CN113393543A - Hyperspectral image compression method, device and equipment and readable storage medium - Google Patents

Hyperspectral image compression method, device and equipment and readable storage medium Download PDF

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CN113393543A
CN113393543A CN202110662427.8A CN202110662427A CN113393543A CN 113393543 A CN113393543 A CN 113393543A CN 202110662427 A CN202110662427 A CN 202110662427A CN 113393543 A CN113393543 A CN 113393543A
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种衍文
郭圆圆
潘少明
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Wuhan University WHU
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Abstract

The invention provides a hyperspectral image compression method, a hyperspectral image compression device, hyperspectral image compression equipment and a readable storage medium. The method comprises the following steps: training a convolutional neural network through a training set, wherein the convolutional neural network comprises a nonlinear transformation module, a quantization module and an entropy model; and verifying the compression performance of the trained convolutional neural network by using the test set, and compressing the hyperspectral image by using the trained convolutional neural network when the compression performance of the trained convolutional neural network reaches the standard. The method has better rate distortion performance for the compression of the hyperspectral image.

Description

Hyperspectral image compression method, device and equipment and readable storage medium
Technical Field
The invention relates to the technical field of image processing, in particular to a hyperspectral image compression method, a hyperspectral image compression device, hyperspectral image compression equipment and a readable storage medium.
Background
The hyperspectral image has rich and unique spectral information, and great convenience is brought to a plurality of applications based on the hyperspectral image, such as tasks of crop classification, quality detection, disaster prediction and the like. However, the advantages also restrict the further development of the hyperspectral image under the limited transmission bandwidth and storage capacity. Therefore, how to effectively solve various challenges brought by the large data volume of the hyperspectral image is a precondition and a key for the hyperspectral image to be widely applied.
In the hyperspectral image compression algorithm, transform coding is widely applied due to smaller computational complexity and good adaptability. The image compression algorithm based on the transformation coding comprises four parts: transform, quantization and entropy coding, inverse transform, implementing separately the coding process and the decorrelation process.
The transformation is to transform an image from a pixel domain to a more compact space in a certain way, the existing hyperspectral image compression method based on transform coding generally assumes that a hyperspectral image is a Gaussian source, pixels can be mapped into independent potential representations only through reversible linear transformation under the condition, and latent variables are compressed into code streams for storage and transmission through quantization and entropy coding. However, the hyperspectral image of the actual scene has obvious non-gaussian characteristics, so that linear transformation is not applicable any more, and the exploration of the nonlinear transformation provides a new method and thought for the problem. In recent years, the development of nonlinear transformation using artificial neural networks, especially deep learning, as a tool has changed the situation of traditional manual parameter setting for image compression. The existing image compression technology based on deep learning has great potential, and the performance exceeds the H.266/VVC (Versatile Video Coding, VVC) standard in the industry. However, these methods are mostly used to process three-band natural images, and the compression for hyperspectral images is relatively small.
The transformation process enables quantization and entropy coding to be performed in a compact space, and spectra of the hyperspectral images have stronger correlation compared with RGB natural images, so that potential representations obtained by the hyperspectral images have different statistical properties from RGB images through the same transformation process. Upon quantization, the latent variable becomes a discrete form, which is then encoded based on an entropy encoding algorithm. The entropy coding process depends on a probability distribution model of latent variables, and the designed entropy model is closer to the real latent variable distribution, the code rate is smaller, and the solution obtained in the entropy rate optimization process is closer to the optimal solution.
In combination with the above analysis, the current compression technology based on deep learning needs to further design a more flexible and accurate entropy model according to the characteristics of the hyperspectral image so as to reduce the mismatch between the entropy model and the real latent variable distribution, thereby achieving the optimal rate distortion performance.
Disclosure of Invention
In order to solve the technical problems, the invention provides a hyperspectral image compression method, a hyperspectral image compression device, hyperspectral image compression equipment and a readable storage medium.
In a first aspect, the present invention provides a hyperspectral image compression method, including:
training a convolutional neural network through a training set, wherein the convolutional neural network comprises a nonlinear transformation module, a quantization module and an entropy model;
and verifying the compression performance of the trained convolutional neural network by using the test set, and compressing the hyperspectral image by using the trained convolutional neural network when the compression performance of the trained convolutional neural network reaches the standard.
