Hyperspectral Image Compression Method Based on Distributed Compressive Sensing
A hyperspectral image and compressed sensing technology, which is applied in the field of hyperspectral image compression, can solve the problems of insufficient redundancy removal, high calculation and complexity, and serious block effects of reconstructed images, and achieve enhanced anti-error performance, Effect of protecting useful information and improving accuracy
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Embodiment 1
[0090] Such as figure 1 As shown, this embodiment provides a hyperspectral image compression method based on distributed compressed sensing, and the method includes:
[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 Group, 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 the information entropy values of all bands in the hyperspectral image; combine the bands corresponding to the information entropy values greater than the first preset entropy value into a first set s 1 ;
[0094] 012. Obtain correlation coefficients r between all adjacent bands in the hyperspectral image; determine two bands corresponding to each correlation coe...
Embodiment 2
[0130] The traditional entropy method is used to select the band with rich information. Generally, these bands are treated specially. For example, in prediction-based technologies such as differential pulse modulation (DPCM), the selected band is generally used as a reference band to predict other non-reference bands. The accuracy of the prediction will be affected by the 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 curve between adjacent bands of hyperspectral images have similar trends, but the correlation curve and entropy curve do not completely overlap, and even the opposite occurs at the turning point of the curve. Trend, that is, when a certain band has a large entropy value, the correlation ...
Embodiment 3
[0200] The following describes the advantages of this embodiment in combination with experimental data and experimental results:
[0201] In the experiment, two hyperspectral images of Terrain and Cuprite are selected as objects, the sparse basis is DWT, the random observation matrix is part of the Hadamard matrix, and the CS reconstruction method is the Basis Pursuit (BP) method.
[0202] The result of band selection remains unchanged, and the grouping situation is 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 thinning. Under the same conditions (Intel single-core 2.66GHz / 32-bit operating system memory 2GB), the 3D-SPIHT algorithm, the IOI-DCS algorithm (using a fixed band grouping algorithm), and the CE-DCS algorithm are compared in this embodiment.
[0203] Table 3 and Table 4 respectively show the average peak signal-to-noise ratio (Average PSNR, APSNR) (average compr...
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