Emmett Ientilucci Headshot

Emmett Ientilucci

Assistant Professor

Chester F. Carlson Center for Imaging Science
College of Science
emmett@cis.rit.edu
585-475-7778
Office Hours
By Appointment
Office Location
CAR-3120
Office Mailing Address
Emmett J. Ientilucci Rochester Institute of Technology Chester F. Carlson Center for Imaging Science 54 Lomb Memorial Drive Rochester, NY 14623

Emmett Ientilucci

Assistant Professor

Chester F. Carlson Center for Imaging Science
College of Science

Education

BS, MS, Ph.D., Rochester Institute of Technology

Bio

Dr. Emmett Ientilucci is an Assistant Professor in RIT's Chester F. Carlson Center for Imaging Science.  He works in the 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. His research interests are in General Remote Sensing, Spectral Image Processing and Exploitation, Hyperspectral Target Detection, Shadow Detection, Radiative Transfer, Radiometric Calibration, Hardware and Atmospheric Compensation.

emmett@cis.rit.edu
585-475-7778
Personal Links
Curriculum Vitae
Google Scholar
LinkedIn
Areas of Expertise
Remote Sensing
Spectral Image Processing and Exploitation
Hyperspectral Target and Shadow Detection
Radiative Transfer
Radiometric Calibrations and Associated Hardware
Atmospheric Compensation
Digital Imaging