Optionally, before the step of training the convolutional neural network through the training set, the method further includes:
cutting a sample hyperspectral image into a plurality of cubic blocks with fixed sizes in a spatial dimension;
and dividing the cuboids with fixed sizes into a training set and a testing set according to a preset proportion.
Optionally, the nonlinear transformation module performs forward nonlinear transformation on the space and spectrum dimensions of the hyperspectral image to obtain a latent variable; the quantization module quantizes the latent variable by adding uniform noise; the entropy model is used for obtaining the probability distribution of the latent variable, so that the code word allocated to each element in the latent variable is determined based on the probability distribution during entropy coding.
Optionally, the convolutional neural network training process is constrained based on a rate-distortion criterion, and is used to determine parameter values in the nonlinear transformation module and the entropy model.
Optionally, the nonlinear transformation includes a forward transformation: g represents Ya(WaX+ba) And (3) inverse transformation:
Figure BDA0003115884840000021
Figure BDA0003115884840000022
wherein,
Figure BDA0003115884840000023
representing the input hyperspectral image,
Figure BDA0003115884840000024
representing the reconstructed image, H, W and B respectively correspond to the row, column and wave band number of the hyperspectral image,
Figure BDA0003115884840000025
representing the latent variables, h, w, N respectively corresponding to the number of rows, columns and filters of the latent variables,
Figure BDA0003115884840000026
and
Figure BDA0003115884840000027
representing the parameters of the network being transformed,
Figure BDA0003115884840000031
and
Figure BDA0003115884840000032
representing inverse transform network parameters, ga(.) represents a nonlinear forward transformation function, gs(.) represents a non-linear inverse transformation function.
Optionally, the function for quantizing the latent variable by adding uniform noise is expressed as follows:
Figure BDA0003115884840000033
wherein training represents a training process, and testing represents a testing process,
Figure BDA0003115884840000034
representing unity uniform noise, round represents rounding operationIn order to do so,
Figure BDA0003115884840000035
representing the quantized latent vector.
Optionally, the statistical characteristics of the latent variables are introduced into the design of the entropy model, and an additional variable is introduced to construct a condition model, so as to improve the precision of the entropy model.
In a second aspect, the present invention also provides a hyperspectral image compression apparatus comprising:
the training module is used for training a convolutional neural network through a training set, wherein the convolutional neural network comprises a nonlinear transformation module, a quantization module and an entropy model;
and the processing module is used for verifying the compression performance of the trained convolutional neural network by using the test set, and compressing the hyperspectral image by using the trained convolutional neural network when the compression performance of the trained convolutional neural network reaches the standard.
In a third aspect, the present invention further provides a hyperspectral image compression apparatus comprising a processor, a memory, and a hyperspectral image compression program stored on the memory and executable by the processor, wherein the hyperspectral image compression program, when executed by the processor, implements the steps of the hyperspectral image compression method as described above.
In a fourth aspect, the present invention further provides a readable storage medium, on which a hyperspectral image compression program is stored, where the hyperspectral image compression program, when executed by a processor, implements the steps of the hyperspectral image compression method as described above.
In the invention, a convolutional neural network is trained through a training set, wherein the convolutional neural network comprises a nonlinear transformation module, a quantization module and an entropy model; and verifying the compression performance of the trained convolutional neural network by using the test set, and compressing the hyperspectral image by using the trained convolutional neural network when the compression performance of the trained convolutional neural network reaches the standard. The method has better rate distortion performance for the compression of the hyperspectral image.
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FIG. 1 is a schematic diagram of a hardware structure of a hyperspectral image compression device according to an embodiment of the invention;
FIG. 2 is a schematic flow chart of an embodiment of a hyperspectral image compression method according to the invention;
FIG. 3 is a functional block diagram of an embodiment of the hyperspectral image compression apparatus of the invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In a first aspect, an embodiment of the present invention provides a hyperspectral image compression apparatus, where the hyperspectral image compression apparatus may be an apparatus with a data processing function, such as a Personal Computer (PC), a notebook computer, and a server.
