# Hyperspectral Unmixing with Robust Collaborative Sparse Regression

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## Abstract

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## 1. Introduction

## 2. Robust Collaborative Sparse Regression

## 3. Experiments

#### 3.1. Experimental Results with Synthetic Data

#### 3.2. Experimental Results with Real Data

## 4. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Performance of SRE as a function of the number of endmember under Gaussian white noise when the SNR is 10 with the (

**a**) LMM; (

**b**) FM; (

**c**) GBM and (

**d**) MGBM.

**Figure 2.**Performance of SRE as a function of the number of endmembers under Gaussian white noise when the SNR is 50 with the (

**a**) LMM; (

**b**) FM; (

**c**) GBM and (

**d**) MGBM.

**Figure 3.**Performance of SRE as a function of SNR under Gaussian white noise when the number of endmembers is four with the (

**a**) LMM; (

**b**) FM; (

**c**) GBM and (

**d**) MGBM.

**Figure 4.**Performance of SRE as a function of SNR under Gaussian white noise when the number of endmembers is 20 with the (

**a**) LMM; (

**b**) FM; (

**c**) GBM and (

**d**) MGBM.

**Figure 5.**Performance of SRE as a function of the number of endmembers under correlated noise when the SNR is 10 with the (

**a**) LMM; (

**b**) FM; (

**c**) GBM and (

**d**) MGBM.

**Figure 6.**Performance of SRE as a function of the number of endmembers under correlated noise when the SNR is 50 with the (

**a**) LMM; (

**b**) FM; (

**c**) GBM and (

**d**) MGBM.

**Figure 7.**Performance of SRE as a function of SNR under correlated noise when the number of endmembers is 4 with the (

**a**) LMM; (

**b**) FM; (

**c**) GBM and (

**d**) MGBM.

**Figure 8.**Performance of SRE as a function of SNR under correlated noise when the number of endmembers is 20 with the (

**a**) LMM; (

**b**) FM; (

**c**) GBM and (

**d**) MGBM.

**Figure 10.**Fractional abundance maps estimated by different unmixing methods using the subimage of the AVIRIS Cuprite dataset.

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**MDPI and ACS Style**

Li, C.; Ma, Y.; Mei, X.; Liu, C.; Ma, J. Hyperspectral Unmixing with Robust Collaborative Sparse Regression. *Remote Sens.* **2016**, *8*, 588.
https://doi.org/10.3390/rs8070588

**AMA Style**

Li C, Ma Y, Mei X, Liu C, Ma J. Hyperspectral Unmixing with Robust Collaborative Sparse Regression. *Remote Sensing*. 2016; 8(7):588.
https://doi.org/10.3390/rs8070588

**Chicago/Turabian Style**

Li, Chang, Yong Ma, Xiaoguang Mei, Chengyin Liu, and Jiayi Ma. 2016. "Hyperspectral Unmixing with Robust Collaborative Sparse Regression" *Remote Sensing* 8, no. 7: 588.
https://doi.org/10.3390/rs8070588