# Extending the Unmixing methods to Multispectral Images

arXiv: https://arxiv.org/abs/2111.11893 · HTML: https://mlsanity.com/paper/2111.11893/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 23 references that are in the map.
- First posted on arXiv 23 Nov 2021.

## Builds on (most cited first, 4 of 4)

- [Algorithms, Initializations, and Convergence for the Nonnegative Matrix Factorization](https://mlsanity.com/paper/1407.7299/) (2014)
- [Spectral Unmixing of Hyperspectral Imagery using Multilayer NMF](https://mlsanity.com/paper/1408.2810/) (2014)
- [Robust Hyperspectral Unmixing with Correntropy based Metric](https://mlsanity.com/paper/1305.7311/) (2013)
- [Effective Spectral Unmixing via Robust Representation and Learning-based Sparsity](https://mlsanity.com/paper/1409.0685/) (2014)

## Built on by (most cited first, 1 of 1)

- [Bidirectional recurrent imputation and abundance estimation of LULC classes with MODIS multispectral time series and geo-topographic and climatic data](https://mlsanity.com/paper/2310.07223/) (2023)

## Abstract (arXiv)

In the past few decades, there has been intensive research concerning the Unmixing of hyperspectral images. Some methods such as NMF, VCA, and N-FINDR have become standards since they show robustness in dealing with the unmixing of hyperspectral images. However, the research concerning the unmixing of multispectral images is relatively scarce. Thus, we extend some unmixing methods to the multispectral images. In this paper, we have created two simulated multispectral datasets from two hyperspectral datasets whose ground truths are given. Then we apply the unmixing methods (VCA, NMF, N-FINDR) to these two datasets. By comparing and analyzing the results, we have been able to demonstrate some interesting results for the utilization of VCA, NMF, and N-FINDR with multispectral datasets. Besides, this also demonstrates the possibilities in extending these unmixing methods to the field of multispectral imaging.