# Physics-Informed Spectral Modeling for Hyperspectral Imaging

## TL;DR

PhISM is a physics-informed deep learning model that effectively disentangles hyperspectral data, requiring minimal supervision, and offers interpretable insights, outperforming previous methods on various benchmarks.

## Contribution

Introduces PhISM, a novel physics-informed spectral modeling approach that learns without supervision and enhances interpretability in hyperspectral imaging.

## Key findings

- Outperforms prior methods on classification and regression benchmarks.
- Requires limited labeled data for training.
- Provides interpretable latent representations.

## Abstract

We present PhISM, a physics-informed deep learning architecture that learns without supervision to explicitly disentangle hyperspectral observations and model them with continuous basis functions. PhISM outperforms prior methods on several classification and regression benchmarks, requires limited labeled data, and provides additional insights thanks to interpretable latent representation.