CN116306819B - Hyperspectral cross calibration method and device based on spectrum reconstruction and electronic equipment - Google Patents
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Abstract
The invention belongs to the technical field of spectrum calibration, and relates to a hyperspectral cross calibration method and device based on spectrum reconstruction, and electronic equipment, wherein the method comprises the following steps: establishing a convolutional neural network model with a multi-head attention mechanism; selecting hyperspectral wave band data of a target wave band; establishing a long-term and short-term memory neural network model, and training the model; reconstructing hyperspectral band data to obtain hyperspectral data of a target band; cross-scaling is performed using target band hyperspectral data. The method effectively solves the problem that the traditional cross calibration requires similar spectrum bands between the remote sensor to be calibrated and the target remote sensor, trains the convolutional neural network model with a multi-head attention mechanism to enable the convolutional neural network model to have good band selection capacity and screen out a band with stronger representativeness in hyperspectral data, performs spectrum reconstruction by utilizing a long-term and short-term memory network after band selection, fits out required band information, and further meets the requirement of cross calibration.
Description
Technical Field
The invention belongs to the technical field of spectrum calibration, and particularly relates to a method and a device for cross calibration based on spectrum reconstruction and electronic equipment.
Background
The trend of current remote sensing applications gradually goes from qualitative to quantitative, which puts higher demands on the reliability of radiation calibration. Although the traditional site replacement calibration method has higher calibration precision, the traditional site replacement calibration method has a plurality of limitations and is difficult to develop in a large scale and high frequency. Cross scaling is a field-free scaling technique that has developed relatively rapidly in recent years and which solves the problem of a high field scaling threshold.
The idea of cross calibration is that when the in-orbit satellite remote sensor to be calibrated and the satellite remote sensor with better calibration result observe the same area at the same time and similar angles, the calibration of the satellite remote sensor to be calibrated can be realized by comparing the measured values of the in-orbit satellite remote sensor to be calibrated and the satellite remote sensor with better calibration result. Compared with the field replacement calibration technology, the method can calibrate the satellite data of multiple remote sensors without establishing a ground correction field. Its advantages are low cost and high-frequency radiation calibration between multiple remote sensors. Liu Jiaxin et al used MODIS satellites in 2021 to cross-scale the thermal infrared channels of China's wind cloud satellite No. 4. While the constraints of cross-scaling are reduced compared to site scaling, cross-scaling requires that the remote sensor to be scaled have a similar spectral range as the target remote sensor, which results in some remote sensors to be scaled lacking available target remote sensors with similar spectral ranges for reference.
Disclosure of Invention
In order to solve the technical problems, the invention provides a hyperspectral cross calibration method and device based on spectrum reconstruction and electronic equipment.
In a first aspect, the present invention provides a hyperspectral cross-scaling method based on spectral reconstruction, comprising:
Establishing a convolutional neural network model with a multi-head attention mechanism;
selecting hyperspectral wave band data of a target wave band by using the convolutional neural network model;
Establishing a long-term memory neural network model, and training the long-term memory neural network model by adopting a back propagation algorithm;
reconstructing the hyperspectral band data of the selected target band by using the trained long-short-term memory neural network model to obtain reconstructed hyperspectral data of the target band;
and performing cross scaling by using the target band hyperspectral data.
In a second aspect, the invention provides a hyperspectral cross scaling device based on spectrum reconstruction, which comprises a model building unit, a selecting unit, a model training unit, a reconstruction unit and a cross scaling unit;
The model building unit is used for building a convolutional neural network model with a multi-head attention mechanism;
the selection unit is used for selecting hyperspectral wave band data of a target wave band by utilizing the convolutional neural network model;
the model training unit is used for building a long-period memory neural network model and training the long-period memory neural network model by adopting a back propagation algorithm;
The reconstruction unit is used for reconstructing the hyperspectral band data of the selected target band by using the trained long-short-term memory neural network model to obtain reconstructed hyperspectral data of the target band;
The cross scaling unit is used for cross scaling by using the target band hyperspectral data.
