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Hyperspectral image lossless compression method based on RKLT and principal component selection

A hyperspectral image, lossless compression technology, applied in the field of remote sensing hyperspectral image processing, can solve the problems of unfavorable floating-point coefficients and processing, and achieve the effect of favorable processing and small storage space

Active Publication Date: 2015-01-07
HARBIN INST OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to propose a hyperspectral image lossless compression method based on RKLT and principal component selection, to solve the problem that the existing KLT method produces floating-point coefficients that are not conducive to hardware processing when hyperspectral image lossless compression is performed.

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  • Hyperspectral image lossless compression method based on RKLT and principal component selection

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Embodiment approach

[0021] combine figure 1 , Figure 5 The embodiment of the present invention is described, specifically as follows:

specific Embodiment approach 1

[0022] Specific embodiment one: a kind of hyperspectral image lossless compression method based on RKLT and principal component selection described in this embodiment comprises the following steps:

[0023] Step 1. Set the number of rows, columns, and bands to n x , n y , n z Convert the 3D hyperspectral image into row number n x ×n y , the number of columns is n z The 2D matrix I;

[0024] Step 2. The matrix I obtained in step 1 is generated through RKLT to generate four sizes all of n z ×n z The matrix T, H, M, N and a row number n x ×n y , the number of columns is n z The matrix Y_RKLT_THMN of transformation coefficients, and the elements of Y_RKLT_THMN are all integers, where T, H, M, N are generated by KLT in RKLT and the number of rows and columns is n z The matrix COEFF composed of eigenvectors is obtained through matrix decomposition;

[0025] Step 3, make Y_RKLT_THMN the nth pcs +1 column vector to nth z The column vectors are zero, and the RKLT inverse t...

specific Embodiment approach 2

[0033] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is that the image conversion method described in step 1 is: the hyperspectral image is 3D, and in order to perform RKLT transformation, the data needs to be transformed into a 2D form, that is, through such as Figure 5 The zig-zag scanning method is realized. As shown in the figure, the number of rows, columns, and bands are n x , n y , n z The 3D hyperspectral image forms a nx ×n y row n z Columns of 2D data. Other steps are the same as in the first embodiment.

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Abstract

The invention discloses a hyperspectral image lossless compression method based on the RKLT and principal component selection and belongs to the technical field of remote sensing hyperspectral image compression. The hyperspectral image lossless compression method based on the RKLT and principal component selection solves the problem that when an existing KLT method is used for hyperspectral image lossless compression, generated floating point number coefficients are not favorable for processing on hardware. According to the technical scheme, a hyperspectral image is converted into a 2D matrix from a 3D matrix; a transformation matrix is decomposed into four integer matrixes and transformation coefficients through the RKLT; RKLT reverse transformation is conducted on principal components which are selected from the transformation coefficients; subtracting is conducted on the matrix obtained after reverse transformation and the original 2D matrix, so that a residual error is obtained; predicting, forward mapping and section coding are conducted on the residual error and an RKLT forward transformation matrix of the selected principal components, so that a coding stream is formed; the transformation matrix generated by the KLT is stored into an RAW file, and the RAW file and the coding stream obtained in the last step serve as compressed data to be transmitted to a compression end; the number of the optimal principal components needing to be selected is found through a searching method. The hyperspectral image lossless compression method is suitable for conducting lossless compression on the hyperspectral image.

Description

technical field [0001] The invention relates to a remote sensing hyperspectral image processing method, in particular to a hyperspectral image compression method based on RKLT and principal component selection, and belongs to the technical field of remote sensing hyperspectral image compression. Background technique [0002] Hyperspectral remote sensing is another revolution in the development of remote sensing technology. It is a remote sensing science and technology with high spectral resolution. Based on Spectroscopy, it can generate dozens to hundreds of continuous band. Hyperspectral remote sensing has high spectral resolution, continuous spectrum, and wider application range. It uses many narrow electromagnetic wave bands to obtain a large amount of relevant data from objects of interest. It can obtain approximately continuous spectral information, covering the entire visible light to the near In the infrared (0.4-2.4 micron) spectral range, the band width is generall...

Claims

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Application Information

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IPC IPC(8): H04N19/61H04N19/129H04N19/597
Inventor 陈浩滑艺
Owner HARBIN INST OF TECH
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