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SPECTRAL VARIABILITY IN HSI ACCEPTED TO GRSM!

April 2, 2021

Congratulations to our labmates and collaborators: Ricardo Augusto Borsoi, Tales Imbiriba, Jose Carlos Moreira Bermudez, Cedric Richard, Jocelyn Chanussot, Lucas Drumets, Jean-Yves Tourneret, Alina Zare and Christian Jutten!  Their publication, “Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review” was recently accepted to the IEEE Geoscience and Remote Sensing Magezine. In their paper, the authors […]

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Multi-Target Multiple Instance Learning for Hyperspectral Target Detection

March 6, 2020

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 in the global positioning system (GPS), and mixed pixels caused by an image’s spatial resolution. […]

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Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review

January 30, 2020

Abstract: The spectral signatures of the materials contained in hyperspectral images (HI), also called endmembers (EM), can be significantly affected by variations in atmospheric, illumination or environmental conditions typically occurring within an HI. Traditional spectral unmixing (SU) algorithms neglect the spectral variability of the endmembers, what propagates significant mismodeling errors throughout the whole unmixing process […]

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