In a third aspect, the present invention provides an electronic device, comprising:
A processor and a memory;
the memory is used for storing computer operation instructions;
the processor is used for executing the hyperspectral cross scaling method based on spectrum reconstruction by calling the computer operation instruction.
The beneficial effects of the invention are as follows: the method effectively solves the problem that the traditional cross calibration requires similar spectrum bands between the remote sensor to be calibrated and the target remote sensor, trains the convolutional neural network model with a multi-head attention mechanism to enable the convolutional neural network model to have good band selection capacity and screen out a band with stronger representativeness in hyperspectral data, performs spectrum reconstruction by utilizing a long-term and short-term memory network after band selection, fits out required band information, and further meets the requirement of cross calibration.
On the basis of the technical scheme, the invention can be improved as follows.
Furthermore, a plurality of multi-head attention modules and a full connection layer are adopted as the basic structure of the long-short-period memory neural network model, and a step function is added as a filter during output.
Further, selecting hyperspectral band data of a target band using the convolutional neural network model includes: performing feature extraction on the hyperspectral wave band data to obtain a weight matrix of a target wave band; and screening the weight matrix of the target wave band, and reserving the hyperspectral wave band with the largest weight by using a step function to obtain the hyperspectral wave band data of the target wave band.
And when the trained long-short-term memory neural network model is used for reconstructing the hyperspectral wave band data of the selected target wave band, the radiance of each wave band is used as one-dimensional characteristics, and the filtered wave band set is used as a sequence to perform long-short-term memory neural network calculation.
Further, updating the parameters of the convolutional neural network model by adopting a back propagation algorithm, and calculating the mean square error between an original matrix and a matrix reconstructed by using the convolutional neural network model; and determining a wave band meeting the reconstruction of the original full-wave band information according to the mean square error.
Further, cross-scaling using the target band hyperspectral data, comprising:
After the reconstructed target band hyperspectral data are obtained, one-dimensional radiance information is expanded and calculated to obtain a multidimensional tensor;
Performing dimension reduction operation on the tensor by using two linear regression networks, and fitting the reconstructed wave band information to obtain radiance;
Inputting data into the trained long-short-period memory neural network model, and fitting out the radiance of a missing wave band;
and taking the fitted wave band radiance as a target value, and calculating a scaling coefficient by combining with the DN value of the remote sensor to be scaled to finish cross scaling.
Drawings
FIG. 1 is a flow chart of a hyperspectral cross scaling method based on spectral reconstruction provided in example 1 of the present invention;
FIG. 2 is a schematic diagram of a hyperspectral cross-scaling apparatus based on spectral reconstruction in accordance with the present invention;
Fig. 3 is a schematic diagram of an electronic device in embodiment 3 of the present invention.
Icon: 30-an electronic device; 310-a processor; 320-bus; 330-memory; 340-transceiver.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. The components of the embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Example 1
As an embodiment, as shown in fig. 1, to solve the above technical problem, the present embodiment provides a hyperspectral cross scaling method based on spectrum reconstruction, including:
Establishing a convolutional neural network model with a multi-head attention mechanism;
selecting hyperspectral wave band data of a target wave band by using a convolutional neural network model;
establishing a long-term memory neural network model, and training the long-term memory neural network model by adopting a back propagation algorithm;
reconstructing hyperspectral band data of the selected target band by using the trained long-term and short-term memory neural network model to obtain reconstructed hyperspectral band data of the target band;
cross-scaling is performed using target band hyperspectral data.
In the practical application process, a convolutional neural network model with a multi-head attention mechanism is established, and the function of band selection is realized through model training.
Optionally, a plurality of multi-head attention modules and a full connection layer are adopted as the basic structure of the long-short-period memory neural network model, and a step function is added as a filter during output.
