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Debabrata Samanta

Work place: Department of CSE, National Institute of Technology, Durgapur, Mahatma Gandhi Avenue, West Bengal, 713209, India

E-mail: debabrata.samanta369@gmail.com

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Research Interests: Artificial Intelligence, Natural Language Processing, Image Compression, Image Manipulation, Image Processing, Data Structures and Algorithms

Biography

Debabrata Samanta is a member of the IAENG, Board member of the Seventh Sense Research Group Journals (SSRGJ), member of Editorial Board of IJSCE. He obtained his B.Sc. (Physics Honors) in the year 2007, from the Vivekananda Collage, Takurpukur, under Calcutta University; Kolkata, India .He obtained his MCA in the year 2010, from the Academy Of Technology, under WBUT. He is working his PhD in Computer Science and Engg. In the year 2010 from National Institute of Technology, Durgapur, India in the area of Image Processing .He is presently working as an Asst. Professor of Dept of Computer Application, Burdwan Institute of Management and Computer Science, Burdwan, West Bengal, India. His areas of interest are Artificial Intelligence, Natural Language Processing and Image Processing. He has guided 7 PG and 25 UG thesis. He has published 36 papers in International Journals / Conferences.

Author Articles
Classification of SAR Images Based on Entropy

By Debabrata Samanta Goutam Sanyal

DOI: https://doi.org/10.5815/ijitcs.2012.12.09, Pub. Date: 8 Nov. 2012

SAR image classification is the progression of separating or grouping an image into different parts. The good feat of recognition algorithms based on the quality of classified image. The good recital of recognition algorithms depend on the quality of classified image. The proposed classification method is hierarchical: classes which are difficult to distinguish are grouped.An important problem in SAR image application is accurate classification. In this paper, we developed a new methodology of SAR image Classification by Entropy. The severance between different groups or classes is based on logistic and multi-nominal regression, which finds the best combination of features to make the separation and at the same time perform a feature selection depending on Grouped –Entropy value.

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