Referring to fig. 1, fig. 1 is a schematic diagram of a hardware structure of a hyperspectral image compression device according to an embodiment of the present invention. In an embodiment of the present invention, the hyperspectral image compression apparatus may include a processor 1001 (e.g., a Central Processing Unit, CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used for realizing connection communication among the components; the user interface 1003 may include a Display screen (Display), an input unit such as a Keyboard (Keyboard); the network interface 1004 may optionally include a standard wired interface, a WIreless interface (e.g., a WI-FI interface, WI-FI interface); the memory 1005 may be a Random Access Memory (RAM) or a non-volatile memory (non-volatile memory), such as a magnetic disk memory, and the memory 1005 may optionally be a storage device independent of the processor 1001. Those skilled in the art will appreciate that the hardware configuration depicted in FIG. 1 is not intended to be limiting of the present invention, and may include more or less components than those shown, or some components in combination, or a different arrangement of components.
With continued reference to FIG. 1, the memory 1005 of FIG. 1, which is one type of computer storage medium, may include an operating system, a network communication module, a user interface module, and a hyper-spectral image compression program. The processor 1001 may call a hyperspectral image compression program stored in the memory 1005, and execute the hyperspectral image compression method provided by the embodiment of the present invention.
In a second aspect, an embodiment of the present invention provides a hyperspectral image compression method.
In an embodiment, referring to fig. 2, fig. 2 is a schematic flowchart of an embodiment of a hyperspectral image compression method according to the invention. As shown in fig. 2, the hyperspectral image compression method includes:
step S10, training a convolutional neural network through a training set, wherein the convolutional neural network comprises a nonlinear transformation module, a quantization module and an entropy model;
in this embodiment, a training set is pre-constructed, and a convolutional neural network is trained through the training set, where the convolutional neural network includes a nonlinear transformation module, a quantization module, and an entropy model.
Further, in an embodiment, before step S10, the method further includes:
cutting a sample hyperspectral image into a plurality of cubic blocks with fixed sizes in a spatial dimension; and dividing the cuboids with fixed sizes into a training set and a testing set according to a preset proportion.
In this embodiment, before training, a data set, including a training set and a test set, needs to be prepared, and a hyper-parameter of the convolutional neural network needs to be set. For example, 27 KAIST data sets (30) with a size of 2704 × 3376 × 31 and 28 CAVE data (32) with a size of 512 × 512 × 31 are randomly cropped into image blocks with a size of 128 × 128 × 31. The experiment adopts tensierflow frame to train model, sends cut 128 × 128 × 31 image block batch (batch 32) into the built network, iterates 500000 times, and the loss function in training is as follows:
Figure BDA0003115884840000051
wherein, the approximate posterior
Figure BDA0003115884840000052
Using a unit uniform density function of the full decomposition, thus losing the first term of the function
Figure BDA0003115884840000053
Figure BDA0003115884840000054
Expressing a distortion item, performing distortion measurement by adopting Mean Square Error (MSE) loss with a parameter lambda in a training process, wherein the value of lambda is from 0.00001 to 0.01, and the range of bppp (the number of bits occupied by each pixel of each band to be coded) can be controlled to be between 0.1 and 2 (the larger the lambda is, the larger the bppp is);
Figure BDA0003115884840000055
representing the total number of coded bits.
Further, in one embodiment, the nonlinear transformation module performs forward nonlinear transformation on the space and spectrum dimensions of the hyperspectral image to obtain a latent variable; the quantization module quantizes the latent variable by adding uniform noise; the entropy model is used for obtaining the probability distribution of the latent variable, so that the code word allocated to each element in the latent variable is determined based on the probability distribution during entropy coding.
In the embodiment, a training set is input into a convolutional neural network, and a nonlinear transformation module in the convolutional neural network carries out forward nonlinear transformation on the space and spectrum dimensions of a hyperspectral image, so that the image is mapped to a compact latent space from a pixel domain to obtain a latent variable; then, a quantization module in the convolutional neural network quantizes the latent variable by adding uniform noise, the gradient of back propagation is 0 due to quantization in the training process, in order to ensure smooth training, the quantization process is replaced by adding uniform noise in the embodiment, so that the quantization is conducted, and the test process is directly rounded; then, the probability distribution of the latent variable is obtained based on the entropy model, so that when entropy coding is carried out, the code word assigned to each element in the latent variable (namely, each element uses several characters) is determined based on the probability distribution.