In the practical application process, when a convolutional neural network model with a multi-head attention mechanism is established, the method specifically comprises the following steps:
s11: adopting 64 multi-head attention modules and a full connection layer as basic structures of a model to be trained, and adding a step function as a filter during output;
s12: constructing a convolutional neural network structure, and reconstructing an original matrix by using the filtered low-rank matrix by using multi-layer convolution;
S13: and updating network parameters by adopting a back propagation algorithm, calculating the mean square error between the original matrix and the matrix reconstructed by using the convolutional neural network, and analyzing the difference between the original matrix and the reconstructed matrix, wherein the smaller the mean square error is, the smaller the difference is. ; and selecting hyperspectral wave band data of a target wave band by using a convolutional neural network model with a multi-head attention mechanism, wherein the smaller the difference is, the more successful the wave band is selected, otherwise, the larger the difference is, the less representative the selected wave band is and the redundancy is high.
The characteristic extraction capacity of the convolutional neural network is enhanced by adopting an attention mechanism, a large number of redundant hyperspectral wave bands are subjected to characteristic extraction, a weight matrix of the wave bands is obtained, the wave band with the most representativeness is selected, and a ij is set as attention distribution; softmax is the activation function; s (k j,qi) is a scoring function for attention; exp (s (k j,qi)) is an exponential mapping of the attention scoring function; context i is input data; the attention mechanism formula is as follows:
When the attention network is used to weight the band matrix, the size of the input data X is not changed, and only the selected band value is 1 in the tensor X with the size of h×w×s, and the other unselected band values are 0. Let h i be the ith attention head; w i (q) is a learnable parameter of query q; w i (k) is a learnable parameter for key k; w i (v) is a learnable parameter of value v; r Pv is the domain of action of attention; the multi-head attention algorithm formula is as follows:
hi＝f(Wi (q)q,Wi (k)k,Wi (v)v)∈RPv；
H i is the ith attention head, q is the query, k is the key, v is the value, linear conversion is needed after the output of a plurality of attention heads is obtained, and the results of the plurality of attention heads are spliced according to the following formula:
Optionally, selecting hyperspectral band data of the target band by using the convolutional neural network model includes: performing feature extraction on hyperspectral wave band data to obtain a weight matrix of a target wave band; screening the weight matrix of the target wave band, and reserving the hyperspectral wave band with the largest weight by using a step function to obtain hyperspectral wave band data of the target wave band.
In the practical application process, a convolutional neural network model with a multi-head attention mechanism is used for weighting a wave band matrix, a 1 value is given to a wave band with strong representativeness, a 0 value is given to a redundant wave band, then the selected wave band matrix is used for restoring a complete original matrix, when the selected wave band matrix can approximately express the original matrix, the model can be considered to be converged, and the wave band selection result is effective and strong representativeness. And screening the obtained weight matrix, and reserving hyperspectral wave bands with larger weight and stronger representing capacity by using a step function. Only selected bands are used in the process of spectral reconstruction using a long and short-term memory network to avoid redundancy and excessive time complexity.
Optionally, when the trained long-short-term memory neural network model is used for reconstructing hyperspectral band data of the selected target band, the radiance of each band is used as one-dimensional characteristics, and the filtered band set is used as a sequence to perform long-short-term memory neural network calculation.
In the actual application process, I t is set as an input gate at the time t; f t is a forgetting door at the moment t; o t is an output gate at time t; x t is the input value of the network at the current moment; w xi is a learnable parameter of the current moment of the input door; w xf is a learnable parameter of the current moment of the forgetting door; w xo is a learnable parameter of the current moment of the output door; h t-1 is the output value of LSTM at the previous time; w hi is a learnable parameter input by the input gate once; w hf is a learnable parameter input by the forget gate once; w ho is a learnable parameter input by the forget gate once; b i is the bias term of the input gate; b f is a bias term of the forget gate; b o is the bias term of the output gate; sigma is; the long-term memory network formula is as follows:
It＝σ(XtWxi+Ht-1Whi+bi)；
Ft＝σ(XtWxf+Ht-1Whf+bf)；
Ot＝σ(XtWxo+Ht-1Who+bo)；
Optionally, updating the parameters of the convolutional neural network model, and calculating the mean square error between the original matrix and the matrix reconstructed by using the convolutional neural network model; the band satisfying the original full band information is determined according to the mean square error.
