Dr. Emmett Ientilucci is an Associate Professor in RIT’s Digital Imaging and Remote Sensing (DIRS) group. He has degrees in optics and imaging science. Prior to his faculty position, he was a Postdoctoral Research Fellow for the Intelligence Community. He has performed research in Low-Light-Level modeling, LiDAR and physics-based target modeling, hyperspectral sub-pixel detection algorithms, and published on approaches to geometric and stochastic spectral target detection and modeling, spectral endmember selection, spectral variability, atmospheric and radiative transfer modeling, scattering from small particles, remote sensing instrumentation, sensor calibration, atmospheric compensation, and spectral BRDF measurements and modeling.
Dr. Ientilucci has taught courses and labs in radiometry, remote sensing, spectral image analysis, geometrical optics, photo science, dimensional metrology, measurement and analysis, and computer techniques for technicians. He has participated in thesis advisement for 92 undergraduate and graduate imaging science students and has 87 publications in the fields of imaging and remote sensing. He has served as referee on 18 scientific journals including being an Associate Editor for a special issue of Optical Engineering. Dr. Ientilucci is the recipient of the 2020-21 Richard and Virginia Eisenhart Provost’s Award for Excellence in Teaching at RIT. The highest award given at the university for tenured-track faculty.
He has designed and executed numerous, large scale, aerial data collection campaigns. He has written proposals resulting in more than $5.5 million in research related funding, been a program reviewer for NASA, the Department of Defense (DOD), and is Chair for the SPIE Imaging Spectrometry Conference, the WNY Geoscience & Remote Sensing Society, and the STRATUS UAS conference. He is currently working on a text book entitled, “Radiometry and Radiation Propagation” with Oxford University Press.
Dr. Ientilucci has taught courses and labs in radiometry, remote sensing, spectral image analysis, geometrical optics, photo science, dimensional metrology, measurement and analysis, and computer techniques for technicians. He has participated in thesis advisement for 92 undergraduate and graduate imaging science students and has 87 publications in the fields of imaging and remote sensing. He has served as referee on 18 scientific journals including being an Associate Editor for a special issue of Optical Engineering. Dr. Ientilucci is the recipient of the 2020-21 Richard and Virginia Eisenhart Provost’s Award for Excellence in Teaching at RIT. The highest award given at the university for tenured-track faculty.
He has designed and executed numerous, large scale, aerial data collection campaigns. He has written proposals resulting in more than $5.5 million in research related funding, been a program reviewer for NASA, the Department of Defense (DOD), and is Chair for the SPIE Imaging Spectrometry Conference, the WNY Geoscience & Remote Sensing Society, and the STRATUS UAS conference. He is currently working on a text book entitled, “Radiometry and Radiation Propagation” with Oxford University Press.
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FASSP contains three basic modules including a scene model, sensor model and a processing model. Instead of using mean surface reflectance only as input to the model, FASSP transfers user defined statistical characteristics of a scene through the image chain (i.e., from source to sensor). The radiative transfer model, MODTRAN, is used to simulate the radiative transfer based on user defined atmospheric parameters. To retrieve class emissivity and temperature statistics, or temperature / emissivity separation (TES), a LWIR atmospheric compensation method is necessary. The FASSP model has a method to transform statistics in the visible (ie., ELM) but currently does not have LWIR TES algorithm in place. This paper addresses the implementation of such a TES algorithm and its associated transformation of statistics.
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You will have access to both the presentation and article (if available).
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