CN113610729B - Method, system and storage medium for correcting hyperspectral remote sensing image satellite-ground cooperative atmosphere - Google Patents
Method, system and storage medium for correcting hyperspectral remote sensing image satellite-ground cooperative atmosphere Download PDFInfo
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Abstract
The invention relates to a hyperspectral remote sensing image satellite-ground cooperative atmosphere correction method, a system and a storage medium, which comprise the following steps: acquiring ground observation data, satellite observation data and hyperspectral remote sensing image data; carrying out ground observation data cooperative processing on ground observation data, and carrying out quasi-synchronization on the ground observation data and hyperspectral remote sensing images; carrying out satellite observation data cooperative processing on the hyperspectral remote sensing image data and the satellite observation data to generate satellite observation atmospheric state parameter data; the ground observation data after cooperative processing and the atmospheric state parameter data observed by the satellite are subjected to cooperative processing of the satellite ground observation data, and the atmospheric observation data with the satellite and the ground approximately synchronous observation are generated; and carrying out atmosphere correction on the hyperspectral remote sensing image data based on the atmosphere observation data to generate atmosphere correction data. The invention can improve the accuracy and reliability of the inversion of the atmospheric parameters and the accuracy of the atmospheric correction processing. The invention can be applied to the field of remote sensing image atmospheric correction.
Description
Technical Field
The invention relates to the field of remote sensing image atmosphere correction, in particular to a hyperspectral remote sensing image star-ground cooperative atmosphere correction method, a hyperspectral remote sensing image star-ground cooperative atmosphere correction system and a storage medium under the cooperation of star-ground observation data.
Background
The total radiance of the ground target measured by the remote sensor is not a reflection of the true reflected energy of the ground, and radiation energy errors caused by atmospheric absorption, scattering and other effects, particularly scattering effects, are included. The atmospheric correction is the process of eliminating radiation errors caused by atmospheric influences and inverting the actual surface reflectivity of the ground object. The atmospheric correction refers to adjusting the measured value of the remote sensing image according to the atmospheric condition so as to eliminate the atmospheric influence and perform atmospheric correction. The precondition for developing quantitative processing and application of remote sensing images, especially hyperspectral remote sensing images, is that atmospheric correction processing is necessary. Strictly speaking, when these atmospheric correction models are applied to images of a specific scene and a specific phase, synchronized sensor spectral profile information and atmospheric condition feature values are also required. Even though planned, the atmospheric condition feature values are difficult to obtain. Because the then-current atmospheric information is not known for most of the historical satellite data. Even for most satellite images used for ground coverage change detection, accurate extraction of the folded surface reflectivity is difficult to achieve.
There are many methods for atmospheric correction of remote sensing images. The atmospheric conditions may be standard model atmospheric or ground actual measurement data, or may be the result of inversion from the image itself. Indirect atmospheric correction refers to redefining some remote sensing common functions, such as NDVI, to form new functional forms to reduce dependence on the atmosphere. This method does not require knowledge of the various parameters of the atmosphere. But these classifications are not in fact clearly defined. The reflectivity calculated by the radiation transmission model method is high in accuracy, but the method is large in calculation amount and needs more parameters. Such as the moisture content of the atmosphere, the ozone content and the spatial distribution, the aerosol optical characteristics, etc. In conventional atmospheric corrections, such measurements are difficult to perform. The key to atmospheric correction is to obtain accurate and reliable parameters about the optical properties of the atmosphere, such as the optical thickness of the atmosphere, the phase function, the albedo of unidirectional scattering, the gas absorption rate, etc. The difficulty with atmospheric correction is that it is difficult to determine these parameters. If the measured parameter is incorrect, the accuracy of the calculation is directly affected. There is also a limit to the wide application of this method.
