Welcome new PhD student Matt Wein!

  The Machine Learning and Sensing Lab is excited to welcome Matt Wein as a new PhD student! Matt will be analyzing hyperspectral imagery for macrosystems under the Integrating Data science with Trees and Remote Sensing (IDTReeS) project. Matt’s research… Read More

Bag-level classification network for infrared target detection accepted to SPIE, 2022!

Congratulations to our labmates and collaborators: Connor H. McCurley, Daniel Rodriguez, Chandler Trousdale, Arielle Stevens, Anthony Baldino, Eugene Li, Isabella Perlmutter, and Alina Zare. Their paper, “Bag-level classification network for infrared target detection”, was recently accepted to Proc. SPIE 12096, Automatic… Read More

Addressing the Inevitable Imprecision: Multiple Instance Learning for Hyperspectral Image Analysis

Abstract: In many remote sensing and hyperspectral image analysis applications, precise ground truth information is unavailable or impossible to obtain. Imprecision in ground truth often results from highly mixed or sub-pixel spectral responses over classes of interest, a mismatch between the precision… Read More

Bag-level Classification Network for Infrared Target Detection

Abstract: Aided target detection in infrared data has proven an important area of investigation for both military and civilian applications. While target detection at the object or pixel-level has been explored extensively, existing approaches require precisely-annotated data which is often… Read More