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Hyperspectral image denoising method based on robust low-rank tensor

A hyperspectral image, robust technology, applied in the field of hyperspectral image denoising based on robust low-rank tensor, can solve the problem of loss of spatial spectral information, affecting the denoising effect, etc.

Active Publication Date: 2015-11-18
WUHAN UNIV
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However, this will cause the loss of spatial spectral information and affect the denoising effect

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[0041] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0042] Refer to attached figure 1 , the present invention mainly consists of three steps: a mathematical model of hyperspectral image noise, constructing a hyperspectral image robust low-rank tensor denoising model, and using an inaccurate enhanced Lagrangian method to solve the RLRTR model. The real data selected in the embodiment is the IndianPines data set, which has a total of 220 bands, and the wavelength range it covers is 0.4-2.5 μm. After removing the bands 104-108, 150-163, and 220, which are severely absorbed by water vapor, there are 200 bands left. The image The size is 145×145, because the classification accuracy is easily affected by noise, so the classification accuracy can be used to evaluate the denoising effect. The selected comparison algorithm is PARAFAC. Here, we use the Support Vector Machine (SVM) proposed by C.Chang et al. in "L...

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Abstract

The invention proposes a hyperspectral image denoising method based on robust low-rank tensor, which comprises the steps of establishing a mathematical model of hyperspectral image noise, constructing a hyperspectral image robust low-rank tensor (RLRTR) denoising optimization model, and solving the RLRTR denoising optimization model. The hyperspectral image denoising method fully utilizes the prior knowledge of hyperspectral images (HSI) which are polluted by different kinds of noise such as Gaussian noise, impulse noise, dead pixels and striping noise. The hyperspectral image denoising method utilizes the potential low-rank tensor property of clean hyperspectral image data and the sparsity property of abnormal and non-Gaussian noise, and adopts a nuclear norm and an l2,1 norm for representing the low-rank and sparsity properties. The technical scheme of the hyperspectral image denoising method fully uses the prior information and internal structure characteristics of the hyperspectral image, and can remove Gaussian noise, abnormal and non-Gaussian noise simultaneously.

Description

technical field [0001] The invention relates to the field of hyperspectral image denoising, in particular, the invention relates to a hyperspectral image denoising method based on a robust low-rank tensor. Background technique [0002] Over the past few decades, hyperspectral imagery (HSI) has rapidly developed into one of the most powerful techniques in remote sensing. Since hyperspectral images contain rich spectral information, they have been widely used, such as object classification, mineral detection, environmental monitoring and military monitoring. However, detectors, photon effects, and correction errors will inevitably introduce noise into the hyperspectral image data cube, which will not only affect the visual effect of the hyperspectral image, but also affect the subsequent image interpretation and analysis. Therefore, hyperspectral image denoising is an essential preprocessing step for many hyperspectral image applications such as object detection, spectral unm...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00
Inventor 马佳义马泳黄珺梅晓光樊凡
Owner WUHAN UNIV
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