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Spectral-Spatial Method for Hyperspectral Image classification in Noisy Environment

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DOI: 10.23977/geors.2019.21002 | Downloads: 26 | Views: 3876

Author(s)

Pierre Delmas 1, Caroline Fossati 1, Salah Bourennane 1

Affiliation(s)

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

Corresponding Author

Pierre Delmas

ABSTRACT

Target detection is an important issue in the HyperSpectral Image (HSI) processing field. However, current spectral-identification-based target detection algorithms are sensitive to the noise and most denoising algorithms cannot preserve small targets, therefore it is necessary to design a robust detection algorithm that can preserve small targets. Because the signal-dependent photonic noise has become as dominant as the signal-independent noise generated by the electronic circuitry in HSI data collected by new-generation hyperspectral sensors, the reduction of the additive signal-dependent photonic noise becomes the focus of the current research in this field. To reduce the opto-elctronic noise from HSIs, a new method is developed in this paper. Firstly, a pre-whitening procedure is proposed to whiten noise in HSIs. Secondly, a three-dimensional wavelet packet transform (3-WPT) in tensor form is presented to find different component tensors of HSI. Then, to jointly filter a component tensor in each mode, multiway Wiener filter (MWF) is introduced. Moreover, to determine the best transform level and basis of 3-WPT a risk function is proposed. The effectiveness of our method in classification is experimentally demonstrated on a real-world HSI acquired by airborne sensor.

KEYWORDS

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

CITE THIS PAPER

Pierre Delmas, Caroline Fossati and Salah Bourennane, Spectral-Spatial Method for Hyperspectral Image classification in Noisy Environment, Geoscience and Remote Sensing (2019) Vol. 2: 25-40. DOI: http://dx.doi.org/10.23977/geors.2019.21002.

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