Iterative hyperspectral image lossless compression method based on group low-rank representation

A technology of hyperspectral image, low rank representation, applied in image communication, instrument, character and pattern recognition, etc., can solve problems such as affecting compression results, inaccuracy, unstable k-means clustering results, etc., to enhance stability , the effect of high compression ratio

Active Publication Date: 2021-07-02
XIDIAN UNIV
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  • Application Information

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Problems solved by technology

[0004] In summary, in the current lossless compression methods for hyperspectral images, there are problems: the spatial-spectral correlation of hyperspectral images is not fully utilized, the clustering results based on k-means are unstable and inaccurate, and the clustering results are not fully utilized. The relationship between the three modules of , prediction and entropy coding, these problems directly affect the compression results

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  • Iterative hyperspectral image lossless compression method based on group low-rank representation
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  • Iterative hyperspectral image lossless compression method based on group low-rank representation

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Embodiment 1

[0026] After decades of development, the amount of data acquired by imaging spectrometers has expanded rapidly with the continuous improvement of spatial resolution and spectral resolution, and its huge amount of data has caused a huge burden on the storage and transmission of hyperspectral images. Seriously restricting the application prospects of hyperspectral images. Therefore, in order to improve storage and transmission efficiency and reduce costs, the present invention proposes an iterative hyperspectral image lossless compression method based on group low-rank representations.

[0027] The present invention is an iterative hyperspectral image lossless compression method based on group low-rank representation, see figure 1 , including the following steps:

[0028] (1) Define a spectral angle similarity measurement method: the spectral angle similarity measurement method of the present invention combines spectral angle and Euclidean distance to measure the similarity of ...

Embodiment 2

[0039] The iterative hyperspectral image lossless compression method based on group low-rank representation is the same as embodiment 1, the spectral angle similarity measurement method described in step (1), the spectral angle similarity measurement method combines spectral angle and Euclidean distance to hyperspectral image The similarity measure and its similarity measure formula are expressed as follows:

[0040]

[0041] in, Indicates the spectral line x and the spectral line The similarity of , the spectral line x is any spectral line participating in the similarity measurement, and the spectral line is another spectral line that needs to be compared with the spectral line x, Indicates the spectral line x and the spectral line The spectral angular distance between Indicates the spectral line x and the spectral line The spectral angular distance between, NA means spectral line x and spectral line The maximum spectral angular distance between, N L Indicat...

Embodiment 3

[0044] The iterative hyperspectral image lossless compression method based on the group low-rank representation is the same as embodiment 1-2, and the rough clustering initialization of the original image described in step (2) includes the following steps:

[0045] (2a) Select seed points: randomly select K seed points in the input hyperspectral image, and K is selected as 400 in this example.

[0046] (2b) Calculate the search range of rough clustering initialization seed points: input the expected number of superpixels K, assuming that the number of spectral lines of a hyperspectral image is N, then the search range calculation formula of a seed point is:

[0047]

[0048] L is the search step size, and the unit is the number of spectral lines. Since the calculation result of L is not necessarily an integer, the calculation result is an approximate value. In this example, N is 79099776 spectral lines, and the obtained L is 197749 spectral lines.

[0049] (2c) Assign a ro...

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Abstract

The invention discloses an iterative hyperspectral image lossless compression method based on group low-rank representation, and solves the problems that a traditional compression method ignores the correlation of an image space, a clustering result is unstable, and modules are not connected. The method comprises the following implementation steps: defining a spectral angle similarity measurement method; roughly clustering the original image; solving a rough clustering block coefficient matrix through low-rank representation; re-clustering the coefficient matrix to obtain an initial clustering result; iteratively optimizing the initial clustering result to obtain a prediction coefficient and a prediction residual error of a final clustering block; carrying out entropy coding to obtain a code stream file to be transmitted; and after entropy decoding, decompressing the code stream file at a decoding end to obtain a lossless compressed hyperspectral image. According to the method, a spectral angle correlation measurement method is defined, and utilization of spatial correlation is increased; low-rank representation is combined with subspace clustering, so that the stability of a clustering result is improved; and each module is correlated through iterative optimization, so that the result compression ratio is increased. The method is applied to image compression.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to image lossless compression, in particular to an iterative hyperspectral image lossless compression method based on group low-rank representation, which is used for hyperspectral image compression. Background technique [0002] Hyperspectral images are obtained by reflecting electromagnetic waves of different bands from the same surface object, and the number of bands in the visible to near-infrared spectrum range can reach hundreds. The nanoscale spectral resolution of hyperspectral images makes hyperspectral images rich in spectral information, which can provide precise details of ground features, and has a wide range of applications in environmental monitoring, military investigation, resource management, mineral exploration, and vegetation research. After decades of development, the amount of data acquired by imaging spectrometers has expanded rapidly with the continuou...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04N19/42G06K9/62H04N19/103H04N19/91
CPCH04N19/42H04N19/91H04N19/103G06F18/23213
Inventor 冯志玺赵世慧杨淑媛刘志徐光颖孟会晓
Owner XIDIAN UNIV
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