Further, in one embodiment, the convolutional neural network training process is constrained based on a rate-distortion criterion to determine the nonlinear transformation module and the parameter values in the entropy model.
In this embodiment, a rate distortion criterion is adopted to solve parameter values in the nonlinear transformation module and the entropy model, a concept of variation inference is combined with rate distortion in an optimization process, and a rate distortion optimization process is explained from a probability perspective:
Figure BDA0003115884840000061
wherein,
Figure BDA0003115884840000062
the reconstruction image is represented, d (·) represents a distortion measurement criterion, a peak signal to noise ratio (PSNR) is generally adopted in the hyperspectral image, the structural similarity SSIM is used for carrying out distortion measurement on the pixel, the larger the value is, the better the reconstruction effect of the pixel is, a spectrum angle SAM is used for measuring the reconstruction accuracy of the spectrum, and lambda represents a Lagrange multiplier.
In order to optimize the loss function, the thought of variation inference is adopted, parameter solution is carried out by designing an approximate posterior to approximate to a real posterior, KL divergence is adopted to measure two posteriors, and a calculation formula is as follows:
Figure BDA0003115884840000063
wherein,
Figure BDA0003115884840000064
representing an approximate posterior, can be represented by any simple distribution, and is typically represented as full in compressionThe units of decomposition are evenly distributed so as to remove this term from the loss function, while the remaining three terms except the constant term const,
Figure BDA0003115884840000065
corresponding distortion
Figure BDA0003115884840000066
Figure BDA0003115884840000067
Corresponding code rate
Figure BDA0003115884840000068
Figure BDA0003115884840000069
Corresponding additional information
Figure BDA00031158848400000610
Further, in an embodiment, the non-linear transform comprises a forward transform: g represents Ya(WaX+ba) And (3) inverse transformation:
Figure BDA0003115884840000071
wherein,
Figure BDA0003115884840000072
representing the input hyperspectral image,
Figure BDA0003115884840000073
representing the reconstructed image, H, W and B respectively correspond to the row, column and wave band number of the hyperspectral image,
Figure BDA0003115884840000074
representing the latent variables, h, w, N respectively corresponding to the number of rows, columns and filters of the latent variables,
Figure BDA0003115884840000075
and
Figure BDA0003115884840000076
Figure BDA0003115884840000077
representing the parameters of the network being transformed,
Figure BDA0003115884840000078
and
Figure BDA0003115884840000079
representing inverse transform network parameters, ga(.) represents a nonlinear forward transformation function, gs(.) represents a non-linear inverse transformation function.
Further, in one embodiment, the function for quantizing the latent variable by adding uniform noise is expressed as follows:
Figure BDA00031158848400000710
wherein training represents a training process, and testing represents a testing process,
Figure BDA00031158848400000711
indicating unity uniform noise, round indicates a rounding operation,
Figure BDA00031158848400000712
representing the quantized latent vector.
In this embodiment, the latent variable is quantized, unit uniform noise approximation is adopted during training, and a rounding mode is adopted during testing.
Further, in one embodiment, statistical properties of latent variables are introduced into the design of the entropy model, and at the same time, additional variables are introduced to construct a condition model, so as to improve the accuracy of the entropy model.
In the embodiment, the statistical characteristics of the latent variables are introduced into the design of the entropy model to reduce the difference between the entropy model and the distribution of the real latent variables, and the smaller the difference between the entropy model and the real latent variables is, the smaller the obtained code rate is, and a certain priori cognition can be added into the latent layer representation to improve the precision of the entropy model. Here, a condition model is constructed by introducing additional variables, and the calculation formula is as follows:
Figure BDA00031158848400000713
wherein,
Figure BDA00031158848400000714
a representation of the quantized latent layer is represented,
Figure BDA00031158848400000715
a conditional entropy model is represented by a model of conditional entropy,
Figure BDA00031158848400000716
representing additional variables
Figure BDA00031158848400000717
As a priori information of the entropy model,
Figure BDA00031158848400000718
representing the true distribution of the latent representation.
