Sathishkumar Samiappan (Sathish)
Postdoctoral Associate

Office: HPC 207
Phone: (662) 325-4049

Box 9627
2 Research Blvd
Mississippi State, MS 39762

Sathishkumar Samiappan received B.Engg. degree in Electronics and Communication Engineering from Bharathiar University, Coimbatore, India, in 2003, M.Tech degree in Computer Science, with specialization in computer vision and image processing, from Amrita University, Coimbatore, India, in 2006 and PhD in Electrical and Computer Engineering from Mississippi State University in 2014. Until 2009, he was a Lecturer in the Department of Electronics and Communication Engineering, Amrita University, Coimbatore, India. During 2009-2014, He was a Graduate Research Assistant with Geosystems Research Institute and Graduate Teaching Assistant with the Department of Electrical and Computer Engineering at Mississippi State University.
Research Interest:
machine learning, pattern recognition, signal processing, and image processing.

Selected PublicationsTotal Publications by Sathishkumar Samiappan (Sathish):  29 
Samiappan, S., Turnage, G., Hathcock, L. A., & Moorhead, R. J. (2017). Probabilistic Neural Network and Wavelet Transform for Mapping of Phragmites Australis Using Low Altitude Remote Sensing. Proceedings of IEEE SIBGRAPI. Niteroi, Brazil. DOI:10.1109/SIBGRAPI.2017.42.

Samiappan, S., Turnage, G., McCraine, C., Hathcock, L. A., & Moorhead, R. J. (2017). Post-Logging Estimation of Loblolly Pine (Pinus Taeda) Stump Size, Area and Population Using Imagery from a Small Unmanned Aerial System. MDPI Drones. 1(1), 15. DOI:10.3390/drones1010004.

Prince Czarnecki, J. M., Samiappan, S., Wasson, L. L., McCurdy, J. D., Reynolds, D., Williams, W. P., & Moorhead, R. J. (2017). Applications of Unmanned Aerial Vehicles in Weed Science. Advances in Animal Biosciences: Precision Agriculture (ECPA) 2017. Edinburgh, Scotland. 8(2), 807-811. DOI:10.1017/S2040470017001339.

Samiappan, S., Turnage, G., Hathcock, L. A., Yao, H., Kincaid, R., Moorhead, R. J., & Ashby, S. (2017). Classifying Common Wetland Plants Using Hyperspectral Data To Identify Optimal Spectral Bands For Species Mapping Using A Small Unmanned Aerial Systems- A Case Study. Proceedings of IEEE IGARSS. Fort Worth, TX.

Moorhead, R. J., Prince Czarnecki, J. M., Samiappan, S., & Henry, W. B. (2017). Swimming in Sensors and Drowning in Data: What Is Needed for UASs to Be Effective? Proceedings Volume 10218, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II. Anaheim, CA: Society of Photo-Optical Instrumentation Engineers (SPIE). 102180L. DOI:10.1117/12.2267721. [Abstract]

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