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Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification

Department of Computer Science and Engineering, Parala Maharaja Engineering College, Berhampur 761003, India
Amity School of Engineering and Technology, Amity University, Uttar Pradesh 201303, India
Department of Computer Applications, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Sikkim 737102, India
Directorate of Research, Sikkim Manipal University, Gangtok, Sikkim 737102, India
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Abstract

As a huge number of satellites revolve around the earth, a great probability exists to observe and determine the change phenomena on the earth through the analysis of satellite images on a real-time basis. Therefore, classifying satellite images plays strong assistance in remote sensing communities for predicting tropical cyclones. In this article, a classification approach is proposed using Deep Convolutional Neural Network (DCNN), comprising numerous layers, which extract the features through a downsampling process for classifying satellite cloud images. DCNN is trained marvelously on cloud images with an impressive amount of prediction accuracy. Delivery time decreases for testing images, whereas prediction accuracy increases using an appropriate deep convolutional network with a huge number of training dataset instances. The satellite images are taken from the Meteorological & Oceanographic Satellite Data Archival Centre, the organization is responsible for availing satellite cloud images of India and its subcontinent. The proposed cloud image classification shows 94% prediction accuracy with the DCNN framework.

Keywords

satellite imagessatellite image classificationcyclone predictionDeep Convolutional Neural Network (DCNN)featureslayersdown-sampling process

