# Spectral Variability Augmented Sparse Unmixing of Hyperspectral Images

arXiv: https://arxiv.org/abs/2110.09744 · HTML: https://mlsanity.com/paper/2110.09744/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 73 references that are in the map.
- First posted on arXiv 19 Oct 2021.

## Builds on (most cited first, 12 of 12)

- [An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing](https://mlsanity.com/paper/1810.12000/) (2018)
- [Spectral Variability in Hyperspectral Data Unmixing: A Comprehensive Review](https://mlsanity.com/paper/2001.07307/) (2020)
- [Hyperspectral unmixing with spectral variability using a perturbed linear mixing model](https://mlsanity.com/paper/1502.01260/) (2015)
- [Hyperspectral Image Unmixing with Endmember Bundles and Group Sparsity Inducing Mixed Norms](https://mlsanity.com/paper/1805.10232/) (2018)
- [Spectral Variability Aware Blind Hyperspectral Image Unmixing Based on Convex Geometry](https://mlsanity.com/paper/1904.03888/) (2019)
- [Tech Report: A Fast Multiscale Spatial Regularization for Sparse Hyperspectral Unmixing](https://mlsanity.com/paper/1712.01770/) (2017)
- [Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing](https://mlsanity.com/paper/1504.01515/) (2015)
- [Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability](https://mlsanity.com/paper/1406.5071/) (2014)
- [A Data Dependent Multiscale Model for Hyperspectral Unmixing With Spectral Variability](https://mlsanity.com/paper/1808.01047/) (2018)
- [Spectral Unmixing: A Derivation of the Extended Linear Mixing Model from the Hapke Model](https://mlsanity.com/paper/1903.12089/) (2019)
- [Deep Generative Models for Library Augmentation in Multiple Endmember Spectral Mixture Analysis](https://mlsanity.com/paper/1909.09741/) (2019)
- [Hyperspectral unmixing with spectral variability using adaptive bundles and double sparsity](https://mlsanity.com/paper/1804.11132/) (2018)

## Built on by (most cited first, 2 of 2)

- [Benchmarking Adversarial Patch Against Aerial Detection](https://mlsanity.com/paper/2210.16765/) (2022)
- [Image Processing and Machine Learning for Hyperspectral Unmixing: An Overview and the HySUPP Python Package](https://mlsanity.com/paper/2308.09375/) (2023)

## Abstract (arXiv)

Spectral unmixing (SU) expresses the mixed pixels existed in hyperspectral images as the product of endmember and abundance, which has been widely used in hyperspectral imagery analysis. However, the influence of light, acquisition conditions and the inherent properties of materials, results in that the identified endmembers can vary spectrally within a given image (construed as spectral variability). To address this issue, recent methods usually use a priori obtained spectral library to represent multiple characteristic spectra of the same object, but few of them extracted the spectral variability explicitly. In this paper, a spectral variability augmented sparse unmixing model (SVASU) is proposed, in which the spectral variability is extracted for the first time. The variable spectra are divided into two parts of intrinsic spectrum and spectral variability for spectral reconstruction, and modeled synchronously in the SU model adding the regular terms restricting the sparsity of abundance and the generalization of the variability coefficient. It is noted that the spectral variability library and the intrinsic spectral library are all constructed from the In-situ observed image. Experimental results over both synthetic and real-world data sets demonstrate that the augmented decomposition by spectral variability significantly improves the unmixing performance than the decomposition only by spectral library, as well as compared to state-of-the-art algorithms.