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Improvement of Target Detection Based on Signal Dependent Noise Reduction for Hyperspectral Image

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DOI: 10.23977/geors.2018.11013 | Downloads: 17 | Views: 3311

Author(s)

Caroline Fossati 1, Salah Bourennane 1

Affiliation(s)

1 Aix Marseille Univ, CNRS, Centrale Marseille, Institut Fresnel, Marseille, France

Corresponding Author

Caroline Fossati

ABSTRACT

In the hyperspectral images (HSI) acquired by the new-generation hyperspectral sensors the signal dependent noise is an important limitation to the detection. Therefore, noise reduction is an important preprocessing step to analyze the information in the hyperspectral image. A signal dependent noise cannot be reduced by conventional linear filtering. Therefore, a new method based on Parallel factor analysis (PARAFAC) decomposition is proposed to estimate the noise of hyperspectral remote sensing image. Then, the estimated noise is used for whitening the colored structural noise. By using this transformation, the data noise from new-generation hyperspectral sensors is diminished, thereby allowing a minimization of negative impacts on hyperspectral detection applications.

KEYWORDS

Detection, multi-linear algebra, reduction, hyperspectral image.

CITE THIS PAPER

Caroline Fossati and Salah Bourennane, Improvement of Target Detection Based on Signal Dependent Noise Reduction for Hyperspectral Image, Geoscience and Remote Sensing (2018) Vol. 1: 18-27.

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