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Ridge ratio shrinkage-based hyperspectral image virtual dimension estimation method

A hyperspectral image and dimensionality technology, which is applied in the field of hyperspectral image virtual dimension estimation based on ridge ratio shrinkage, can solve problems such as inability to guarantee estimation consistency and poor accuracy of hyperspectral image virtual dimension estimation, and achieve strong Operability and reproducibility, generalizability and rationality, effect of reducing processing time and storage space

Active Publication Date: 2019-08-16
XI AN JIAOTONG UNIV
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  • Application Information

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Problems solved by technology

The usual practice now is to use the HFC method. The HFC method is based on eigenvalue analysis and Neyman-Pearson detection theory, which is simple and effective, but the hypothesis testing idea used in it cannot guarantee the consistency of the estimate, and the size of the first type of error needs to be Tried to select, resulting in poor accuracy of hyperspectral image virtual dimension estimation

Method used

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  • Ridge ratio shrinkage-based hyperspectral image virtual dimension estimation method
  • Ridge ratio shrinkage-based hyperspectral image virtual dimension estimation method
  • Ridge ratio shrinkage-based hyperspectral image virtual dimension estimation method

Examples

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Embodiment 1

[0080] The first example uses mock data. The TRR method and the NWTRR method proposed by the present invention are compared with other methods, wherein the NWTRR method uses the TRR method after noise whitening. The noise whitening method is as follows: Do preprocessing. Preprocessing includes demeaning and whitening. By performing a linear transformation on the observed data vector to make its mean zero and variance one, the correlation between observations is removed.

[0081] In the process of generating simulated data, we set the virtual dimension of the image to be 5, the white noise to be Legendre white noise, and the signal-to-noise ratio to take four different values ​​of 20, 40, 60, and 80 to increase the accuracy of the comparison. The results are shown in Table 1 Shown:

[0082] The comparative data of table 1. embodiment 1

[0083]

[0084] From the analysis in Table 1, it can be seen that when the signal-to-noise ratio is 20, the above method performs bas...

Embodiment 2

[0086] In the second example, we apply the method TRR of the present invention and other methods used in Example 1 to four actual images for virtual dimension estimation, and the results are shown in Table 2:

[0087] The comparative data of table 2. embodiment 2

[0088]

[0089] The above four actual images are commonly used hyperspectral datasets. It can be seen from Table 2 that the TRR method of the present invention performs well in all four groups of images, the floating range near the virtual dimension is small, and the real virtual dimension is basically estimated correctly. While other methods, such as ELM, which perform well in simulated datasets, suffer significant errors. The performance of HFC and the denoised improved NWHFC is also unsatisfactory in each set of images. Therefore, in general, other methods have large errors.

[0090] To sum up, in the current popular theory of HFC hypothesis testing, we found that it is inconsistent, biased estimation, and ...

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Abstract

The invention discloses a ridge ratio shrinkage-based hyperspectral image virtual dimension estimation method. The method comprises the steps of extracting a sample matrix of a to-be-processed hyperspectral image; calculating to obtain a covariance matrix and a correlation matrix of the sample matrix, calculating characteristic values of the covariance matrix and the correlation matrix, and sorting; constructing a ratio sequence according to the characteristic value sequence; and comparing the sequence value in the ratio sequence with a preset constraint value to obtain a virtual dimension ofthe to-be-processed hyperspectral image. The estimation method abandons the idea of hypothetical inspection, and directly adopts an algorithm for constructing a ratio sequence to estimate, so that theaccuracy of the virtual dimension estimation of the hyperspectral image can be improved.

Description

technical field [0001] The invention belongs to the technical field of hyperspectral image virtual dimension estimation, in particular to a hyperspectral image virtual dimension estimation method based on ridge ratio shrinkage. Background technique [0002] Spectral images with a spectral resolution in the range of 10nm are called hyperspectral images. Through hyperspectral sensors mounted on different space platforms, that is, imaging spectrometers, in the ultraviolet, visible, near-infrared and mid-infrared regions of the electromagnetic spectrum, the target area is simultaneously imaged in tens to hundreds of continuous and subdivided spectral bands ; While obtaining surface image information, it also obtains its spectral information, truly achieving the combination of spectrum and image. Compared with multispectral remote sensing images, hyperspectral images have not only greatly improved in terms of information richness, but also provide the possibility of more reasona...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
CPCG06V10/751
Inventor 朱学虎康越刘军民罗兰王一成
Owner XI AN JIAOTONG UNIV
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