (MRFO) is proposed. RBFNN is a three-layer network comprising of input, output, and hidden layers that can process large amounts. The trained network can discover hidden data patterns in unseen data. The learning algorithm and seed selection play a vital role in the performance of the network. The seed selection is done using the spectral indices to further improve the performance of the network. The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays. It emulates three unique foraging behaviours namelys chain, cyclone, and somersault foraging. The satellite images contain enormous amount of data and thus require exploration in large search space. The spiral movement of the MRFO algorithm enables it to explore large search spaces effectively. The proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager (OLI) images of New Brunswick area. The method was applied to identify and classify the land cover changes in the area induced by flooding. The images are classified using the proposed method and a change map is developed using post classification comparison. The change map shows that a large amount of agricultural area was washed away due to flooding. The measurement of the affected area in square kilometres is also performed for mitigation activities. The results show that post flooding the area covered by water is increased whereas the vegetated area is decreased. The performance of the proposed method is done with existing state-of-the-art methods.


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Satellite Image Classification Using a Hybrid Manta Ray Foraging Optimization Neural Network

Show Author's information Amit Kumar Rai1,2( ), Nirupama Mandal1, Krishna Kant Singh3, Ivan Izonin4
Department of Electronics Engineering, Indian Institute of Technology, Dhanbad (ISM, Dhanbad), Dhanbad 826004, India
Department of Electronics and Communication Engineering, Asansol Engineering College, Asansol 713305, India
Department of CSE, ASET, Amity University, Noida 201301, India
Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv 79000, Ukraine

Abstract

A semi supervised image classification method for satellite images is proposed in this paper. The satellite images contain enormous data that can be used in various applications. The analysis of the data is a tedious task due to the amount of data and the heterogeneity of the data. Thus, in this paper, a Radial Basis Function Neural Network (RBFNN) trained using Manta Ray Foraging Optimization algorithm (MRFO) is proposed. RBFNN is a three-layer network comprising of input, output, and hidden layers that can process large amounts. The trained network can discover hidden data patterns in unseen data. The learning algorithm and seed selection play a vital role in the performance of the network. The seed selection is done using the spectral indices to further improve the performance of the network. The manta ray foraging optimization algorithm is inspired by the intelligent behaviour of manta rays. It emulates three unique foraging behaviours namelys chain, cyclone, and somersault foraging. The satellite images contain enormous amount of data and thus require exploration in large search space. The spiral movement of the MRFO algorithm enables it to explore large search spaces effectively. The proposed method is applied on pre and post flooding Landsat 8 Operational Land Imager (OLI) images of New Brunswick area. The method was applied to identify and classify the land cover changes in the area induced by flooding. The images are classified using the proposed method and a change map is developed using post classification comparison. The change map shows that a large amount of agricultural area was washed away due to flooding. The measurement of the affected area in square kilometres is also performed for mitigation activities. The results show that post flooding the area covered by water is increased whereas the vegetated area is decreased. The performance of the proposed method is done with existing state-of-the-art methods.

Keywords: classification, planning, change detection, Radial Basis Function Neural Network (RBFNN), Manta Ray Foraging Optimization algorithm (MRFO), Landsat 8, disaster mitigation

References(22)