Select Scholarship

Published Conference Proceedings
Conran, David and Emmett Ientilucci. "Defining a New Resolution Technique for Remote Imaging Sensors." Proceedings of the International Geoscience and Remote Sensing Symposium. Ed. IGARSS. Brussels, Belgium: IEEE, 2021. Print.
Wiseman, Sandra, et al. "Enhanced target Detection Under Poorly Illuminated Conditions." Proceedings of the International Geoscience and Remote Sensing Symposium. Ed. IGARSS. Brussels, Belgium: n.p., 2021. Print.
Sun, Yihang, Emmett Ientilucci, and Sophie Voisin. "Improvement of the Harris Corner Detector using an Entropy-Block-Based Strategy." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV. Ed. Miguel Velez-Reyes and David W. Messinger. Orlando, FL: SPIE, 2018. Print.
Ientilucci, Emmett. "Processing a New Hyperspectral Data set for Target Detection and Atmospheric Compensation Algorithm Assessment: The RIT2017 Data Set." Proceedings of the IGARSS 2018. Ed. José Moreno. Valencia, Spain: IEEE, 2018. Print.
Yang, Gefei, et al. "Dual-Channel DenseNET for Hyperspectral Image Classification." Proceedings of the IGARSS 2018. Ed. José Moreno. Valencia, Spain: IEEE, 2018. Print.
Ientilucci, Emmett J. "Detection of Spectrally Varing BRDF Materials in Hyperspectral Imagery." Proceedings of the Algorithms and Technologies for multispectral, hyperspectral, and ultraspectral imagery XXII. Baltimore, Maryland: SPIE, 2015. Print.
Ientilucci, Emmett J. "Target Detection for Hyperspectral Imaging with Small Sample Size." Proceedings of the Target Detection for Hyperspectral Imaging with Small Sample Size. Baltimore, Maryland: SPIE, 2015. Print.
Ientilucci, Emmett J. "Vehicle Level BRDF Roof-Top Hyperspectral Data Collection." Proceedings of the Algorithms,and technologies for multispectral, hyperspectral, and ultraspectral imagery XXII. Baltimore, Maryland: SPIE, 2015. Print.
Ientilucci, Emmett J. "Improved Atmospheric Retrievals of Hyperspectral Data using Geometric Constraints." Proceedings of the Imaging Spectrometry XX. San Diego, California: SPIE, 2015. Print.
Ientilucci, Emmett J. "Target Detection Assessment of the SHARE 2010/2012 Hyperspectral Data Collection Campaign." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXI. Baltimore, Maryland: SPIE, 2015. Print.
Ientilucci, Emmett J. "Target Detection for Hyperspectral Imaging with Small Sample Size." Proceedings of the Remote Sensing: Understanding the Earth for a Safer World. Milan, Milan: IEEE, 2015. Print.
Ientilucci, Emmett J. "Using A New GUI Tool to Leverage Lidar Data to Aid in Hyperspectral Image Material Detection in the Radiance Domain on RIT SHARE LiDAR/HSI Data." Proceedings of the Imaging Spectrometry XVIII. San Diego, California: n.p., 2013. Print.
Albano, James A., David W. Messinger, and Emmett J. Ientilucci. "Spectral Target Detection Using a Physical Model and A Manifold Learning Technique." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX. Baltimore, Maryland: n.p., 2013. Print.
Giannandrea, AnneMarie, et al. "The SHARE 2012 Data Campaign." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX. Baltimore, Maryland: n.p., 2013. Print.
Ientilucci, Emmett J. "Spectral Target Detection Using A Physical Model and A Manifold Learning Technique." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX. Baltimore, Maryland: n.p., 2013. Print.
Ientilucci, Emmett J. "SHARE 2012: Analysis of Illumination Differences on Targets in Hyperspectral Imagery." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIX. Baltimore, Maryland: n.p., 2013. Print.
Herweg, Jared, et al. "SpecTIR Hyperspectral Airborne Rochester Experiment Data Collection Campaign." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. Baltimore, Maryland: n.p., 2012. Print.
Ientilucci, Emmett J. "Leveraging Lidar Data to Aid in Hyperspectral Image Target Detection in the Radiance Domain." Proceedings of the Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XVIII. Baltimore, Maryland: n.p., 2012. Print.
Basener, William, et al. "A Detection Identification Process with Geometric Target Detection and Subpixel Spectral Visualization." Proceedings of the WHISPERS 2011. Lisbon, Portugal: n.p., 2011. Print.
Herweg, Jared, et al. "Spectral Variations in HSI Signatures of Thin Fabrics for Detecting and Tracking of Pedestrians." Proceedings of the Active and Passive Signatures II. Orlando, FL: n.p., 2011. Print.
Invited Keynote/Presentation
Ientilucci, Emmett J. "Remote Sensing and Data Acquisition." 2021 IEEE GRSS Workshop on Spatial Geoinformatics using Machine Learning Approaches. IEEE. Kolkata, India. 24 Sep. 2021. Guest Lecture.
Ientilucci, Emmett. "Remote Sensing, Spectral Target Detection, and Unmanned Aerial Systems (UAS)." IEEE Engineering Symposium. IEEE. Rochester, NY. 23 Apr. 2019. Conference Presentation.
Ientilucci, Emmett. "Atmospheric Compensation." Workshop on Advanced Machine Learning Techniques for Climate Informatics. IEEE. Kolkata, India. 4 Nov. 2019. Guest Lecture.
Ientilucci, Emmett. "Lab-based Radiometric Concepts for Undergraduate and Graduate Students." OSA ETOP Conference. Optical Society of America (OSA). Quebec, Canada. 21 May 2019. Conference Presentation.
Ientilucci, Emmett J. "Remote Sensing, Spectral Target Detection, and Unmanned Aerial Systems (UAS)." IEEE Engineering Symposium. IEEE. Rochester, NY. 23 Apr. 2019. Conference Presentation.
Ientilucci, Emmett. "Hyperspectral Data Processing and Target Detection." Remotely Sensed Big Data Analysis and Mining (RSBDAM 2018). IEEE GRSS. Kolkata, India. 33 Dec. 2018. Guest Lecture.
Ientilucci, Emmett. "Remote Sensing and Data Analysis Techniques." Guest Lecturer. National Institute of Technology (NIT). Silchar, India. 25 Jan. 2018. Guest Lecture.
Journal Paper
Jha, Sudhanshu Shekhar, Rama Rao Nidamanuri, and Emmett J. Ientilucci. "Influence of atmospheric modeling on spectral target detection through forward modeling approach in multi-platform remote sensing data." ISPRS Journal of Photogrammetry and Remote Sensing 183. (2022): 286-306. Web.
Rangnekar, Aneesh, et al. "AeroRIT: A New Scene for Hyperspectral Image Analysis." Transactions on Geoscience and Remote Sensing 58. 11 (2020): 1-9. Print.
Rangnekar, Aneesh, et al. "Uncertainty Estimation for Semantic Segmentation of Hyperspectral Imagery." Dynamic Data Driven Application Systems 12312. (2020): 163-170. Print.
Ientilucci, Emmett and Steve Adler-Golden. "Atmospheric Compensation of Hyperspectral Data." IEEE Geoscience and Remote Sensing Magazine 7. 2 (2019): 31-50. Print.
Savakis, Andreas, et al. "Change Detection in Satellite Imagery with Region Proposal Networks." Journal of the DSIAC Defense Systems Information Analysis Center 6. 4 (2019): 22-28. Web.
Yousefhussien, Mohammad, et al. "A Multi-scale Fully Convolutional Network for Semantic Labeling of 3D Point Clouds." ISPRS Journal of Photogrammetry and Remote Sensing 143. (2018): 191-204. Web.
Wang, Yue and Emmett Ientilucci. "A Practical Approach to Landsat 8 TIRS Stray Light Correction Using Multi-Sensor Measurements." Journal of Remote Sensing 10. 4 (2018): 589. Web.
Rangnekar, A., et al. "Aerial Spectral Super-Resolution using Conditional Adversarial Networks." Computer Vision and Pattern Recognition (CVPR). (2017): 1-9. Web.
Bandyopadhya, M., et al. "On the Fusion of LiDAR and Aerial Color Imagey to Detect Urban Vegetation and Buildings." J. of Photogrammetric Engineering and Remote Sensing 82. 2 (2016): 123-136. Print.
Ientilucci, Emmett J. and Emmett J. Ientilucci. "Fusion of LADAR and Hyperspectral Data with an Application in Material Identification." IEEE Geoscience and Remote Sensing Magazine. (2015): --. Print.
Ientilucci, Emmett J., et al. "Extracting Structural Vegetation Components From Small-Footprint Waveform Lidar for Biomass Estimation in Savanna Ecosystems." IEEE Journal of Selected Topics in Applied Earth Observation and Remote Sensing 7. 2 (2014): 480--490. Print.
Ientilucci, Emmett J. and Emmett J. Ientilucci. "Fusion of LADAR and Hyperspectral Data with an Application in Material Identification." IEEE GRS Magazine special issue on data fusion in remote sensing. (2014): --. Print.
Matteoli, Stefania, Emmett J. Ientilucci, and John P. Kerekes. "Operational and Performance Considerations of Radiative Transfer Modeling in Hyperspectral Target Detection." IEEE Transactions on Geoscience and Remote Sensing 49. 4 (2011): 1343-1355. Print.
Journal Editor
Ientilucci, Emmett, ed. Imaging Spectrometry. San Diego: SPIE Optical Engineering and Applications, 2019. Print.
Ientilucci, Emmett and John Silny, ed. Imaging Spectrometry XXII: Applications, Sensors, and Processing. San Diego: SPIE, 2018. Print.
Invited Paper
Ientilucci, Emmett. "Lab-based Radiometric Concepts for Undergraduate and Graduate Students." Proceedings Volume 11143, Education and Training in Optics and Photonics: ETOP 2019. (2019). Print.
Ientilucci, Emmett. "Spectral Target Detection Considerations from a Physical Modeling Perspective." IEEE IGARSS. (2017). Print.
Full Length Book
Ientilucci, Emmett J. and John R. Schott. Radiometry and Radiation Propagation. 1st ed. Rochester, NY: Oxford University Press, 2011. Print.
Published Article
Matteoli, S., E.J. Ientilucci, J.P. Kerekes. “Operational and performance considerations of radiative transfer modeling in hyperspectral target detection.” IEEE Transactions on Geoscience and Remote Sensing, (2010): 1-13. Print. £
Ientilucci, E.J., P. Bajorski, “Hyperspectral target detection in a whitened space utilizing forward modeling concepts.” Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, 1.2 (2010): n.p. Print. £
McGlinchy, J., J.A. van Aardt, H.E. Rhody, J.P. Kerekes, E.J. Ientilucci, G.P. Asner, D. Knapp, R. Mathieu, T. Kennedy-Bowdoin, B.F. Erasmus, K. Wessels, I.P. Smit, J. Wu, D. Sarrazin. “Extracting Structural Land Cover Components using Small footprint Waveform Lidar Data.” IEEE International Geoscience and Remote Sensing Symposium, 2010. n.p. Print. " 
Gillis, D., J. Bowles, E.J. Ientilucci. “Results of GLMM-based target detection on the RIT data set.” Defense and Security Symposium, April 2010. n.p. Print.
Matteoli, S., E.J. Ientilucci, J.P. Kerekes, “Characterization ofPhysics-based Radiative Transfer ModelingParameters for a Blind Test Airborne Hyperspectral Data Set.” Proceedingsof Algorithms and Technologies for Multispectral, Hyperspectral, andUltraspectral Imagery XVI, Defense and Security Symposium, 7695 (April 2010): n.p. Print.

