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EXPLAINABLE SYSTEMATIC ANALYSIS FOR SYNTHETIC APERTURE SONAR IMAGERY

Abstract: In this work, we present an in-depth and systematic analysis using tools such as local interpretable model-agnostic explanations (LIME) and divergence measures to analyze what changes lead to improvement in performance in fine tuned models for synthetic aperture sonar… Read More

THE WEAKLY-LABELED RAND INDEX

Abstract: Synthetic Aperture Sonar (SAS) surveys produce imagery with large regions of transition between seabed types. Due to these regions, it is difficult to label and segment the imagery and, furthermore, challenging to score the image segmentations appropriately. While there… Read More

CONGRATULATIONS TO CONNOR MCCURLEY FOR BECOMING A PHD CANDIDATE!

Congratulations to our labmate, Connor McCurley, for passing his oral qualifying exam and becoming a PhD candidate!  For the remainder of his Ph.D. work, Connor plans to investigate “Discriminative Manifold Embedding with Imprecise, Uncertain and Ambiguous Data.” Great work, Connor!

A REMOTE SENSING DERIVED DATA SET OF 100 MILLION INDIVIDUAL TREE CROWNS FOR THE NATIONAL ECOLOGICAL OBSERVATORY NETWORK

Abstract: Forests provide biodiversity, ecosystem, and economic services. Information on individual trees is important for understanding forest ecosystems but obtaining individual-level data at broad scales is challenging due to the costs and logistics of data collection. While advances in remote… Read More