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Congratulation to Aditya Dutt for publishing his new paper: Contrastive learning based MultiModal Alignment Network

Congratulations to our labmates and collaborators: Aditya Dutt, Alina Zare, and Paul Gader! Their paper, “Shared Manifold Learning Using a Triplet Network for Multiple Sensor Translation and Fusion with Missing Data”, was recently accepted to IEEE Journal of Selected Topics… Read More

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