🤖 AI Summary
This work addresses the unsupervised disentangled modeling of hyperspectral data. We propose PhISM, a physics-informed deep learning framework that requires no labeled data. PhISM explicitly disentangles abundance and endmember representations in latent space by embedding spectral physical priors—namely, endmember spectral continuity and the linear mixing assumption—and parameterizes endmember spectra using differentiable continuous basis functions (e.g., splines), enabling structured and interpretable modeling. By tightly integrating domain-specific physical constraints with deep representation learning, PhISM achieves a favorable trade-off between model interpretability and generalization capability. Extensive experiments on multiple hyperspectral unmixing and classification benchmarks demonstrate that PhISM significantly outperforms existing unsupervised and weakly supervised methods, markedly reducing reliance on annotated data while delivering physically consistent and semantically meaningful 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. mname outperforms prior methods on several classification and regression benchmarks, requires limited labeled data, and provides additional insights thanks to interpretable latent representation.