Objective

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Hyperspectral remote sensing is a technique based on the principle of spectrometry to obtain some very narrow and continuous image data in the ultraviolet

visible

near-infrared and mid-infrared regions of the electromagnetic spectrum. Hyperspectral imaging technology combines the traditional two-dimensional image remote sensing technology and spectral technology to obtain the surface image and the spectral information at the same time. Hyperspectral images(HSI) can not only classify and recognize ground objects with high spectral diagnostic ability

but also contain rich information

which makes them widely used in many fields. The unique characteristics of hyperspectral images bring convenience and advantages to the acquisition of geographic information and the identification of ground objects. Unfortunately

there are also some difficulties in hyperspectral technology:the amount of data obtained by hyperspectral sensors is large

but it is often interfered by various factors during the acquisition process

such as environment and equipment

so that the data is polluted

which reduces the data availability and limits the subsequent application of hyperspectral sensors in various fields. Therefore

reducing noise pollution of data

obtaining more effective image information and increasing the utilization rate of image data are important links to ensure that hyperspectral images can play an important role in subsequent applications.

Method

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Hundreds of continuous spectral bands image the target region at the same time

so that the hyperspectral image can provide spatial and spectral domain information. Moreover

the continuity of hyperspectral images in spatial domain and spectral domain makes the correlation between adjacent channels strong

that is a low-rank property. Based on this feature

spectral low-rank priors or spatial low-rank priors are considered to establish the restoration model for hyperspectral data restoration. Undoubtedly

the combination of the two models can achieve better recovery effect. But now

although the recovery method based on nuclear norm has a strong theory to ensure that excellent results can be obtained

due to the defects in the application

the second-best results can be obtained in the actual application. In the rank function

different eigenvalues contain different information of the observation data

among which the larger eigenvalues mainly contain the original data information

while the smaller eigenvalues mainly contain the noise information of the observation data. However

the restoration method based on nuclear norm minimization treats all the eigenvalues equally

and the corresponding model algorithm uses the same threshold to shrink the eigenvalues

thus losing a large amount of image information under the premise of false noise

which is an important defect of current mainstream denoising methods. In addition

some theoretical requirements of the nuclear norm are hardly satisfactory in practice. Considering truncated nuclear norm regularization is more robust and accurate than nuclear norm for the rank function's approximation

the application of truncated norm in hyperspectral denoising is still in the stage of using the low-rank priori information of hyperspectral spectrum for denoising

and the spatial low-rank priori information of hyperspectral is not used. The results of the existing methods are not satisfactory. We improve the current low rank based prior information in the spatial domain and based on the low rank based priori information in the spectral domain

a low rank representation model is proposed to depress the sparse noises. Based on the low rank based priori information in the spatial domain

the total variation regularization method is proposed to depress the density noises. Finally combined with the advantages of the two model we propose the model with truncated nuclear norm minimization and total variation regularization. This model not only retains the processing advantages of current mainstream models

but also makes full use of the truncated nuclear norm. As for the algorithm

alternating direction method of multipliers(ADMM) is a simple and effective method for distributed convex optimization. This method can decompose the original function and the amplification function

so as to optimize in parallel under more general assumptions. Therefore

the paper chooses ADMM method to solve the model. In order to verify the denoising effect of the model proposed in this paper

as well as the universality and generalization of the model

two truly collected hyperspectral sets were selected for the experiment. Gaussian noise

salt-pepper noise and dead line noise with different intensity are added to simulate noise pollution in real situation. In addition

different size images are selected for experiments to test the denoising effect of various methods.

Result

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The restoration results of the proposed method are compared with the latest method

the peak signal to noise ratio (PSNR) index and the structure similarity (SSIM) index are improved 3.2 dB and 0.22. It can be seen from the experimental results that the gaussian white noise is still left after the traditional method is processed

while the image processed by the method in this paper effectively restrains the mixed noise. Not only the visual image shows that the de-noising result of the model proposed in this paper is more detailed than that recovered by total variation-regularized low-rank matrix factorization (LRTV) and other methods. PSNR and SSIM index of each channel also confirmed this result.In addition

the method proposed in this paper has stronger image restoration ability under higher noise

and no outliers appear. It can effectively improve the image quality

and get good recovery results for images containing various noises and images of different sizes

the peak signal to noise ratio is improved 1.33 dB

which shows that the model has good generalization and universality.

Conclusion

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The low rank based priori information in the spectral domain is more robust and the model in this paper relies on the low rank based priori knowledge in both spectral and spatial domain tightly so that it can efficiently depresses the sparse noise and the density noise in the degraded hyperspectral remote sensing images. Experiments show that truncated nuclear norm can better control noise error by using the sum of smaller eigenvalues

so as to better represent the characteristics of data. Experiments with different noises added to hyperspectral data of different sizes also show that this method has excellent denoising effect.