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


Affiliations
1 Student, Department of Computer Engineering, Smt. Radhikatai Pandav College of Engineering, Nagpur, India
2 Assistant Professor, Department of Computer Engineering, Smt. Radhikatai Pandav College of Engineering, Nagpur, India
 

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

Abstract Views: 163  |  PDF Views: 128

Authors

Diksha Naik, Apurva Sawarbhande, Barkha Deogade, Pooja Dupare Pooja Khodke
Student, Department of Computer Engineering, Smt. Radhikatai Pandav College of Engineering, Nagpur, India
Vandana S. Choubey
Assistant Professor, Department of Computer Engineering, Smt. Radhikatai Pandav College of Engineering, Nagpur, India

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.

References