- Assistant Professor, Department of Applied Computing
- Member, Center of Data Science, Institute of Computing and Cybersystems (ICC)
- Ph.D., Electrical Engineering, University of Dayton, Dayton, OH, USA
- M.S., Electrical Engineering, University of South Alabama, Mobile, AL, USA
Dr. Sidike Paheding is an assistant professor in the Department of Applied Computing at Michigan Technological University. Prior to joining Michigan Tech in 2020, Dr. Paheding was Visiting Assistant Professor at Purdue University Northwest. He also held postdoctoral research associate and assistant research professor positions in the Remote Sensing Lab at Saint Louis University. His research interests cover a variety of topics in image/video processing, machine learning, deep learning, computer vision, and remote sensing. He has advised students at undergraduate, M.S., and Ph.D. levels, and authored/coauthored close to 100 research articles, including several top peer-review journal papers. Dr. Paheding is an associate editor of the Springer journal Signal, Image, and Video Processing, ASPRS Journal Photogrammetric Engineering & Remote Sensing, and serves as a guest editor/reviewer for a number of reputed journals. He is an invited member of Tau Beta Pi (Engineering Honor Society).
- Machine Learning
- Deep Learning
- Computer Vision
- Image and Video Processing
- Remote Sensing
Sidike, P., Sagan, V., Maimaitijiang, M., Maimaitiyiming, M., Shakoor, N., Burken, J., ... & Fritschi, F. B. (2019). dPEN: deep Progressively Expanded Network for mapping heterogeneous agricultural landscape using WorldView-3 satellite imagery. Remote Sensing of Environment, 221, 756-772. [Impact Factor: 9.085]
Maimaitijiang, M., Sagan, V., Sidike, P., Hartling, S., Esposito, F., & Fritschi, F. B. (2020). Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sensing of Environment, 237, 111599. [Impact Factor: 9.085]
Sidike, P., Sagan, V., Qumsiyeh, M., Maimaitijiang, M., Essa, A. and Asari, V., 2018. Adaptive trigonometric transformation function with image contrast and color enhancement: Application to unmanned aerial system imagery. IEEE Geoscience and Remote Sensing Letters, 15(3), pp.404-408.
Sagan, V., Peterson, K. T., Maimaitijiang, M., Sidike, P., Sloan, J., Greeling, B. A., ... & Adams, C. (2020). Monitoring inland water quality using remote sensing: potential and limitations of spectral indices, bio-optical simulations, machine learning, and cloud computing. Earth-Science Reviews, 103187. [Impact Factor: 9.724]
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M. S., ... & Asari, V. K. (2019). A state-of-the-art survey on deep learning theory and architectures. Electronics, 8(3), 292.
Hartling, S., Sagan, V., Sidike, P., Maimaitijiang, M., & Carron, J. (2019). Urban tree species classification using a WorldView-2/3 and LiDAR data fusion approach and deep learning. Sensors, 19(6), 1284.
Maimaitijiang, M., Ghulam, A., Sidike, P., Hartling, S., Maimaitiyiming, M., Peterson, K., ... & Burken, J. (2017). Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine. ISPRS Journal of Photogrammetry and Remote Sensing, 134, 43-58. [Impact Factor: 7.319]
Sidike, P., Asari, V. K., & Alam, M. S. (2015). Multiclass object detection with single query in hyperspectral imagery using class-associative spectral fringe-adjusted joint transform correlation. IEEE Transactions on Geoscience and Remote Sensing, 54(2), 1196-1208. [Impact Factor: 5.855]