Sidike Paheding

Contact

spahedin@mtu.edu
EERC 417

Assistant Professor, Applied Computing
Affiliated Assistant Professor, Computer Science
Affiliated Faculty, Computational Science & Engineering; ICC Center for Data Sciences

Ph.D., Electrical Engineering, University of Dayton, Ohio

Biography

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 is currently directing Machine Intelligence Lab (MainLab) at Michigan Tech and working on a variety of topics in machine learning, deep learning, computer vision and remote sensing.

Dr. Paheding has authored/coauthored over 100 research articles, and he is one of the recipients of the 2020 Best Paper Award from the MDPI journal Electronics and 2021 Best Paper from journal Remote Sensing, as well as the Best Paper award in ISPRS Geospatial Week 2019. Dr. Paheding serves as an associate editor of the Springer journal Signal, Image, and Video Processing, and ASPRS Journal Photogrammetric Engineering & Remote Sensing. He is also an invited member of Tau Beta Pi (Engineering Honor Society) and members of IEEE and SPIE.

Areas of Expertise:

Machine Learning
Deep Learning
Computer Vision
Image and Video Processing
Hyperspectral Image Analysis
Remote Sensing

Refereed Publications

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 (IF):13.85]
Sagan, V., Maimaitijiang, M., Paheding, S., Bhadra, S., Gosselin, N., Burnette, M., ... & Mockler, T. C. (2021). "Data-Driven Artificial Intelligence for Calibration of Hyperspectral Big Data," in IEEE Transactions on Geoscience and Remote Sensing. [IF: 8.125]
Paheding, S., Reyes, A.A., Kasaragod, A. and Oommen, T., (2022). GAF-NAU: Gramian Angular Field encoded Neighborhood Attention U-Net for Pixel-Wise Hyperspectral Image Classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) workshop (pp. 409-417).
Siddique, N., Paheding, S., Angulo, A. A. R., Alom, M. Z., & Devabhaktuni, V. K. (2022). Fractal, recurrent, and dense U-Net architectures with EfficientNet encoder for medical image segmentation. Journal of Medical Imaging, 9(6), 064004.
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. [IF: 12.038]
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. [IF: 5.343]