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Physics-Informed Spectral Modeling for Hyperspectral Imaging
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AI
DOI：10.1109/LGRS.2026.3678651.png)
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Abstract
En 中文
We present physically informed spectral modeler (PhISM), a physics-informed deep learning (DL) architecture that learns without supervision to explicitly disentangle hyperspectral observations and model them with continuous basis functions. PhISM outperforms previous methods on several classification and regression benchmarks, requires limited labeled data, and provides additional insights thanks to its interpretable latent representation.
Keywords:
Explainable artificial Intelligence (AI)
hyperspectral imaging
representation learning
self-supervised learning
Journal
I
IF:
4.4
Papers:
585
Citations:
0