The optimal atmospheric correction method of the remote sensing image is to only pass through the remote sensing image information without auxiliary data such as field measurement and the like, and can be suitable for historical data and remote research areas. Researchers have proposed some methods of atmospheric correction that do not require atmospheric and ground measured data, especially satellite synchronized observations. Dark-object methods (dark-pels). The atmospheric correction by the dark pixel method mainly depends on the information of the image, and some information which cannot be directly obtained on the image can be found in the literature of corresponding previous research results. From the assumption condition of the dark pixel method and the simplified atmospheric correction model, it can be seen that the dark pixel method does not consider the multiple scattering irradiation effect of the atmosphere, does not consider the multiple scattering influence among pixels and does not consider the influence of terrain difference, and meanwhile, the determination of the dark pixels in the image has certain subjectivity, and all the influence on the result accuracy of the dark pixel method. The invariant target method (invariable-object methods) is to assume that there are pixels on the image that have a relatively stable reflected radiation characteristic and that can determine the geographic significance of these pixels, then these pixels are referred to as invariant targets whose reflectivity on the remote sensing image at different phases will have a linear relationship. When such linear relationships of invariant objects and their reflectivity in different simultaneous remote sensing images are determined, atmospheric corrections can be made to the remote sensing images. The histogram matching method (histogram matching methods) is to match the histogram of the affected area by using the histogram if it is determined that the reflectivity of a certain area not affected by the atmosphere is the same as that of an area affected by the atmosphere and the unaffected area can be determined. The key to this approach is to find 2 regions of equal reflectivity but opposite atmospheric influence, and it also assumes that the spatial distribution of the aerosol is uniform. Therefore, if a wide-range remote sensing image can be divided into a plurality of small blocks, better effect can be obtained when the atmospheric correction is respectively carried out by the method.
The existing hyperspectral remote sensing image atmospheric correction method has the following problems: (1) The atmospheric state parameter inversion is carried out based on the hyperspectral remote sensing image alone, and is mainly based on an empirical model, so that the accuracy is difficult to compare with synchronous observation data; (2) The atmospheric state parameter observation difficulty which has the same imaging area as the hyperspectral remote sensing imaging and is strictly synchronous in time is extremely high, and the operability of the method in the prior art is not high.
Disclosure of Invention
Aiming at the problems, the invention aims to provide a hyperspectral remote sensing image satellite-ground cooperative atmosphere correction method, a hyperspectral remote sensing image satellite-ground cooperative atmosphere correction system and a storage medium, which can improve the accuracy and reliability of atmosphere parameter inversion and improve the accuracy of atmosphere correction processing.
In order to achieve the above purpose, the present invention adopts the following technical scheme: a hyperspectral remote sensing image satellite-ground cooperative atmosphere correction method comprises the following steps: acquiring ground observation data, satellite observation data and hyperspectral remote sensing image data; carrying out ground observation data cooperative processing on ground observation data, and carrying out quasi-synchronization on the ground observation data and hyperspectral remote sensing images; carrying out satellite observation data cooperative processing on the hyperspectral remote sensing image data and the satellite observation data to generate satellite observation atmospheric state parameter data; the ground observation data after cooperative processing and the atmospheric state parameter data observed by the satellite are subjected to cooperative processing of the satellite ground observation data, and the atmospheric observation data with the satellite and the ground approximately synchronous observation are generated; and carrying out atmosphere correction on the hyperspectral remote sensing image data based on the atmosphere observation data to generate atmosphere corrected earth surface reflectivity data.
Further, the ground observation data is subjected to ground observation data cooperative processing, including time cooperation, space cooperation and quality cooperation.
Further, the time collaboration: according to the difference of the acquisition time of the ground observation data and the acquisition time of the hyperspectral remote sensing image, determining a time weight, and generating the ground observation data quasi-synchronous with the hyperspectral remote sensing image through resampling.
