# Spectral Unmixing via Data-guided Sparsity

arXiv: https://arxiv.org/abs/1403.3155 · HTML: https://mlsanity.com/paper/1403.3155/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Mar 2014.

## Built on by (most cited first, 14 of 14)

- [Deep Hyperspectral Unmixing using Transformer Network](https://mlsanity.com/paper/2203.17076/) (2022)
- [Hyperspectral Unmixing Based on Nonnegative Matrix Factorization: A Comprehensive Review](https://mlsanity.com/paper/2205.09933/) (2022)
- [Pixel-to-Abundance Translation: Conditional Generative Adversarial Networks Based on Patch Transformer for Hyperspectral Unmixing](https://mlsanity.com/paper/2312.13127/) (2023)
- [Adaptive Multi-Order Graph Regularized NMF with Dual Sparsity for Hyperspectral Unmixing](https://mlsanity.com/paper/2503.19258/) (2025)
- [SAWU-Net: Spatial Attention Weighted Unmixing Network for Hyperspectral Images](https://mlsanity.com/paper/2304.11320/) (2023)
- [A Multibranch Convolutional Neural Network for Hyperspectral Unmixing](https://mlsanity.com/paper/2208.02361/) (2022)
- [Rethinking Coupled Tensor Analysis for Hyperspectral Super-Resolution: Recoverable Modeling Under Endmember Variability](https://mlsanity.com/paper/2512.19489/) (2025)
- [SCA-Net: A Self-Correcting Two-Layer Autoencoder for Hyper-spectral Unmixing](https://mlsanity.com/paper/2102.05713/) (2021)
- [Equal is Not Always Fair: A New Perspective on Hyperspectral Representation Non-Uniformity](https://mlsanity.com/paper/2505.11267/) (2025)
- [Sparsity and Total Variation Constrained Multilayer Linear Unmixing for Hyperspectral Imagery](https://mlsanity.com/paper/2508.03403/) (2025)
- [SWAN: Self-supervised Wavelet Neural Network for Hyperspectral Image Unmixing](https://mlsanity.com/paper/2510.22607/) (2025)
- [Hyperspectral Unmixing with 3D Convolutional Sparse Coding and Projected Simplex Volume Maximization](https://mlsanity.com/paper/2512.05674/) (2025)
- [WS-Net: Weak-Signal Representation Learning and Gated Abundance Reconstruction for Hyperspectral Unmixing via State-Space and Weak Signal Attention Fusion](https://mlsanity.com/paper/2603.09037/) (2026)
- [Polyhedral Unmixing: Bridging Semantic Segmentation with Hyperspectral Unmixing via Polyhedral-Cone Partitioning](https://mlsanity.com/paper/2603.29438/) (2026)

## Abstract (arXiv)

Hyperspectral unmixing, the process of estimating a common set of spectral bases and their corresponding composite percentages at each pixel, is an important task for hyperspectral analysis, visualization and understanding. From an unsupervised learning perspective, this problem is very challenging---both the spectral bases and their composite percentages are unknown, making the solution space too large. To reduce the solution space, many approaches have been proposed by exploiting various priors. In practice, these priors would easily lead to some unsuitable solution. This is because they are achieved by applying an identical strength of constraints to all the factors, which does not hold in practice. To overcome this limitation, we propose a novel sparsity based method by learning a data-guided map to describe the individual mixed level of each pixel. Through this data-guided map, the ℓp(0<p<1) constraint is applied in an adaptive manner. Such implementation not only meets the practical situation, but also guides the spectral bases toward the pixels under highly sparse constraint. What's more, an elegant optimization scheme as well as its convergence proof have been provided in this paper. Extensive experiments on several datasets also demonstrate that the data-guided map is feasible, and high quality unmixing results could be obtained by our method.