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
Tag: target detection
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
MT_eFUMI code is now available!
MATLAB implementation of Multi-target Extended Functions of Multiple Instances has been made public! It is available in our GitHub repository MT_eFUMI MT_eFUMI is capable of learning multiple target spectral signatures from weakly- and inaccurately-labeled hyperspectral imagery. It is a generalization… Read More
Congratulations to Yiming Cui for a Successful Proposal Defense!
Congratulations to our labmate Yiming Cui for successfully defending his research proposal! Defending an oral research proposal is the second of four milestones to completing a Ph.D. at the University of Florida. Yiming is planning to conduct point cloud semantic… Read More
MULTI-TARGET MI-ACE ACCEPTED TO TGRS!
Congratulations to our labmates: Susan K. Meerdink, James Bocinsky, Alina Zare, Nick Kroeger, Connor H. McCurley, Daniel Shats and Paul D. Gader! Their paper, “Multi-Target Multiple Instance Learning for Hyperspectral Target Detection” was recently accepted to IEEE Transactions on Geoscience… Read More
MIMRF Published in TGRS!
Congratulations to GatorSense alumna, Xiaoxiao Du! Her paper, titled “Multi-resolution Multi-modal Sensor Fusion For Remote Sensing Data with Label Uncertainty”, was recently published in IEEE Transactions on Geoscience and Remote Sensing. Check it out here!
Multi-Target Multiple Instance Learning for Hyperspectral Target Detection
Abstract: In remote sensing, it is often challenging to acquire or collect a large dataset that is accurately labeled. This difficulty is usually due to several issues, including but not limited to the study site’s spatial area and accessibility, errors… Read More
Master’s Defenses!
Congratulations to our labmates, Hudanyun Sheng and Princess Lyons, for successful Master’s defenses! Hudanyun conducted work on “Switchgrass Genotype Classification using Hyperspectral Imagery”, while Princess investigated “Anomaly and Target Detection in Synthetic Aperture Sonar”. Great job, you two!
Cross-site learning in deep learning RGB tree crown detection
Abstract: Tree detection is a fundamental task in remote sensing for forestry and ecosystem ecology applications. While many individual tree segmentation algorithms have been proposed, the development and testing of these algorithms is typically site specific, with few methods evaluated… Read More