[1]
K. K. Singh and A. Singh, Detection of 2011 Sikkim earthquake-induced landslides using neuro-fuzzy classifier and digital elevation model, Natural Hazards, vol. 83, no. 2, pp. 1027–1044, 2016.
DOI Google Scholar
[2]
N. Kheradmandi and V. Mehranfar, A critical review and comparative study on image segmentation-based techniques for pavement crack detection, Construction and Building Materials, vol. 321, p. 126162, 2022.
DOI Google Scholar
[3]
K. Liyanage and B. M. Whitaker, Feature analysis in satellite image classification using LC-KSVD and frozen dictionary learning, in Proc. 2022 Intermountain Engineering, Technology and Computing (IETC), Orem, UT, USA, 2022, pp. 1–6.
DOI Google Scholar
[4]
K. K. Singh, M. J. Nigam, K. Pal, and A. Mehrotra, A fuzzy Kohonen local information C-means clustering for remote sensing imagery, IETE Technical Review, vol. 31, no. 1, pp. 75–81, 2014.
DOI Google Scholar
[5]
S. Gxokwe, T. Dube, D. Mazvimavi, and M. Grenfell, Using cloud computing techniques to monitor long-term variations in ecohydrological dynamics of small seasonally-flooded wetlands in semi-arid South Africa, Journal of Hydrology, vol. 612, p. 128080, 2022.
DOI Google Scholar
[6]
N. Mori, T. Takahashi, T. Yasuda, and H. Yanagisawa, Survey of 2011 Tohoku earthquake tsunami inundation and run-up, Geophysical Research Letters, vol. 38, no. 7, pp. 1–6, 2011.
DOI Google Scholar
[7]
G. Amarnath, An algorithm for rapid flood inundation mapping from optical data using a reflectance differencing technique, Journal of Flood Risk Management, vol. 7, no. 3, pp. 239–250, 2013.
DOI Google Scholar
[8]
S. M. Chignell, R. S. Anderson, P. Evangelista, M. J. Laituri, and D. M. Merritt, Multi-temporal independent component analysis and Landsat 8 for delineating maximum extent of the 2013 colorado front range flood, Remote Sensing, vol. 7, no. 8, pp. 9822–9843, 2015.
DOI Google Scholar
[9]
W. K. Mun and L. Billa, Post-flood land use damage estimation using improved normalized difference flood index (NDFI3) on Landsat 8 datasets: December 2014 floods, Kelantan, Malaysia, Arabian Journal of Geosciences, vol. 11, no. 15, p. 434, 2018.
DOI Google Scholar
[10]
D. Lu and Q. Weng, A survey of image classification methods and techniques for improving classification performance, International Journal of Remote Sensing, vol. 28, no. 5, pp. 823–870, 2007.
DOI Google Scholar
[11]
L. Zhu, F. -L. Chung, and S. Wang, Generalized fuzzy C-means clustering algorithm with improved fuzzy partitions, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), vol. 39, no. 3, pp. 578–591, 2009.
DOI Google Scholar
[12]
P. Wang, Pattern recognition with fuzzy objective function algorithms (James C. Bezdek), SIAM Review, vol. 25, no. 3, p. 442, 2013.
DOI Google Scholar
[13]
K. K. Singh, M. J. Nigam, and K. Pal, Detection of 2011 Tohoku tsunami inundated areas in Ishinomaki city using generalized improved fuzzy Kohonen clustering network, European Journal of Remote Sensing, vol. 47, no. 1, pp. 461–475, 2014.
DOI Google Scholar
[14]
A. Singh and K. K. Singh, Unsupervised change detection in remote sensing images using fusion of spectral and statistical indices, The Egyptian Journal of Remote Sensing and Space Science, vol. 21, no. 3, pp. 345–351, 2018.
DOI Google Scholar
[15]
F. Schwenker, H. A. Kestler, and G. Palm, Three learning phases for radial-basis-function networks, Neural Networks, vol. 14, nos. 4&5, pp. 439–458, 2001.
DOI Google Scholar
[16]
W. Zhao, Z. Zhang, and L. Wang, Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications, Engineering Applications of Artificial Intelligence, vol. 87, p. 103300, 2020.
DOI Google Scholar
[17]
USGS, http://earthexplorer.usgs.gov, 2022.
[18]
J. W. Rouse Jr., R. H. Haas, J. A. Schell, and D. W. Deering, Monitoring vegetation systems in the Great Plains with ERTS, in Third Earth Resources Technology Satellite-1 Symposium-Volume I: Technical Presentations, NASA SP-351, S. C. Freden, E. P. Mercanti, and M. A. Becker, eds. Washington, DC, USA: NASA, 1974, pp. 309–317.
[19]
A. R. Huete, A soil-adjusted vegetation index (SAVI), Remote Sensing of Environment, vol. 25, no. 3, pp. 295–309, 1988.
DOI Google Scholar
[20]
H. Xu, Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery, International Journal of Remote Sensing, vol. 27, no. 14, pp. 3025–3033, 2006.
DOI Google Scholar
[21]
Y. Zha, J. Gao, and S. Ni, Use of normalized difference built-up index in automatically mapping urban areas from TM imagery, International Journal of Remote Sensing, vol. 24, no. 3, pp. 583–594, 2003.
DOI Google Scholar
[22]
R. G. Congalton and K. Green, Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition. Boca Raton, FL, USA: CRC Press, 2008.
DOI
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Received: 15 May 2022
Revised: 11 July 2022
Accepted: 18 July 2022
Published: 24 November 2022
Issue date: March 2023

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