# RECONSTRUCTION OF HYPERSPECTRAL IMAGE BASED ON REGRESSION ANALYSIS - Optimum Regression Model and Channel Selection

### Yuji Sakatoku, Jay Arre Toque, Ari Ide-Ektessabi

#### 2009

#### Abstract

The purpose of this study is to develop an efficient appraoch for producing hyperspectral images by using reconstructed spectral reflectance from multispectral images. In this study, an indirect reconstruction based on regression analysis was employed because of its stability to noise and its practicality. In this approach however, the regression model selection and channel selection when acquiring the multispectral images play important roles, which consequently affects the efficiency and accuracy of reconstruction. The optimum regression model and channel selection were investigated using the Akaike information criterion (AIC). By comparing the model based on the AIC model based on the pseudoinverse method (the pseudinverse method is a widely used reconstruction technique), RMSE could be reduced by fifty percent. In addition, it was shown that AIC-based model has good stability to noise.

#### References

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#### Paper Citation

#### in Harvard Style

Sakatoku Y., Toque J. and Ide-Ektessabi A. (2009). **RECONSTRUCTION OF HYPERSPECTRAL IMAGE BASED ON REGRESSION ANALYSIS - Optimum Regression Model and Channel Selection
** . In *Proceedings of the First International Conference on Computer Imaging Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2009)* ISBN 978-989-8111-68-5, pages 50-55. DOI: 10.5220/0001791800500055

#### in Bibtex Style

@conference{imagapp09,

author={Yuji Sakatoku and Jay Arre Toque and Ari Ide-Ektessabi},

title={RECONSTRUCTION OF HYPERSPECTRAL IMAGE BASED ON REGRESSION ANALYSIS - Optimum Regression Model and Channel Selection
},

booktitle={Proceedings of the First International Conference on Computer Imaging Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2009)},

year={2009},

pages={50-55},

publisher={SciTePress},

organization={INSTICC},

doi={10.5220/0001791800500055},

isbn={978-989-8111-68-5},

}

#### in EndNote Style

TY - CONF

JO - Proceedings of the First International Conference on Computer Imaging Theory and Applications - Volume 1: IMAGAPP, (VISIGRAPP 2009)

TI - RECONSTRUCTION OF HYPERSPECTRAL IMAGE BASED ON REGRESSION ANALYSIS - Optimum Regression Model and Channel Selection

SN - 978-989-8111-68-5

AU - Sakatoku Y.

AU - Toque J.

AU - Ide-Ektessabi A.

PY - 2009

SP - 50

EP - 55

DO - 10.5220/0001791800500055