Optionally, cross-scaling using target band hyperspectral data includes:
After the reconstructed target band hyperspectral data are obtained, one-dimensional radiance information is expanded and calculated to obtain a multidimensional tensor;
performing dimension reduction operation on tensors by using two linear regression networks, and fitting the reconstructed wave band information to obtain radiance;
inputting data into a trained long-short-period memory neural network model, and fitting out the radiance of a missing wave band;
And taking the fitted wave band radiance as a target value, and calculating a calibration coefficient by combining a DN value (Digital Number) of the remote sensing device to be calibrated, so as to finish cross calibration.
In the practical application process, after the calculation of the long-term and short-term memory neural network, the characteristic dimension of the data can be changed, and linear regression is needed, and optionally, the specific process of the linear regression is as follows:
after the reconstructed target band hyperspectral data are obtained, setting the dimension number of output features as 64 and setting a hidden layer as 3;
Reducing the dimension of the 64-dimensional features by using a linear regression mode, wherein the number of the feature dimensions after the dimension reduction is 8, and then reducing the feature dimensions to 1 by using a linear regression mode again;
The reduced tensor format is (n, 1), where n is the number of input bands, and the tensor is deformed into (1, 1) using a linear connection;
And finally obtaining the radiance value of the target wave band through three times of calculation and using the radiance value for cross calibration.
The following will simulate hyperspectral datasets using two sets of datasets for training a neural network:
JPL (Jet Propulsion Laboratory) hyperspectral dataset
The spectrum characteristics of the ground features are the interaction results of electromagnetic waves and the surface of the ground features, and are often expressed as reflection, heat radiation, microwave radiation and scattering characteristics of the ground features in different wave bands, the ground feature types are screened from a spectrum database, then the ground feature reflectivity is converted into ground feature emissivity, atmospheric profile data are obtained from a thermodynamic database, and finally the screened data are calculated through MODTRA simulation software to obtain corresponding radiance.
Tigr (The Thermodynamic Initial Guess Retrieval) atmospheric profile dataset
The TIGR atmospheric profile data set is an atmospheric profile database established by French dynamic weather laboratories and is mainly used as an initial estimate of atmospheric profile inversion, and comprises atmospheric temperature and humidity profiles which are carefully selected from a large number of atmospheric samples in different periods and global ranges by using a topology method, belong to sounding observation, and are distributed in all places of the world in 2311 total atmospheric profiles. 716 sunny open lines are selected as the atmospheric profile data in the method.
The method effectively solves the problem that the traditional cross calibration requires similar spectrum bands between the remote sensor to be calibrated and the target remote sensor, trains the convolutional neural network model with a multi-head attention mechanism to enable the convolutional neural network model to have good band selection capacity and screen out a band with stronger representativeness in hyperspectral data, performs spectrum reconstruction by utilizing a long-term and short-term memory network after band selection, fits out required band information, and further meets the requirement of cross calibration.
Example 2
Based on the same principle as the method shown in the embodiment 1 of the present invention, as shown in fig. 2, the embodiment of the present invention further provides a hyperspectral cross scaling device based on spectrum reconstruction, which comprises a model building unit, a selecting unit, a model training unit, a reconstruction unit and a cross scaling unit;
the model building unit is used for building a convolutional neural network model with a multi-head attention mechanism;
The selection unit is used for selecting hyperspectral wave band data of a target wave band by using the convolutional neural network model;
the model training unit is used for building a long-term memory neural network model and training the long-term memory neural network model by adopting a back propagation algorithm;
The reconstruction unit is used for reconstructing hyperspectral band data of the selected target band by using the trained long-short-term memory neural network model to obtain reconstructed hyperspectral band data of the target band;
And the cross scaling unit is used for cross scaling by using the hyperspectral data of the target wave band.