In the design of the entropy model, the statistic prior represented by a potential layer is added, and the parameter is solved by using a convolutional neural network:
Figure BDA00031158848400000719
wherein f represents a distribution capable of describing the statistical characteristics of the latent layer representation, if the Gaussian characteristics are obvious, the distribution can be represented as Gaussian distribution, and if the non-Gaussian characteristics are obvious, T distribution, Laplace distribution and the like can be selected; the choice of f is determined by the statistical properties of the latent layer representation. The parameter of f is obtained by convolutional neural network learning, namely, on the premise of determining the type of f, the parameter information of f distribution is learned by using variables.
After the hyperspectral image is subjected to nonlinear transformation of the convolutional neural network, the distribution of latent variables has obvious non-Gaussian characteristics, so that the prior information needs to be added during the design of an entropy model, and the characteristic can be well captured by finding t distribution in the experimental process, so that the t distribution is selected to model the latent variables of the hyperspectral image.
In order to make the whole compression process microminiaturible, the quantization process adopts additive unit uniform noise approximation; in order to make the entropy model more fit with the posterior distribution, when the entropy model is designed, the convolution is uniformly distributed by one unit, and the formula is as follows:
Figure BDA0003115884840000081
wherein eta isiA scale parameter representing the t-distribution (similar but not equal to the variance), and v represents a degree of freedom, the shape of the t-distribution can be adjusted. c represents the analytical form of the probability distribution of the entropy model. Using additional variables for parametric variables of probability distributions
Figure BDA0003115884840000082
Through the prior network.
Entropy coding adopts arithmetic coding, in the process of arithmetic coding, an entropy model provides probability distribution for the processes of arithmetic coding and arithmetic decoding, the size of code words distributed to the entropy coding model (namely, each symbol occupies a plurality of bits) is determined according to the probability distribution of each element in latent variables, and after entropy coding, the latent variables are changed into a binary code stream for storage or transmission.
And step S20, verifying the compression performance of the trained convolutional neural network by using the test set, and compressing the hyperspectral image by the trained convolutional neural network when the compression performance of the trained convolutional neural network reaches the standard.
In this embodiment, a uniform noise approximate quantization process is used for training, and an rounding mode is directly used for testing. The entropy coding adopts common arithmetic coding, and minimizes the rate distortion loss training model until convergence. The whole large graph is directly put in during testing, and on the CAVE data set, when the degree of freedom is 21, the effects that the bppp is 0.1219, the PSNR is 36.74dB, the SSIM is 0.9175 and the SAM is 0.2137 can be achieved. At 20 degrees of freedom, on the KAIST data set, the effects of the PSNR being 39.99dB, the SSIM being 0.9524 and the SAM being 0.2331 can be achieved when the bppp is 0.0885.
If the user needs to use the image information, the binary code stream can be reduced into latent variables through arithmetic decoding, then the latent variables are input into an inverse transformation network consisting of two space and spectrum modules, in the inverse transformation network, the space and spectrum modules are connected by IGDN, and the up-sampling is recovered to the original image size. The compression frame is divided into four parts including transformation network, quantization, entropy coding and inverse transformation network.
In this embodiment, for the anisotropy of the hyperspectral image, a spatial and spectral convolution module (SS module, including SS module _ down (for constructing an encoding network) and SS module _ up (for constructing a decoding network)) is proposed, and the SS module _ down and the SS module _ up are connected by a GDN. In SS module _ down, for an image tensor with spectral dimension B (B × H × W), a first layer of down-sampling is followed by inputting a filter of 5 × B, generating N feature representations; after GDN and one down sampling, 5 × N convolution layers are input to generate B feature representations, and N feature representations are generated through 1 × B convolution layers. The process of SS module _ up is similar to SS module _ down, but downsampling is replaced with upsampling. When the spectrum module is designed, the spectrum dimension of the hyperspectral image is introduced into the network, the rearrangement of the spectrum information is realized, and therefore the correlation among the spectra is reduced. Aiming at the non-Gaussian characteristics represented by the latent layer of the hyperspectral image, the assumption of traditional Gaussian distribution is not adopted during the design of the entropy model, and the matching degree of the entropy model and the latent layer representation statistical distribution is improved by introducing some non-Gaussian distributions as statistical prior of the latent layer representation. According to the fitting of latent variables of the hyperspectral data sets CAVE and KAIST data sets, the t distribution is found to be good in performance, meanwhile, the distribution shape can be flexibly changed by adjusting the degree of freedom, and when the degree of freedom tends to be infinite, the t distribution and the Gaussian distribution can be equivalent. This property enables the t-distribution to capture both the non-gaussian property of the latent layer representation and the universality of the gaussian distribution.