Further, the space cooperates with: fitting and resampling are carried out through physical or mathematical simulation through the spatial relationship between the position of the ground observation site and the position of the hyperspectral imaging region, so as to generate ground observation data with the same imaging range as the hyperspectral remote sensing image;
The quality is cooperated: the quality of the observed data is processed, the observed data is divided according to a plurality of grades, and the quality weight of each grade is set.
Further, the performing satellite observation data cooperative processing on the hyperspectral remote sensing image data and the satellite observation data includes:
Calculating and generating atmospheric state data of the water vapor content, the atmospheric transmittance and the aerosol optical thickness for the hyperspectral remote sensing image observed by the satellite; calculating and generating atmospheric state data of atmospheric transmittance and aerosol optical thickness for polarization data acquired by a polarized remote sensor observed by a satellite; and performing cross comparison on the two types of atmospheric state data, and calculating to obtain the atmospheric state parameter data observed by the satellite.
Further, the cooperative processing of the ground observation data after cooperative processing and the atmospheric state parameter data observed by the satellite into the planetary ground observation data includes:
On the basis of the cooperation of ground observation data and satellite observation data, hyperspectral remote sensing imaging is enabled to have the atmospheric state data of satellite and ground approximately synchronous observation at the same time, and optimized atmospheric observation data is formed through a weighted average model.
Further, the performing atmospheric correction on the hyperspectral remote sensing image data based on the atmospheric observation data includes:
According to the optimized atmospheric observation data, calculating and generating atmospheric parameters;
According to the atmospheric parameters, an atmospheric correction radiation transmission model or an empirical model is adopted, an atmospheric correction lookup table is established, and atmospheric correction is carried out on the hyperspectral remote sensing image, so that atmospheric correction data are generated.
A hyperspectral remote sensing image satellite-ground cooperative atmosphere correction system comprises: the system comprises a data acquisition module, a first data cooperative processing module, a second data cooperative processing module, a third data cooperative processing module and a correction module; the data acquisition module is used for acquiring ground observation data, satellite observation data and hyperspectral remote sensing image data; the first data cooperative processing module performs ground observation data cooperative processing on ground observation data and quasi-synchronizes the ground observation data with the hyperspectral remote sensing image; the second data cooperative processing module is used for carrying out satellite observation data cooperative processing on the hyperspectral remote sensing image data and the satellite observation data to generate satellite observation atmospheric state parameter data; the third data cooperative processing module is used for carrying out cooperative processing on the ground observation data after cooperative processing and the satellite-observed atmospheric state parameter data to generate the atmospheric observation data with the satellite and the ground approximately synchronous observation; and the correction module is used for carrying out atmosphere correction on the hyperspectral remote sensing image data based on the atmosphere observation data to generate atmosphere correction data.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods described above.
A computing apparatus, comprising: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods described above.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. According to the invention, satellite-ground observation data cooperation is added in the flow of the existing atmosphere correction method, the characteristics of modes such as inversion of the atmospheric parameters, ground-to-air observation, air-to-ground atmospheric observation and the like based on hyperspectral remote sensing images are comprehensively utilized, the comprehensive and effective utilization capacity of various observation modes is improved, the inversion precision and reliability of the atmospheric parameters are improved, and the atmospheric correction processing precision is improved.
2. The method solves the problem that ground observation data is difficult to apply to the actual hyperspectral remote sensing image atmospheric correction processing, and realizes the synchronous acquisition of ground discrete observation data and hyperspectral remote sensing image imaging through time coordination, space coordination and quality coordination.
Drawings
FIG. 1 is a flow chart of a calibration method according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which are obtained by a person skilled in the art based on the described embodiments of the invention, fall within the scope of protection of the invention.
In an embodiment of the present invention, as shown in fig. 1, a method for performing star-earth collaborative atmospheric correction on hyperspectral remote sensing images is provided, which includes the following steps:
step 1, acquiring ground observation data, satellite observation data and hyperspectral remote sensing image data;
The ground observation data refer to the atmospheric observation data of the ground end, and are short for ground observation data; the satellite observation data refers to atmospheric observation data at a satellite end, and is short for satellite observation data.