Optionally, a plurality of multi-head attention modules and a full connection layer are adopted as the basic structure of the long-short-period memory neural network model, and a step function is added as a filter during output.
Optionally, selecting hyperspectral band data of the target band by using the convolutional neural network model includes: performing feature extraction on hyperspectral wave band data to obtain a weight matrix of a target wave band; screening the weight matrix of the target wave band, and reserving the hyperspectral wave band with the largest weight by using a step function to obtain hyperspectral wave band data of the target wave band.
Optionally, when the trained long-short-term memory neural network model is used for reconstructing hyperspectral band data of the selected target band, the radiance of each band is used as one-dimensional characteristics, and the filtered band set is used as a sequence to perform long-short-term memory neural network calculation.
Optionally, updating the model parameters of the convolutional neural network by adopting a back propagation algorithm, and calculating the mean square error between the original matrix and the matrix reconstructed by using the convolutional neural network model; the band satisfying the original full band information is determined according to the mean square error.
Optionally, cross-scaling using target band hyperspectral data includes:
After the reconstructed target band hyperspectral data are obtained, one-dimensional radiance information is expanded and calculated to obtain a multidimensional tensor;
performing dimension reduction operation on tensors by using two linear regression networks, and fitting the reconstructed wave band information to obtain radiance;
inputting data into a trained long-short-period memory neural network model, and fitting out the radiance of a missing wave band;
and taking the fitted wave band radiance as a target value, and calculating a scaling coefficient by combining with the DN value of the remote sensor to be scaled to finish cross scaling.
Example 3
Based on the same principle as the method shown in the embodiment of the present invention, there is also provided in the embodiment of the present invention an electronic device, as shown in the accompanying drawings, which may include, but is not limited to: a processor and a memory; a memory for storing a computer program; a processor for performing the method of any of the embodiments of the invention by invoking a computer program.
In an alternative embodiment, an electronic device is provided, the electronic device 30 shown in fig. 3 comprising: a processor 310 and a memory 330. Wherein the processor 310 is coupled to the memory 330, such as via a bus 320.
Optionally, the electronic device 30 may further comprise a transceiver 340, and the transceiver 340 may be used for data interaction between the electronic device and other electronic devices, such as transmission of data and/or reception of data, etc. It should be noted that, in practical applications, the transceiver 340 is not limited to one, and the structure of the electronic device 30 is not limited to the embodiment of the present invention.
The processor 310 may be a CPU central processing unit, a general purpose processor, a DSP data signal processor, an ASIC specific integrated circuit, an FPGA field programmable gate array or other programmable logic device, a hardware component, or any combination thereof. Processor 310 may also be a combination that performs computing functions, e.g., including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.
Bus 320 may include a path that communicates information between the components. Bus 320 may be a PCI peripheral component interconnect standard bus or an EISA extension industry standard architecture bus, among others. The bus 320 may be classified into a control bus, a data bus, an address bus, and the like. For ease of illustration, only one thick line is shown in FIG. 3, but not only one bus or one type of bus.
Memory 330 may be, but is not limited to, ROM read-only memory or other type of static storage device that can store static information and instructions, RAM random-access memory or other type of dynamic storage device that can store information and instructions, EEPROM electrically erasable programmable read-only memory, CD-ROM read-only or other optical disk storage, optical disk storage (including optical disks, laser disks, compact disks, digital versatile disks, etc.), magnetic disk storage media, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer.
The memory 330 is used for storing application program codes (computer programs) for executing the inventive arrangements and is controlled to be executed by the processor 310. The processor 310 is configured to execute the application code stored in the memory 330 to implement what is shown in the foregoing method embodiments.
