Collecting Open-Source Resources for Hyperspectral Imaging

A team of scientists from academia and industry has worked to develop an open-source ecosystem of information related to data collection and system design for use in the advancement of hyperspectral imaging with support from the National Science Foundation (NSF). This talk will summarize the open-source resources currently available and the current landscape of applications in the field.

Issues discussed will include the current limitations and future potential of hardware, software, and data for hyperspectral imaging. Different hardware mechanisms to achieve hyperspectral imaging will be described including selected open-source designs at use in automated imaging of plants and remote sensing. Major open-source software packages, including several R and Python packages, will be summarized with description of their particular data structures and functions. Repositories of data available to the public in many different applications will be summarized, including current developments in AI methods for data analysis.

About the presenter
Yingyun Liu, Ph.D., holds the title of senior scientist and chief of the Microbiology Division at Brimrose Technology Corporation (BTC) in Sparks, Maryland. He is one of the CO-PIs of the NSF project titled "POSE: Phase II: An Open Source Hyperspectral Imaging Ecosystem" in collaboration with four universities led by Holly Rushmeier, Ph.D., of Yale University. Previously he served as the principal investigator (PI) for the SBIR project titled “Rapid viral pandemic suppression with molecular affinity development”. Before joining BTC, he worked at Johns Hopkins University in drug discovery projects targeting immunological and infectious diseases.