# Spectral Unmixing of Hyperspectral Images Based on Block Sparse Structure

arXiv: https://arxiv.org/abs/2204.04638 · HTML: https://mlsanity.com/paper/2204.04638/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 44 references that are in the map.
- First posted on arXiv 10 Apr 2022.

## Builds on (most cited first, 10 of 10)

- [Endmember-Guided Unmixing Network (EGU-Net): A General Deep Learning Framework for Self-Supervised Hyperspectral Unmixing](https://mlsanity.com/paper/2105.10194/) (2021)
- [Pattern-Coupled Sparse Bayesian Learning for Recovery of Block-Sparse Signals](https://mlsanity.com/paper/1311.2150/) (2013)
- [Spectral Unmixing of Hyperspectral Imagery using Multilayer NMF](https://mlsanity.com/paper/1408.2810/) (2014)
- [Dynamical spectral unmixing of multitemporal hyperspectral images](https://mlsanity.com/paper/1510.04238/) (2015)
- [Iterative Bayesian Reconstruction of Non-IID Block-Sparse Signals](https://mlsanity.com/paper/1412.2316/) (2014)
- [Robust Linear Spectral Unmixing using Anomaly Detection](https://mlsanity.com/paper/1501.03731/) (2015)
- [Sparsity Constrained Graph Regularized NMF for Spectral Unmixing of Hyperspectral Data](https://mlsanity.com/paper/1411.0392/) (2014)
- [Sparsity Constrained Distributed Unmixing of Hyperspectral Data](https://mlsanity.com/paper/1902.07593/) (2019)
- [Bayesian Hypothesis Testing for Block Sparse Signal Recovery](https://mlsanity.com/paper/1508.05495/) (2015)
- [Clustered Multitask Nonnegative Matrix Factorization for Spectral Unmixing of Hyperspectral Data](https://mlsanity.com/paper/1905.08032/) (2019)

## Abstract (arXiv)

Spectral unmixing (SU) of hyperspectral images (HSIs) is one of the important areas in remote sensing (RS) that needs to be carefully addressed in different RS applications. Despite the high spectral resolution of the hyperspectral data, the relatively low spatial resolution of the sensors may lead to mixture of different pure materials within the image pixels. In this case, the spectrum of a given pixel recorded by the sensor can be a combination of multiple spectra each belonging to a unique material in that pixel. Spectral unmixing is then used as a technique to extract the spectral characteristics of the different materials within the mixed pixels and to recover the spectrum of each pure spectral signature, called endmember. Block-sparsity exists in hyperspectral images as a result of spectral similarity between neighboring pixels. In block-sparse signals, the nonzero samples occur in clusters and the pattern of the clusters is often supposed to be unavailable as prior information. This paper presents an innovative spectral unmixing approach for HSIs based on block-sparse structure. Hyperspectral unmixing problem is solved using pattern coupled sparse Bayesian learning strategy (PCSBL). To evaluate the performance of the proposed SU algorithm, it is tested on both synthetic and real hyperspectral data and the quantitative results are compared to those of other state-of-the-art methods in terms of abundance angle distance and mean squared error. The achieved results show the superiority of the proposed algorithm over the other competing methods by a significant margin.