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A 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, which can solve the problems of affecting the denoising effect and loss of spatial spectral information.

Active Publication Date: 2017-11-28
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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  • A Hyperspectral Image Denoising Method Based on Robust Low Rank Tensor
  • A Hyperspectral Image Denoising Method Based on Robust Low Rank Tensor
  • A Hyperspectral Image Denoising Method Based on Robust Low Rank Tensor

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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 Indian Pines 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 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 adopt the support vector machine ( SVM) is used as a classifier to clas...

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Abstract

The present invention proposes a hyperspectral image denoising method based on robust low-rank tensor, including establishing a mathematical model of hyperspectral image noise, constructing a hyperspectral image robust low-rank tensor (RLRTR) denoising model, and solving RLRTR Denoising optimization model. The present invention makes full use of the prior knowledge of the hyperspectral image (HSI), which is polluted by different noises, such as Gaussian noise, impulse noise, dead pixels, and banding noise. Utilize the potential low-rank tensor characteristics of clean hyperspectral image data and the sparsity characteristics of abnormal and non-Gaussian noise, and simultaneously use the nuclear norm and l2,1 norm to characterize the low-rank and sparse characteristics; the present invention The technical solution makes full use of the prior information and intrinsic structural features of hyperspectral images, and can simultaneously remove Gaussian noise, abnormal and non-Gaussian noise.

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 Patents(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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