Efficient Classification of Satellite Image with Hybrid Approach Using CNN-CA


  • S. Poonkuntran School of Computing Science and Engineering, VIT Bhopal University, India
  • V. Abinaya Velammal College of Engineering and Technology, Madurai, India
  • S. Manthira Moorthi Indian Space Research Organization Space Application Centre, Ahemedabad, India
  • M P Oza Indian Space Research Organization Space Application Centre, Ahemedabad, India




Soft computing, Satellite image, CNN, Cellular automata and classification


Today, satellite imagery is being utilized to help repair and restore societal issues caused by habitats for a variety of scientific studies. Water resource search, environmental protection simulations, meteorological analysis, and soil class analysis may all benefit from the satellite images. The categorization algorithms were used generally and the most appropriate strategies are also be used for analyzing the Satellite image. There are several normal classification mechanisms, such as optimum likelihood, parallel piping or minimum distance classification that have presented in some other existing technologies. But the traditional classification algorithm has some disadvantages. Convolutional neural network (CNN) classification based on CA was implemented in this article. Using the gray level Satellite image as the target and CNN image classification by the CA’s selfiteration mechanism and eventually explores the efficacy and viability of the proposed method in long-term satellite remote sensing image water body classification. Our findings indicate that the proposed method not only has rapid convergence speed, reliability but can also efficiently classify satellite remote sensing images with long-term sequence and reasonable applicability. The proposed technique acquires an accuracy of 91% which is maximum than conventional methods.


Dhingra, S., & Kumar, D. (2019). A review of remotely sensed satellite image classification. International Journal of Electrical & Computer Engineering (2088-8708), 9(3).


Kumar, R., & Sharma, P. K. (2019). Classification Techniques for Object Detection in Remote Sensing Images. Available at SSRN 3356495.


Das, P., & Pandey, V. (2019). Use of Logistic Regression in Land-Cover Classification with Moderate-Resolution Multispectral Data. Journal of the Indian Society of Remote Sensing, 47(8), 1443-1454.


Barde, I., Suryawanshi, N., Samarth, S., Bhure, T., Rehpade, N. N., & Balamwar, S. Object Based Classification Using Image Processing Techniques.

Borra, S., Thanki, R., & Dey, N. (2019). Satellite Image Classification. In Satellite Image Analysis: Clustering and Classification (pp. 53-81). Springer, Singapore.


Tuba, E., Jovanovic, R., & Tuba, M. (2020). Multispectral Satellite Image Classification Based on Bare Bone Fireworks Algorithm. In Information and Communication Technology for Sustainable Development (pp. 305-313). Springer, Singapore.


Mohammed, M. A., Naji, T. A., & Abduljabbar, H. M. (2019). The effect of the activation functions on the classification accuracy of satellite image by artificial neural network. Energy Procedia, 157, 164-170.


Yu, L., Lan, J., Zeng, Y., & Zou, J. (2019). Comparison of Land Cover Types Classification Methods Using Tiangong-2 Multispectral Image. In Proceedings of the Tiangong-2 Remote Sensing Application Conference (pp. 241-253). Springer, Singapore.


Ishii, T.; Nakamura, R.; Nakada, H.; Mochizuki, Y.; Ishikawa, H. (2015). Surface object recognition with CNN andSVM in Landsat 8 images. In Proceedings of the IEEE 14th IAPR International Conference on MachineVision Applications (MVA), Tokyo, Japan, 18-22 May 2015; pp. 341-344.


Espinola, M., Piedra-Fernandez, J. A., Ayala, R., Iribarne, L., & Wang, J. Z, (2014). "Contextual and Hierarchical Classification of Satellite Images Based on Cellular Automata", IEEE Transactions on Geoscience and Remote Sensing, 53(2), 795-809.


M. Espnola et al., (2010). Cellular automata applied in remote sensing to implement contextual pseudo-fuzzy classification, in Proc. 9th Int. Conf. ACRI, vol. 6350.


SarikaYadav, Imdad Rizvi, ShailajaKadam, Luis Iribarne, and James Z. Wang, (2015). Urban Tree Canopy Detection Using Object-Based Image Analysis for Very High Resolution Satellite Images IEEE Trans on geosciences and remote sensing.


F. S. Al-Ahmadi and A. S. Hames, (2009). , "Comparison of Four Classification Methods to Extract Land Use and Land Cover from Raw Satellite Images for Some Remote Arid Areas, Kingdom of Saudi Arabia", JKAU; Earth Sci., Vol. 20 No.1, pp: 167-191 (A.D./1430 A.H.)


D. Menaka, L. Padmasuresh and S. SelvinPrem Kumar, 2015. "Classification of Multispectral Satellite Images using Sparse SVM Classifier", Indian Journal of Science and Technology, Vol. 8, No. 24.


DM.Längkvist, A. Kiselev, M. Alirezaie and A.Loutfi 2016. Classification and segmentation of satellite orthoimagery using convolutional neural networks. Remote Sensing, 8(4), 329.


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