Dr. Eric David Jansing Profile

Dr. Eric David Jansing

Principal Remote Sensing Scientist

SPIE Involvement:

Author

Area of Expertise:

data analysis , machine learning , hyperspectral , remote sensing , synthetic aperture radar , deep learning

Websites:

Company Website

Significant experience in algorithm development for detection and extraction of optical and electromagnetic signatures, particularly in infrared (IR), hyperspectral imaging (HSI) and synthetic aperture radar (SAR). More than 15 years experience in signal and image processing, data analysis, interpretation, modeling, optimization, and evolutionary computation. Extensive experience in analysis, test planning and management. Heavily involved in developing new business. Awarded FY2008 R. W. Hart Prize for Excellence in Independent Research and Development for the Exploitation of Synthetic Aperture Data Products IR&D. Also awarded 2011 Distinguished Alumni award from the University of Louisville Speed Scientific School for the Electrical and Computer Engineering Department. Extensive teaching experience, including full semester courses as well as short technical development courses.

Proceedings Article | 14 May 2019 Presentation + Paper

Applications of spectral image quality equation for longwave infrared hyperspectral imagery

Blake Rankin, Joshua Broadwater, E. David Jansing

Proceedings Volume 10986, 1098604 (2019) https://doi.org/10.1117/12.2518222

KEYWORDS: Long wavelength infrared, Image quality, Infrared imaging, Infrared radiation, Sensors, Calibration, Hyperspectral imaging, Algorithm development, Black bodies

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Proceedings Article | 17 May 2016 Paper

Comparing performance of standard and iterative linear unmixing methods for hyperspectral signatures

Travis Gault, Melissa Jansen, Mallory DeCoster, E. David Jansing, Benjamin Rodriguez

Proceedings Volume 9840, 984027 (2016) https://doi.org/10.1117/12.2224060

KEYWORDS: Monte Carlo methods, Iterative methods, Databases, Computer simulations, Long wavelength infrared, Sensors, Applied physics, Algorithm development, Spectroscopy, Electroluminescent displays

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Linear unmixing is a method of decomposing a mixed signature to determine the component materials that are present in sensor’s ﬁeld of view, along with the abundances at which they occur. Linear unmixing assumes that energy from the materials in the ﬁeld of view is mixed in a linear fashion across the spectrum of interest. Traditional unmixing methods can take advantage of adjacent pixels in the decomposition algorithm, but is not the case for point sensors. This paper explores several iterative and non-iterative methods for linear unmixing, and examines their eﬀectiveness at identifying the individual signatures that make up simulated single pixel mixed signatures, along with their corresponding abundances. The major hurdle addressed in the proposed method is that no neighboring pixel information is available for the spectral signature of interest. Testing is performed using two collections of spectral signatures from the Johns Hopkins University Applied Physics Laboratory’s Signatures Database software (SigDB): a hand-selected small dataset of 25 distinct signatures from a larger dataset of approximately 1600 pure visible/near-infrared/short-wave-infrared (VIS/NIR/SWIR) spectra. Simulated spectra are created with three and four material mixtures randomly drawn from a dataset originating from SigDB, where the abundance of one material is swept in 10% increments from 10% to 90%with the abundances of the other materials equally divided amongst the remainder. For the smaller dataset of 25 signatures, all combinations of three or four materials are used to create simulated spectra, from which the accuracy of materials returned, as well as the correctness of the abundances, is compared to the inputs. The experiment is expanded to include the signatures from the larger dataset of almost 1600 signatures evaluated using a Monte Carlo scheme with 5000 draws of three or four materials to create the simulated mixed signatures. The spectral similarity of the inputs to the output component signatures is calculated using the spectral angle mapper. Results show that iterative methods signiﬁcantly outperform the traditional methods under the given test conditions.

Course Instructor

NON-SPIE: Synthetic Aperture Radar (JHU, Whiting EN 525.748)

This course covers the basics of synthetic aperture radar (SAR). In particular, the course will examine why there are limiting design considerations for real aperture radar, and how a synthetic aperture can overcome these limitations to create high-resolution radar imaging. Stripmap and spotlight SAR will be compared and contrasted. Spotlight SAR technology will be compared to computerized axial tomography (CAT). Signal processing of the SAR data will be covered, including motion compensation, Doppler beam-sharpening, polar formatting, aperture weighting (or apodization), and autofocus. Advanced topics will include interferometric processing of SAR data, a brief overview of bi-static SAR, moving targets in SAR, and the difficulty in estimating motion of targets in single-channel SAR. Students will work through problems involving radar and synthetic aperture radar processing. Over the life of the course, each student will develop a SAR simulator that will generate synthetic data based on simple point scatterers in a benign background. The simulator will include an image formation processor, based on modules built by the student.

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