An Implementation of Satellite Image Classification and Analysis using Machine Learning with ISRO LISS IV

Diksha Naik, Apurva Sawarbhande, Barkha Deogade, Pooja Dupare, Pooja Khodke, Vandana S. Choubey
International Journal of Computational and Electronic Aspects in Engineering
Volume 2: Issue 2, June 2021, pp 1-9

Author's Information
Diksha Naik1 
Corresponding Author
1Student, Department of Computer Engineering, Smt. Radhikatai Pandav College of Engineering, Nagpur, India
naikgdiksha338@gmail.com

 Apurva Sawarbhande1, Barkha Deogade1, Pooja Dupare1, Pooja Khodke1
1Student, Department of Computer Engineering, Smt. Radhikatai Pandav College of Engineering, Nagpur, India

 Vandana S. Choubey2
2Assistant Professor, Department of Computer Engineering, Smt. Radhikatai Pandav College of Engineering, Nagpur, India

Research Article -- Peer Reviewed
First online on – 8 June 2021

Open Access article under Creative Commons License

Cite this article –Diksha Naik, Apurva Sawarbhande, Barkha Deogade, Pooja Dupare, Pooja Khodke, Vandana S. Choubey “An Implementation of Satellite Image Classification and Analysis using Machine Learning with ISRO LISS IV”, International Journal of Computational and Electronic Aspects in Engineering, RAME Publishers, vol. 2, issue 2, pp. 1-9, 2021.
https://doi.org/10.26706/ijceae.2.2.20210404

Abstract:-
Image category is a complicated system that can be stricken by many factors. This paper examines modern-day practices, problems, and potentialities of image category. The emphasis is positioned at the summarization of primary advanced category procedures and the strategies used for enhancing category accuracy. In addition, a few essential problems affecting category performance are discussed. This literature evaluate indicates that designing an appropriate image processing system is a prerequisite for a a hit category of remotely sensed records right into a thematic map. Effective use of a couple of functions of remotely sensed records and the choice of a appropriate category approach are especially extensive for enhancing category accuracy. Non-parametric classifiers such as neural network, choice tree classifier, and knowledge-primarily based totally category have an increasing number of turn out to be essential procedures for multi-source records classification. Integration of faraway sensing, geographical data systems (GIS), and professional machine emerges as a brand-new studies frontier. More studies, however, is had to discover and decrease uncertainties with inside the image-processing chain to enhance category accuracy.
Index Terms:-
Artificial Neural Network (ANN), Machine Learning, Sensor LISS IV, Remote Sensing
REFERENCES
  1. Shifali, “Satellite image classification using back propagation neural network,” Indian Journal of Science and Technology, vol. 9, issue 45, December 2016.
  2. G. Soumadip, “A tutorial on different classification techniques for remotely sensed imagery datasets,” Smart Computing Review, vol. 4, no. 1, February 2014.
  3. S. Praveena, “Hybrid clustering algorithm and feed-forward neural network for satellite image classification,” International Journal of Engineering Science Invention, vol. 3, issue 1, pp. 39-47, January 2014.
  4. Shabnam Jabari and Yun Zhang, 2013. “Very High-Resolution Satellite Image Classification Using Fuzzy Rule-Based Systems”, Algorithms, vol.6, no.4, pp. 762- 781.
  5. Bjorn Frohlich., Eric Bach., Irene Walde., Soren Hese., Christiane Schmullius, and Joachim Denzler. “Land Cover Classification of Satellite Images using Contextual Information”, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume II-3/W1, pp. 1-6, 2012.
  6. G. Ariputhiran, “Feature extraction and classification of high-resolution satellite images using GLCM and back propagation technique,” International Journal of Engineering and Computer Science, vol. 2, issue 2, pp. 525-528, Feb 2013.
  7. R. Kadhum, “Image noise reduction using back propagation neural network,” M.S.C Thesis, University of Baghdad, College of Science, computer science, Iraq, 2001.
  8. O. S. Eluyode, “Comparative study of biological and artificial neural networks,” European Journal of Applied Engineering and Scientific Research, vol. 2, issue 1, pp. 36-46, 2013.
  9. M. Reginald, “Multi-layer perceptron error surfaces: Visualization, structure and modelling,” Phd Thesis, University of Queensland, Department of Computer Science and Electrical Engineering, Australia, 1999 (Revised January, 2000).
  10. A. Salihah, “Modified global and modified linear contrast stretching algorithms: New colour contrast enhancement techniques for microscopic analysis of malaria slide images,” Hindawi Publishing Corporation Computational and Mathematical Methods in Medicine, vol. 2012, article ID 637360, pp. 1-16, 2012.

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