References

[1]
M. Mizutori and D. Guha-Sapir, Economic Losses, Poverty and Disasters 1998-2017. United Nations Office for Disaster Risk Reduction, United Nations, New York, USA, 2017.
[2]
P. Wallemacq and H. Rowena, Economic Losses, Poverty & Disasters: 1998–2017. Brussels, Belgium: Centre for Research on the Epidemiology of Disasters, 2018.
[3]
F. Thomalla and H. Schmuck, ‘We all knew that a cyclone was coming’: Disaster preparedness and the cyclone of 1999 in Orissa, India, Disasters, vol. 28, no. 4, pp. 373–387, 2004.
Crossref Google Scholar
[4]
L. Z. Jiang, S. M. Fu, and J. H. Sun, New method for detecting extratropical cyclones: The eight-section slope detecting method, Atmos. Ocean. Sci. Lett., vol. 13, no. 5, pp. 436–442, 2020.
Crossref Google Scholar
[5]
U. Neu, M. G. Akperov, N. Bellenbaum, R. Benestad, R. Blender, R. Caballero, A. Cocozza, H. F. Dacre, Y. Feng, K. Fraedrich, et al., IMILAST: A community effort to intercompare extratropical cyclone detection and tracking algorithms, Bull. Am. Meteorol. Soc., vol. 94, no. 4, pp. 529–547, 2013.
Crossref Google Scholar
[6]
A. Murata, S. I. I. Watanabe, H. Sasaki, H. Kawase, and M. Nosaka, The development of a resolution-independent tropical cyclone detection scheme for high-resolution climate model simulations, J. Meteorol. Soc. Japan. Ser. II, vol. 97, no. 2, pp. 519–531, 2019.
Crossref Google Scholar
[7]
K. Rajesh, V. Ramaswamy, K. Kannan, and N. Arunkumar, Satellite cloud image classification for cyclone prediction using dichotomous logistic regression based fuzzy hypergraph model, Futur. Gener. Comput. Syst., vol. 98, pp. 688–696, 2019.
Crossref Google Scholar
[8]
D. Matsuoka, M. Nakano, D. Sugiyama, and S. Uchida, Deep learning approach for detecting tropical cyclones and their precursors in the simulation by a cloud-resolving global nonhydrostatic atmospheric model, Prog. Earth Planet. Sci., vol. 5, no. 1, p. 80, 2018.
Crossref Google Scholar
[9]
S. Shakya, S. Kumar, and M. Goswami, Deep learning algorithm for satellite imaging based cyclone detection, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 13, pp. 827–839, 2020.
Crossref Google Scholar
[10]
J. G. Powers, J. B. Klemp, W. C. Skamarock, C. A. Davis, J. Dudhia, D. O. Gill, J. L. Coen, D. J. Gochis, R. Ahmadov, S. E. Peckham, et al., The weather research and forecasting model: Overview, system efforts, and future directions, Bull. Am. Meteorol. Soc., vol. 98, no. 8, pp. 1717–1737, 2017.
Crossref Google Scholar
[11]
A. C. Lorenc, Analysis methods for numerical weather prediction, Q.J.R. Meteorol. Soc., vol. 112, no. 474, pp. 1177–1194, 1986.
Crossref Google Scholar
[12]
G. E. Liston and R. A. Pielke, A climate version of the regional atmospheric modeling system, Theor. Appl. Climatol., vol. 66, nos. 1&2, pp. 29–47, 2000.
Crossref Google Scholar
[13]
R. A. Pielke, W. R. Cotton, R. L. Walko, C. J. Tremback, W. A. Lyons, L. D. Grasso, M. E. Nicholls, M. D. Moran, D. A. Wesley, T. J. Lee, et al., A comprehensive meteorological modeling system-RAMS, Meteorol. Atmos. Phys., vol. 49, nos. 1–4, pp. 69–91, 1992.
Crossref Google Scholar
[14]
M. Mu, W. S. Duan, and B. Wang, Conditional nonlinear optimal perturbation and its applications, Nonlin. Process. Geophys., vol. 10, no. 6, pp. 493–501, 2003.
Crossref Google Scholar
[15]
J. J. Baik and H. S. Hwang, Tropical cyclone intensity prediction using regression method and neural network, J. Meteorol. Soc. Japan. Ser. II, vol. 76, no. 5, pp. 711–717, 1998.
Crossref Google Scholar
[16]
M. Rütgers, S. Lee, and D. You, Typhoon track prediction using satellite images in a generative adversarial network, arXiv preprint arXiv: 1808.05382, 2018.
Google Scholar
[17]
R. Kovordányi and C. Roy, Cyclone track forecasting based on satellite images using artificial neural networks, ISPRS J. Photogramm. Remote Sens., vol. 64, no. 6, pp. 513–521, 2009.
Crossref Google Scholar
[18]
T. J. Sejnowski, The Deep Learning Revolution. Cambridge, MA, USA: MIT Press, 2018, pp. 1–10.
Crossref
[19]
H. J. Yoo, Deep convolution neural networks in computer vision-a review, IEIE Trans. Smart Process. Comput., vol. 4, no. 1, pp. 35–43, 2015.
Crossref Google Scholar
[20]
Meteorological & Oceanographic Satellite Data Archival Centre, Goverment of India, MOSDAC Data, https://www.mosdac.gov.in/satellite-catalog, 2013.
[21]
J. Salat, J. Pascual, M. Flexas, T. M. Chin, and J. Vazquez-Cuervo, Forty-five years of oceanographic and meteorological observations at a coastal station in the NW Mediterranean: A ground truth for satellite observations, Ocean Dynamics, vol. 69, no. 9, pp. 1067–1084, 2019.
Crossref Google Scholar
[22]
A. K. Rai, N. Mandal, A. Singh, and K. K. Singh, Landsat 8 OLI satellite image classification using convolutional neural network, Procedia Comput. Sci., vol. 167, pp. 987–993, 2020.
Crossref Google Scholar
[23]