In this embodiment, a convolutional neural network is trained through a training set, wherein the convolutional neural network includes a nonlinear transformation module, a quantization module, and an entropy model; and verifying the compression performance of the trained convolutional neural network by using the test set, and compressing the hyperspectral image by using the trained convolutional neural network when the compression performance of the trained convolutional neural network reaches the standard. Through the embodiment, the method has better rate distortion performance for the compression of the hyperspectral image.
In a third aspect, an embodiment of the present invention further provides a hyperspectral image compression apparatus.
In an embodiment, referring to fig. 3, fig. 3 is a functional module schematic diagram of an embodiment of a hyperspectral image compression device according to the invention. As shown in fig. 3, the hyperspectral image compression apparatus includes:
the training module 10 is used for training a convolutional neural network through a training set, wherein the convolutional neural network comprises a nonlinear transformation module, a quantization module and an entropy model;
and the processing module 20 is configured to verify the compression performance of the trained convolutional neural network by using the test set, and compress the hyperspectral image by using the trained convolutional neural network when the compression performance of the trained convolutional neural network meets the standard.
Further, in an embodiment, the hyperspectral image compression apparatus further includes a construction module configured to:
cutting a sample hyperspectral image into a plurality of cubic blocks with fixed sizes in a spatial dimension;
and dividing the cuboids with fixed sizes into a training set and a testing set according to a preset proportion.
Further, in one embodiment, the nonlinear transformation module performs forward nonlinear transformation on the space and spectrum dimensions of the hyperspectral image to obtain a latent variable; the quantization module quantizes the latent variable by adding uniform noise; the entropy model is used for obtaining the probability distribution of the latent variable, so that the code word allocated to each element in the latent variable is determined based on the probability distribution during entropy coding.
Further, in one embodiment, the convolutional neural network training process is constrained based on a rate-distortion criterion to determine the nonlinear transformation module and the parameter values in the entropy model.
Further, in an embodiment, the non-linear transform comprises a forward transform: g represents Ya(WaX+ba) And (3) inverse transformation:
Figure BDA0003115884840000101
wherein,
Figure BDA0003115884840000102
representing the input hyperspectral image,
Figure BDA0003115884840000103
representing the reconstructed image, H, W and B respectively correspond to the row, column and wave band number of the hyperspectral image,
Figure BDA0003115884840000104
representing the latent variables, h, w, N respectively corresponding to the number of rows, columns and filters of the latent variables,
Figure BDA0003115884840000105
and
Figure BDA0003115884840000106
Figure BDA0003115884840000107
representing the parameters of the network being transformed,
Figure BDA0003115884840000108
and
Figure BDA0003115884840000109
representing inverse transform network parameters, ga(.) represents a nonlinear forward transformation function, gs(.) represents a non-linear inverse transformation function.
Further, in one embodiment, the function for quantizing the latent variable by adding uniform noise is expressed as follows:
Figure BDA00031158848400001010
wherein training represents a training process, and testing represents a testing process,
Figure BDA00031158848400001011
indicating unity uniform noise, round indicates a rounding operation,
Figure BDA00031158848400001012
representing the quantized latent vector.
Further, in one embodiment, statistical properties of latent variables are introduced into the design of the entropy model, and at the same time, additional variables are introduced to construct a condition model, so as to improve the accuracy of the entropy model.
The function implementation of each module in the hyperspectral image compression device corresponds to each step in the hyperspectral image compression method embodiment, and the functions and the implementation process are not described in detail herein.
In a fourth aspect, the embodiment of the present invention further provides a readable storage medium.
