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A hyperspectral remote sensing image restoration method based on non-convex low rank sparse constraint

A technology of hyperspectral remote sensing and sparse constraint, applied in the field of hyperspectral remote sensing image restoration based on non-convex low-rank sparse constraint model, it can solve the problems of affecting the quality of image restoration and the convergence speed cannot meet the requirements, and achieves the preservation of image details and high accuracy. Image restoration quality, to achieve the effect of restoration

Active Publication Date: 2018-12-28
HARBIN INST OF TECH
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Problems solved by technology

However, such algorithms mostly use convex relaxation with low-rank constraints or sparse constraints. 1 norm to construct the constrained optimization model, but l 1 Due to the shrinkage effect, the norm sometimes has an estimation bias, which affects the quality of image restoration; in addition, when the dimension of the image data matrix increases, the convergence speed often cannot meet the requirements

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  • A hyperspectral remote sensing image restoration method based on non-convex low rank sparse constraint
  • A hyperspectral remote sensing image restoration method based on non-convex low rank sparse constraint
  • A hyperspectral remote sensing image restoration method based on non-convex low rank sparse constraint

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example 1

[0095] Example 1: This example selects a typical dataset in the field of remote sensing——EO-1Hyperion Australia dataset. The size of the original image is 3858×256×242. Due to space limitations, after removing the overlapping bands in the range of visible light, near-infrared and short-wave infrared , select a size of 400×200×150, subtract the minimum pixel value from each pixel value, and then divide by the difference between the maximum and minimum pixel values ​​to obtain the data normalized to [0, 1]. Use the method proposed by the present invention to carry out image recovery processing, and use the above-mentioned LRMR, LRTV, NAILRMA to carry out comparative experiments. Experimental results such as figure 2 shown, where figure 2 (a) is the original hyperspectral remote sensing image of the 52nd band, figure 2 (b) is the effect of the LRMR method, figure 2 (c) is the effect of LRTV method, figure 2 (d) is the effect of the NAILRMA method, figure 2 (e) is the e...

example 2

[0096] Example 2: This example selects a typical data set in the field of remote sensing—Hyperspectral Digital ImageryCollection Experiment (HYDICE) Washington DC Mall data set. The size of the original image is 1208×307×191. Due to space limitations, the selected image size is 256×256×11 . Same as Example 1, the original image data is normalized, and Gaussian noise with an average value of 25dB is randomly added to all bands artificially. Then use the method proposed by the present invention to restore and compare with other methods. Experimental results such as image 3 shown, where image 3 (a) is one of the clear hyperspectral remote sensing image data, image 3 (b) is the image after adding Gaussian noise and strip noise, image 3 (c) is the effect of LRMR method (SNR=10.8786dB, MSSIM=0.66377), image 3 (d) is the effect of LRTV method (SNR=10.6603dB, MSSIM=0.74259), image 3 (e) is the NAILRMA method effect (SNR=15.5521dB, MSSIM=0.83878), image 3 (f) is the effec...

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Abstract

A method for restoring hyperspectral remote sensing image based on non-convex and low-rank sparse constraint belongs to the field of hyperspectral remote sensing image processing in remote sensing image processing. In order to solve the problem that the existing hyperspectral remote sensing image restoration technology can not effectively remove noise and improve the image restoration quality, themethod comprises the following steps: inputting a hyperspectral remote sensing image; initializing a weight coefficient matrix, iterative times and a convergence threshold, initializing sub-image size and scanning step, partitioning sub-blocks; establishing an image restoration model; the auxiliary variable and the coefficient of the regular term being introduced, and the maximum-minimum algorithm being used to solve the problem iteratively; judging whether the restoration result satisfies the convergence condition; obtaining a hyperspectral restored image that meets the requirements by iterative times, otherwise returning to corresponding steps to continue the iterative operation; calculating a weight coefficient matrix and assigning appropriate weights to each sub-block; hyperspectral remote sensing images being restored to obtain the final restored hyperspectral remote sensing images. The effect of denoising is obvious and the image details are preserved.

Description

technical field [0001] The invention belongs to the field of hyperspectral image processing in remote sensing image processing, and in particular relates to a hyperspectral remote sensing image restoration method based on a non-convex low-rank sparse constraint model. Background of the invention [0002] Hyperspectral remote sensing imaging technology combines spectral analysis and optical imaging technology to detect the two-dimensional geometric space and one-dimensional spectral information of the target, and obtain high-resolution continuous and narrow-band image data. At present, hyperspectral imaging technology is developing rapidly, and it is widely used in environmental research, geological exploration, military surveillance and other fields because of its rich spectral information of ground objects. However, due to the physical defects of the sensor, photon effects, transmission loss, and calibration errors, the hyperspectral remote sensing images obtained in practi...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06T7/11
CPCG06T7/11G06T2207/10032G06T2207/20021G06T2207/20192G06T5/70
Inventor 胡悦李晓迪赵旷世苑鑫
Owner HARBIN INST OF TECH
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