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- Ph. D. in Mechanical Engineering -
- B.Tech. in Artificial Intelligence (AI) and Data Science (Medical Engineering) - Undergraduate
Publication Type : Conference Proceedings
Publisher : Springer Berlin Heidelberg,
Source : Image Analysis and Recognition, Lecture notes on computer science book series, Springer Berlin Heidelberg, Volume 3212, Berlin, Heidelberg (2004)
Url : https://link.springer.com/chapter/10.1007/978-3-540-30126-4_52
ISBN : 9783540301264
Keywords : Dempster-Shafer Theory, Fisher’s discriminant, Hotelling’s T2, Markov Random Field(MRF)
Campus : Amritapuri
School : School of Arts and Sciences
Department : Mathematics
Year : 2004
Abstract : A technique has been suggested for multisensor data fusion to obtain landcover classification. It takes care of feature level fusion with Dempster-Shafer rule and data level fusion with Markov Random Field model based approach vis-a-vis for determining the optimal segmentation. Subsequently, segments are validated and classification accuracy for the test data is evaluated. Two illustrations of data fusion of optical images and a Synthetic Aperture Radar (SAR) image is presented and accuracy results are compared with those of some recent techniques in literature for the same image data.
Cite this Research Publication : A. Sarkar, Banerjee, N., Pramod P. Nair, Banerjee, A., Brahma, S., Kartikeyan, B., and Majumder, K. L., “A MRF Based Segmentatiom Approach to Classification Using Dempster Shafer Fusion for Multisensor Imagery”, Image Analysis and Recognition, Lecture notes on computer science book series, vol. 3212. Springer Berlin Heidelberg, Berlin, Heidelberg, 2004.