Dr. Joseph Meola, PHD
Dr. Joseph Meola received the B.S. and M.S. degrees in electrical engineering from the University of Dayton, Dayton, OH in 2004 and 2006, respectively. He received the PhD degree in electrical and computer engineering from Ohio State University in 2011. He is currently the Technical Advisor for the Electro-optic Target Detection and Surveillance Branch of the Sensors Directorate of the Air Force Research Laboratory (AFRL), Wright-Patterson Air Force Base, OH. His research interests are in hyperspectral data modeling, sensor calibration and characterization, data exploitation, atmospheric modeling, and target detection.
Education
Doctor of Philosophy, Electrical Engineering, The Ohio State University, October 2008 – December 2011
Dissertation title: A model-based approach to hyperspectral change detection
Master of Science, Electrical Engineering, University of Dayton, August 2004 – May 2006
Thesis title: “Analysis of hyperspectral change detection as affected by vegetation and illumination variation”
Bachelor of Science, Electrical Engineering, University of Dayton, August 2000 – May 2004
Publications
[12] N. Westing, K. Gross, B. Borghetti, J. Martin, J. Meola, “Learning Set Representations for LWIR In-Scene Atmospheric Compensation,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. Vol. 13, pp. 1438-1449 (2020).\
[11] B. Rankin, M.T. Eismann, J. Meola, “Spectral radiance modeling and Bayesian model averaging for longwave infrared hyperspectral imagery and subpixel target identification,” IEEE Trans. On Geoscience and Remote Sensing, Vol. 55 (12), pp. 6726-6735 (December 2017)
[10] J. Vila, P. Schniter, and J. Meola, “Hyperspectral unmixing via turbo bilinear approximate message passing,” IEEE Trans. Computational Imaging, Vol. 1 (3), pp. 143-158 (September 2015)
[9] V. Alexandre, et al, “Field trial of active remote sensing using a high power short-wave infrared supercontinuum laser,” Applied Optics, vol. 52 (27), pp. 6813-6823 (2013)
[8] J.N. Ash and J. Meola “Incorporating spatial structure into Hyperspectral Scene Analysis,” IEEE Statistical Signal Processing Workshop (August 2012)
[7] J. Meola, M.T. Eismann, R.L. Moses, J.N. Ash, “Application of model-based change detection to airborne VNIR/SWIR hyperspectral imagery,” IEEE Trans. On Geoscience and Remote Sensing, Vol. 50 (10), pp. 3693-3706 (2012)
[6] J. Meola, M.T. Eismann, R.L. Moses, J.N. Ash, “Modeling and estimation of signal-dependent noise in hyperspectral imagery,” Applied Optics, Vol. 50 (21), pp. 3829-3846 (May 2011)
[5] J. Meola, M.T. Eismann, R.L. Moses, J.N. Ash, “Detecting changes in hyperspectral imagery using a model-based approach,” IEEE Trans. On Geoscience and Remote Sensing, Vol. 49 (7), pp. 2647 – 2660 (July 2011)
[4] P.C. Hytla, R.C. Hardie, M.T. Eismann, and J. Meola, “Anomaly detection in hyperspectral imagery: a comparison of methods using diurnal and seasonal data,” Journal of Applied Remote Sensing, vol. 3 (January 2010)
[3] M.T. Eismann, J. Meola, A.D. Stocker, “Automated hyperspectral target detection and change detection from an airborne platform: Progress and challenges,” IEEE IGARSS, (July 2010)
[2] M.T. Eismann, J. Meola, A.D. Stocker, S.G. Beaven, and A.P. Schaum, “Airborne hyperspectral detection of small changes,” Applied Optics, vol. 47, pp. F27-F45 (October 2008)
[1] M.T. Eismann, J. Meola, and R.C. Hardie, “Hyperspectral change detection in the presence of diurnal and seasonal variations,” IEEE Transactions on Geoscience and Remote Sensing, vol. 46, pp. 237-249 (January 2008)