Currently Teaching

IMGS-699
Imaging Science Graduate Co-op
0 Credits
This course is a cooperative education experience for graduate imaging science students.
IMGS-891
Continuation of Thesis
0 Credits
Continuation of Thesis
IMGS-890
Research & Thesis
1 - 6 Credits
Doctoral-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
IMGS-790
Research & Thesis
1 - 6 Credits
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.
IMGS-251
Radiometry
3 Credits
This course introduces the concepts of quantitative measurement of electromagnetic energy. The basic radiometric and photometric terms are introduced using calculus-based definitions. Governing equations for source propagation and sensor output are derived. Simple source concepts are reviewed and detector figures of merit are introduced and used in problem solving. The radiometric concepts are then applied to simple imaging systems so that a student could make quantitative measurements with imaging instruments.
IMGS-723
Remote Sensing: Spectral Image Analysis
3 Credits
This course is focused on analysis of high-dimensional remotely sensed data sets. It begins with a review of the properties of matter that control the spectral nature of reflected and emitted energy. It then introduces three mathematical ways to characterize spectral data and methods to perform initial analysis of spectral data to characterize and preprocess the data. These include noise characterization and mitigation, radiometric calibration, atmospheric compensation, dimensionality characterization, and reduction. Much of the course focuses on spectral image analysis algorithms employing the three conceptual approaches to characterizing the data. These analytical tools are aimed at segmentation, subpixel or pixel unmixing approaches and target detection including treatment of signal processing theory and application. There is also a significant emphasis on incorporation of physics based algorithms into spectral image analysis. The course concludes with an end-to-end treatment of image fidelity incorporating atmospheres, sensors, and image processing effects.
MATH-790
Research & Thesis
0 - 9 Credits
Masters-level research by the candidate on an appropriate topic as arranged between the candidate and the research advisor.

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