The above is only a preferred embodiment of the present invention, and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A hyperspectral cross-scaling method based on spectral reconstruction, comprising:
Establishing a convolutional neural network model with a multi-head attention mechanism;
selecting hyperspectral wave band data of a target wave band by using the convolutional neural network model;
Establishing a long-term memory neural network model, and training the long-term memory neural network model by adopting a back propagation algorithm;
reconstructing the hyperspectral band data of the selected target band by using the trained long-short-term memory neural network model to obtain reconstructed hyperspectral data of the target band;
Cross-scaling using the target band hyperspectral data, comprising: after the reconstructed target band hyperspectral data are obtained, one-dimensional radiance information is expanded and calculated to obtain a multidimensional tensor; performing dimension reduction operation on the tensor by using two linear regression networks, and fitting the reconstructed wave band information to obtain radiance; inputting data into the trained long-short-period memory neural network model, and fitting out the radiance of a missing wave band; and taking the fitted wave band radiance as a target value, and calculating a scaling coefficient by combining with the DN value of the remote sensor to be scaled to finish cross scaling.
2. The hyperspectral cross scaling method based on spectrum reconstruction according to claim 1, wherein a plurality of multi-head attention modules and full connection layers are adopted as the basic structure of the long-short-term memory neural network model, and step functions are added as filters during output.
3. The hyperspectral cross-scaling method based on spectral reconstruction as claimed in claim 1, wherein selecting hyperspectral band data of a target band using the convolutional neural network model includes: performing feature extraction on the hyperspectral wave band data to obtain a weight matrix of a target wave band; and screening the weight matrix of the target wave band, and reserving the hyperspectral wave band with the largest weight by using a step function to obtain the hyperspectral wave band data of the target wave band.
4. The hyperspectral cross calibration method based on spectrum reconstruction according to claim 1, wherein when the hyperspectral band data of the selected target band is reconstructed by using the trained long-short-term memory neural network model, the radiance of each band is used as one-dimensional characteristics, and the filtered band set is used as a sequence to perform long-short-term memory neural network calculation.
5. The hyperspectral cross scaling method based on spectrum reconstruction according to claim 1, wherein the parameters of the convolutional neural network model are updated by adopting a back propagation algorithm, and the mean square error between an original matrix and a matrix reconstructed by using the convolutional neural network model is calculated; and determining a wave band meeting the reconstruction of the original full-wave band information according to the mean square error.
6. The hyperspectral cross calibration device based on spectrum reconstruction is characterized by comprising a model building unit, a selecting unit, a model training unit, a reconstruction unit and a cross calibration unit;
The model building unit is used for building a convolutional neural network model with a multi-head attention mechanism;
the selection unit is used for selecting hyperspectral wave band data of a target wave band by utilizing the convolutional neural network model;
the model training unit is used for building a long-period memory neural network model and training the long-period memory neural network model by adopting a back propagation algorithm;
The reconstruction unit is used for reconstructing the hyperspectral band data of the selected target band by using the trained long-short-term memory neural network model to obtain reconstructed hyperspectral data of the target band;
The cross scaling unit, configured to perform cross scaling using the target band hyperspectral data, includes: after the reconstructed target band hyperspectral data are obtained, one-dimensional radiance information is expanded and calculated to obtain a multidimensional tensor; performing dimension reduction operation on the tensor by using two linear regression networks, and fitting the reconstructed wave band information to obtain radiance; inputting data into the trained long-short-period memory neural network model, and fitting out the radiance of a missing wave band; and taking the fitted wave band radiance as a target value, and calculating a scaling coefficient by combining with the DN value of the remote sensor to be scaled to finish cross scaling.
7. An electronic device, comprising:
A processor and a memory;
the memory is used for storing computer operation instructions;
The processor for performing the hyperspectral cross scaling method based on spectral reconstruction as claimed in any one of claims 1 to 5 by invoking the computer operating instructions.
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