Transforming Hyperspectral Images Into Chemical Maps: A Novel End-to-End Deep Learning Approach
This addresses the problem of noisy, spatially incoherent chemical maps in hyperspectral imaging for applications like food analysis, though it is an incremental improvement over existing deep learning methods.
The study tackled chemical map generation from hyperspectral images by proposing an end-to-end deep learning approach using a modified U-Net, which achieved a 7% lower test set root mean squared error than traditional PLS regression on pork belly fat prediction and produced spatially correlated maps with 99.91% variance correlation.
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. This study compares 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 that is 7% 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 2.37% 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.