2D Spectral Representations and Autoencoders for Hyperspectral Imagery Classification and ExplanabilitY

Zigfried Hampel-Arias, Adra Carr, Natalie Klein, Eric Flynn

Abstract: Hyperspectral imagery comprises a rich source of remote sensing data which can be used for various analysis tasks such as target identification. Machine learning techniques allow analysts to build models that can be trained to perform material identification to high accuracy. Yet key to implementing trained classifier models is understanding on which spectral features the model relies for making decisions. Harnessing explainability methodology along with self-supervised models such as autoencoders, we can begin to probe the limits of what a classification model outputs for end users. In this work, we demonstrate the use of an autoencoder models and alternate spectral representations for contrastive explanations as an explainability method for material classification in hyperspectral imagery data.

External IDs:dblp:conf/ssiai/HampelAriasCKF24