A. Wimmers, C. Velden, and J. H. Cossuth, Using deep learning to estimate tropical cyclone intensity from satellite passive microwave imagery, Mon. Wea. Rev., vol. 147, no. 6, pp. 2261–2282, 2019.
Crossref Google Scholar
[24]
B. Chen, B. F. Chen, and H. T. Lin, Rotation-blended CNNs on a new open dataset for tropical cyclone image-to-intensity regression, in Proc. 24th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, London, UK, 2018, pp. 90–99.
Crossref Google Scholar
[25]
J. S. Combinido, J. R. Mendoza, and J. Aborot, A convolutional neural network approach for estimating tropical cyclone intensity using satellite-based infrared images, in 2018 24th Int. Conf. Pattern Recognition (ICPR), Beijing, China, 2018, pp. 1474–1480.
Crossref Google Scholar
[26]
C. Kar, A. Kumar, and S. Banerjee, Tropical cyclone intensity detection by geometric features of cyclone images and multilayer perceptron, SN Appl. Sci., vol. 1, no. 9, p. 1099, 2019.
Crossref Google Scholar
[27]
S. M. Chen, Y. M. Wang, and I. Tsou, Using artificial neural network approach for modelling rainfall-runoff due to typhoon, J. Earth Syst. Sci., vol. 122, no. 2, pp. 399–405, 2013.
Crossref Google Scholar
[28]
C. C. Young and W. C. Liu, Prediction and modelling of rainfall-runoff during typhoon events using a physically-based and artificial neural network hybrid model, Hydrol. Sci.J., vol. 60, no. 12, pp. 2102–2116, 2015.
Crossref Google Scholar
[29]
S. Kim, Y. Matsumi, S. Q. Pan, and H. Mase, A real-time forecast model using artificial neural network for after-runner storm surges on the Tottori coast, Japan, Ocean Eng., vol. 122, pp. 44–53, 2016.
Crossref Google Scholar
[30]
Y. F. Wang, W. Zhang, and W. Fu, Back Propogation(BP)-neural network for tropical cyclone track forecast, in Proc. 2011 19th Int. Conf. Geoinformatics, Shanghai, China, 2011, pp. 1–4.
Crossref Google Scholar
[31]
J. Y. Zhou, J. Xiang, and S. X. Huang, Classification and prediction of typhoon levels by satellite cloud pictures through GC-LSTM deep learning model, Sensors, vol. 20, no. 18, p. 5132, 2020.
Crossref Google Scholar
[32]
S. S. Roy, V. Lakshmanan, S. K. R. Bhowmik, and S. B. Thampi, Doppler weather radar based nowcasting of cyclone Ogni, J. Earth Syst. Sci., vol. 119, no. 2, pp. 183–199, 2010.
Crossref Google Scholar
[33]
S. N. K. B. Amit and Y. Aoki, Disaster detection from aerial imagery with convolutional neural network, in Proc. 2017 Int. Electronics Symp. Knowledge Creation and Intelligent Computing (IES-KCIC), Surabaya, Indonesia, 2017, pp. 239–245.
Crossref Google Scholar
[34]
G. Kim and A. P. Barros, Quantitative flood forecasting using multisensor data and neural networks, J. Hydrol., vol. 246, nos. 1–4, pp. 45–62, 2001.
Crossref Google Scholar
[35]
M. Kim, M. S. Park, J. Im, S. Park, and M. I. Lee, Machine learning approaches for detecting tropical cyclone formation using satellite data, Remote Sens., vol. 11, no. 10, p. 1195, 2019.
Crossref Google Scholar
[36]
V. F. Dvorak, Tropical cyclone intensity analysis and forecasting from satellite imagery, Mon. Wea. Rev., vol. 103, no. 5, pp. 420–430, 1975.
Crossref
[37]
B. B. Traore, B. Kamsu-Foguem, and F. Tangara, Deep convolution neural network for image recognition, Ecol. Inform., vol. 48, pp. 257–268, 2018.
Crossref Google Scholar
[38]
Y. LeCun, LeNet-5, convolutional neural networks, http://yann.lecun.com/exdb/lenet, 2015.
[39]
A. F. Agarap, Deep learning using rectified linear units (ReLU), arXiv Preprint arXiv: 1803.08375, 2018.
Google Scholar
[40]
S. Sharma, S. Sharma, and A. Athaiya, Activation functions in neural networks, Int.J. Eng. Appl. Sci. Technol., vol. 4, no. 12, pp. 310–316, 2020.
Crossref Google Scholar
[41]
I. Kouretas and V. Paliouras, Simplified hardware implementation of the softmax activation function, in Proc. 2019 8th Int. Conf. Modern Circuits and Systems Technologies (MOCAST), Thessaloniki, Greece, 2019, pp. 1–4.
Crossref Google Scholar
[42]
R. Rojas, Neural Networks-A Systematic Introduction. Berlin, Germany: Springer, 1996, pp. 149–182.
Crossref
[43]
X. Y. Deng, Q. Liu, Y. Deng, and S. Mahadevan, An improved method to construct basic probability assignment based on the confusion matrix for classification problem, Inf. Sci., vols. 340&341, pp. 250–261, 2016.
Crossref Google Scholar
[44]
R. Susmaga, Confusion matrix visualization, in Intelligent Information Processing and Web Mining, M. A. Kłpotek, S. T. Wierzchoń K. Trojanowski, eds. Berlin, Germany: Springer, 2004, pp. 107–116.
Crossref
Big Data Mining and Analytics
Volume 6 Issue 1,
March 2023
Pages 32-43
DOI: 10.26599/BDMA.2021.9020017
Cite this article:
Jena KK, Bhoi SK, Nayak SR, et al. Deep Convolutional Network Based Machine Intelligence Model for Satellite Cloud Image Classification. Big Data Mining and Analytics, 2023, 6(1): 32-43. https://doi.org/10.26599/BDMA.2021.9020017
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