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Distributed compression sensing-based hyperspectral image compression method

A hyperspectral image and compressed sensing technology, applied in the field of hyperspectral image compression, can solve the problems of insufficient redundancy removal, serious block effect of reconstructed images, high calculation amount and complexity, and achieve enhanced anti-error performance and improved performance. Accuracy, Effectiveness of Protecting Useful Information

Inactive Publication Date: 2017-09-01
NORTHEASTERN UNIV LIAONING
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, in the existing technology, the 3D-SPIHT compression algorithm that can support the protection of information of interest in hyperspectral images is essentially a method based on transformation. The block effect of the reconstructed image is relatively serious. In addition, the 3D-SPIHT compression algorithm fails to make full use of the strong spectral correlation of the hyperspectral image, and the removal of redundancy is not thorough enough.

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  • Distributed compression sensing-based hyperspectral image compression method
  • Distributed compression sensing-based hyperspectral image compression method
  • Distributed compression sensing-based hyperspectral image compression method

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Embodiment 1

[0090] like figure 1 As shown, the present embodiment provides a hyperspectral image compression method based on distributed compressed sensing, the method comprising:

[0091] Step 01: For the hyperspectral image to be processed, based on the correlation of each band in the hyperspectral image and the information entropy value of each band, divide all the bands of the hyperspectral image into a low-correlation band group and multiple high-correlation bands Groups, each high-correlation band group includes: a reference band and multiple non-reference bands;

[0092] For example, this step 01 may include:

[0093] 011. Obtain information entropy values ​​of all bands in the hyperspectral image; form bands corresponding to information entropy values ​​greater than the first preset entropy value into a first set s 1 ;

[0094] 012. Obtain the correlation coefficient r between all adjacent bands in the hyperspectral image; determine two bands corresponding to each correlation c...

Embodiment 2

[0130] The traditional entropy method is used to select bands with rich information. Usually these bands are treated specially, for example, in prediction-based techniques like Differential Pulse Modulation (DPCM), the selected bands are usually used as reference bands to predict other non-reference bands. The prediction accuracy will be affected by the strength of correlation between the selected band and other bands in the group. If the band selected according to the entropy method has little correlation with the remaining bands in the group, the corresponding prediction error will be large. . From the analysis of the characteristics of hyperspectral images, the correlation and entropy curves between adjacent bands of hyperspectral images have similar trends, but the correlation curves and entropy curves do not completely coincide, and even appear opposite at the turning point of the curves. Trend, that is to say, when a band has a large entropy value, the correlation with ...

Embodiment 3

[0200] The superiority of this embodiment is described below in conjunction with experimental data and experimental results:

[0201] Two hyperspectral images of Terrain and Cuprite were selected as objects in the experiment, DWT was used as the sparse basis, the random observation matrix was a partial Hadamard matrix, and the CS reconstruction method was the Basis Pursuit (BP) method.

[0202]The band selection results remain unchanged, and the grouping conditions are shown in Table 1 and Table 2. Sampling rate SR=M / N×100%, M is the number of rows of the observation matrix, and N is the length of the original signal after sparse. Under the same conditions (Intel single-core 2.66GHz / 32-bit operating system memory 2GB), in this embodiment, the 3D-SPIHT algorithm, the IOI-DCS algorithm (the algorithm using fixed band grouping) and the CE-DCS algorithm are compared.

[0203] Table 3 and Table 4 respectively give the average peak signal-to-noise ratio (Average PSNR, APSNR) of the...

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Abstract

The invention provides a distributed compression sensing-based hyperspectral image compression method. The method comprises the steps of based on correlation of each band spectrum and information entropy of each band spectrum in a hyperspectral image, dividing all bands into a low correlation band group and multiple high correlation band groups, wherein each high correlation band group comprises a reference band and multiple non-reference bands; determining a region of interest and a background region in the reference band; for each non-reference band, performing differential processing on the region of interest and background region in the reference band, so as to acquire corresponding residual images; sequentially compressing residual images and low correlation band images of the region of interest and background region of each high correlation band group and each non-reference band; and sending code streams of all compression codes. According to the method, different distributed compression processing is performed on different bands and different regions, so that important information of the hyperspectral image is fully protected, and the compression rate of the hyperspectral image is improved.

Description

technical field [0001] The invention relates to hyperspectral image compression technology, in particular to a hyperspectral image compression method based on distributed compressed sensing. Background technique [0002] Hyperspectral images are a collection of multiple band images that contain both spatial and spectral information, and have been applied in many fields, such as agriculture, military, geological exploration, and environmental monitoring. However, with the continuous improvement of spatial resolution and spectral resolution, massive amounts of data have been brought. These massive data have brought great challenges to the storage, transmission and application of hyperspectral images, so how to efficiently realize hyperspectral data compression has become an urgent problem to be solved. [0003] However, among the many hyperspectral image compression algorithms proposed, most of them adopt the same processing for most bands and spatial regions in the hyperspec...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04N19/157H04N19/167H04N19/17H04N19/85
CPCH04N19/157H04N19/167H04N19/17H04N19/85
Inventor 郎俊葛锋安继成
Owner NORTHEASTERN UNIV LIAONING
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