A team from the New Mexico State University College of Engineering has been tasked with developing machine learning algorithms in support of national defense. Professor David Voelz has been awarded a two-year grant for nearly $300,000 from the Office of Naval Research for the project titled “Machine Learning-Based Turbulence Analysis and Mitigation for Hyperspectral Imaging.” Collaborating are Associate Professor Laura Boucheron and Assistant Professor Steven Sandoval from NMSU’s Klipsch School of Electrical and Computer Engineering. 

 “My colleagues and I at NMSU had been working for a few years on some related projects for atmospheric modeling and light propagation prediction with machine learning,” Voelz said. “At NMSU, we had expertise in image processing, machine learning and atmospheric optics, so we were a good fit for researching this question.” 

 The project’s objective is to create machine-learning algorithms to support the analysis of atmospheric turbulence effects on hyperspectral imaging and advance the tools for the mitigation of turbulence effects in the images. Voelz said they are interested in a wide spectral sensing range, from 300 nanometers to 10-micron wavelength, and imaging over horizontal or slant paths of a few hundred meters to several kilometers that are relatively near the earth’s surface.

 Defense tasks that can be helped by hyperspectral imaging include target detection, recognition and identification, shape extraction, classification and material characterization. For remote sensing tasks, hyperspectral imaging is often applied in nadir, or down-looking, ground survey applications with an aircraft or satellite. 

“Our intent is to apply machine learning algorithms to exploit the diversity provided by both the spectral and spatial data to aide in image de-blurring and un-mixing.” 

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