Transforming Hyperspectral Images Into Chemical Maps: An End-to-End Deep Learning Approach
Journal:
arXiv
Published Date:
Apr 19, 2025
Abstract
Current approaches to chemical map generation from hyperspectral images are
based on models such as partial least squares (PLS) regression, generating
pixel-wise predictions that do not consider spatial context and suffer from a
high degree of noise. This study proposes an end-to-end deep learning approach
using a modified version of U-Net and a custom loss function to directly obtain
chemical maps from hyperspectral images, skipping all intermediate steps
required for traditional pixel-wise analysis. We compare the U-Net with the
traditional PLS regression on a real dataset of pork belly samples with
associated mean fat reference values. The U-Net obtains a test set root mean
squared error of between 9% and 13% lower than that of PLS regression on the
task of mean fat prediction. At the same time, U-Net generates fine detail
chemical maps where 99.91% of the variance is spatially correlated. Conversely,
only 2.53% of the variance in the PLS-generated chemical maps is spatially
correlated, indicating that each pixel-wise prediction is largely independent
of neighboring pixels. Additionally, while the PLS-generated chemical maps
contain predictions far beyond the physically possible range of 0-100%, U-Net
learns to stay inside this range. Thus, the findings of this study indicate
that U-Net is superior to PLS for chemical map generation.