The readable storage medium of the present invention stores a hyper-spectral image compression program, wherein the hyper-spectral image compression program, when executed by a processor, implements the steps of the hyper-spectral image compression method as described above.
The method implemented when the hyper-spectral image compression program is executed may refer to each embodiment of the hyper-spectral image compression method of the present invention, and details are not repeated here.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) as described above and includes instructions for causing a terminal device to execute the method according to the embodiments of the present invention.
The above description is only a preferred embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes, which are made by using the contents of the present specification and the accompanying drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.

Claims (10)

1. A hyperspectral image compression method is characterized by comprising the following steps:
training a convolutional neural network through a training set, wherein the convolutional neural network comprises a nonlinear transformation module, a quantization module and an entropy model;
and verifying the compression performance of the trained convolutional neural network by using the test set, and compressing the hyperspectral image by using the trained convolutional neural network when the compression performance of the trained convolutional neural network reaches the standard.
2. The hyperspectral image compression method of claim 1, further comprising, prior to the step of training the convolutional neural network through a training set:
cutting a sample hyperspectral image into a plurality of cubic blocks with fixed sizes in a spatial dimension;
and dividing the cuboids with fixed sizes into a training set and a testing set according to a preset proportion.
3. The hyperspectral image compression method according to claim 2, wherein the nonlinear transformation module performs forward nonlinear transformation on the space and spectrum dimensions of the hyperspectral image to obtain latent variables; the quantization module quantizes the latent variable by adding uniform noise; the entropy model is used for obtaining the probability distribution of the latent variable, so that the code word allocated to each element in the latent variable is determined based on the probability distribution during entropy coding.
4. The hyperspectral image compression method of claim 3, wherein the convolutional neural network training process is constrained based on a rate-distortion criterion to determine parameter values in the nonlinear transformation module and the entropy model.
5. The hyperspectral image compression method of claim 4, wherein the nonlinear transformation comprises a forward transformation: g represents Ya(WaX+ba) And (3) inverse transformation:
Figure FDA0003115884830000011
wherein,
Figure FDA0003115884830000012
representing the input hyperspectral image,
Figure FDA0003115884830000013
representing the reconstructed image, H, W and B respectively correspond to the row, column and wave band number of the hyperspectral image,
Figure FDA0003115884830000014
representing the latent variables, h, w, N respectively corresponding to the number of rows, columns and filters of the latent variables,
Figure FDA0003115884830000015
and
Figure FDA0003115884830000016
representing the parameters of the network being transformed,
Figure FDA0003115884830000017
Figure FDA0003115884830000021
and
Figure FDA0003115884830000022
representing inverse transform network parameters, ga(.) represents a nonlinear forward transformation function, gs(.) represents a non-linear inverse transformation function.
6. The hyperspectral image compression method according to claim 5, wherein the function for quantizing the latent variable by adding uniform noise is expressed as follows:
Figure FDA0003115884830000023
wherein training represents a training process, and testing represents a testing process,
Figure FDA0003115884830000025
indicating unity uniform noise, round indicates a rounding operation,
Figure FDA0003115884830000024
representing the quantized latent vector.
7. The hyperspectral image compression method according to claim 6, wherein the statistical properties of latent variables are introduced into the design of the entropy model, and simultaneously, additional variables are introduced to construct a conditional model so as to improve the accuracy of the entropy model.
8. A hyperspectral image compression apparatus, characterized in that the hyperspectral image compression apparatus comprises:
the training module is used for training a convolutional neural network through a training set, wherein the convolutional neural network comprises a nonlinear transformation module, a quantization module and an entropy model;
and the processing module is used for verifying the compression performance of the trained convolutional neural network by using the test set, and compressing the hyperspectral image by using the trained convolutional neural network when the compression performance of the trained convolutional neural network reaches the standard.
9. A hyperspectral image compression apparatus comprising a processor, a memory, and a hyperspectral image compression program stored on the memory and executable by the processor, wherein the hyperspectral image compression program when executed by the processor implements the steps of the hyperspectral image compression method according to any of claims 1 to 7.
10. A readable storage medium having stored thereon a hyper-spectral image compression program, wherein the hyper-spectral image compression program when executed by a processor implements the steps of the hyper-spectral image compression method according to any one of claims 1 to 7.
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