# Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing

arXiv: https://arxiv.org/abs/2206.05508 · HTML: https://mlsanity.com/paper/2206.05508/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jun 2022.

## Built on by (most cited first, 10 of 10)

- [Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package](https://mlsanity.com/paper/2308.09375/) (2023)
- [Tuning-free Plug-and-Play Hyperspectral Image Deconvolution with Deep Priors](https://mlsanity.com/paper/2211.15307/) (2022)
- [AE-RED: A Hyperspectral Unmixing Framework Powered by Deep Autoencoder and Regularization by Denoising](https://mlsanity.com/paper/2307.00269/) (2023)
- [STAR-Net: An Interpretable Model-Aided Network for Remote Sensing Image Denoising](https://mlsanity.com/paper/2505.24327/) (2025)
- [A Deep Unrolling Model with Hybrid Optimization Structure for Hyperspectral Image Deconvolution](https://mlsanity.com/paper/2306.06378/) (2023)
- [Data-driven Modeling in Metrology -- A Short Introduction, Current Developments and Future Perspectives](https://mlsanity.com/paper/2406.16659/) (2024)
- [DTU-Net: A Multi-Scale Dilated Transformer Network for Nonlinear Hyperspectral Unmixing](https://mlsanity.com/paper/2503.03465/) (2025)
- [Nonlinear Hyperspectral Unmixing based on Multilinear Mixing Model using Convolutional Autoencoders](https://mlsanity.com/paper/2303.08156/) (2023)
- [Robust retrieval of material chemical states in X-ray microspectroscopy](https://mlsanity.com/paper/2308.04207/) (2023)
- [Hyperspectral Unmixing with 3D Convolutional Sparse Coding and Projected Simplex Volume Maximization](https://mlsanity.com/paper/2512.05674/) (2025)

## Abstract (arXiv)

Spectral unmixing is one of the most important quantitative analysis tasks in hyperspectral data processing. Conventional physics-based models are characterized by clear interpretation. However they may not be suitable for analyzing scenes with unknown complex physical characteristics. Data-driven methods have developed rapidly in recent years, in particular deep learning methods because they possess superior capability in modeling complex and nonlinear systems. Simply transferring these methods as black-boxes to conduct unmixing may lead to low physical interpretability and generalization ability. This article reviews hyperspectral unmixing works that integrate advantages of both physics-based models and data-driven methods by means of deep neural network structures design, prior design and loss design. Most of these methods derive from a common mathematical optimization framework, and combine good interpretability with high accuracy.