Step 2, carrying out ground observation data cooperative processing on the ground observation data, and enabling the ground observation data to be quasi-synchronous with the hyperspectral remote sensing image;
step 3, carrying out satellite observation data cooperative processing on the hyperspectral remote sensing image data and the satellite observation data to generate satellite observation atmospheric state parameter data;
Step 4, the ground observation data after cooperative processing and the atmospheric state parameter data observed by the satellite are subjected to cooperative processing of the satellite ground observation data, and the atmospheric observation data with the satellite and the ground approximately synchronous observation are generated;
and 5, performing atmospheric correction on the hyperspectral remote sensing image data based on the atmospheric observation data to generate atmospheric corrected earth surface reflectivity data.
In a preferred embodiment, the ground observation data and the satellite observation data in step1 are respectively:
(1) The ground observation data includes:
The method comprises the steps of adopting sensors such as a ground sky polarization observer, a solar photometer and the like to acquire data such as atmospheric transmittance, inversion aerosol optical thickness, an atmospheric particle spectrum function, an phase function and the like for sky observation;
Air humidity, atmospheric visibility, atmospheric cloud amount and other data acquired by a ground weather observation station:
global atmospheric aerosol observation data acquired by a global aerosol observation station network.
(2) The satellite observations include: the method adopts sensors such as an atmospheric polarization observer and a solar photometer carried by satellites, and data such as atmospheric transmittance, inversion aerosol optical thickness, an atmospheric particle spectrum function, a phase function and the like obtained by earth observation.
In a preferred embodiment, in step 2, ground observation data is cooperatively processed with ground observation data, and because the ground observation data is obtained from discrete ground observation sites at discrete observation moments, the ground observation data and the hyperspectral remote sensing image are required to be quasi-synchronized by the ground observation data cooperatively processing, so as to realize the applicability of the ground observation data in the atmospheric correction processing of the hyperspectral remote sensing image.
In the above embodiment, the ground observation data cooperative processing includes time cooperation, space cooperation, and quality cooperation.
The time coordination is to determine a time weight w t by a time parameter interpolation method according to the difference between the time of acquiring the ground observation data and the time of acquiring the hyperspectral remote sensing image, and generate the ground observation data quasi-synchronous with the hyperspectral remote sensing image by resampling.
The time weight w t is:
Where t is the time of satellite observation, and t 1 and t 2 are the time of terrestrial observation (t 1 and t 2 need to be explained separately).
The spatial coordination is that the ground observation data with the same imaging range as the hyperspectral remote sensing image is generated by fitting and resampling through the spatial relation between the ground observation site position and the hyperspectral imaging region position and through physical or mathematical simulation.
Taking a distance weighted model as an example, a distance weighted resampling algorithm is adopted to calculate the atmospheric state parameters of the remote sensing imaging point positions. Because the ground observation sites are irregular discrete data points, the distances between the imaging points and the observation sites are gradually found, and different weight coefficients are respectively given according to different distances of the distances, namely the correlation degree of the observed values of the observation sites to the actual values of the imaging points. The distance is reduced along with the increase of the distance, and the weight with small distance is added to the reaction weight, and the weight with large distance is reduced. To further emphasize the effect of distance, a power function of the inverse of distance is used as a weight function as follows:
Where w d is a distance weight, (x k,yk,zk) is a position coordinate of an observation site, (x i,yi,zi) is a position coordinate of an imaging point, d k is a distance value, α is a proportional adjustment coefficient for weight calculation, and e is a bias adjustment coefficient for weight calculation.
Quality synergy is the processing of the quality of the observed data, which is classified according to several classes, e.g., 1-2-3 classes, according to the observation specifications and standards. According to the statistical experience of the data acquired in advance, the quality weight w q of each grade is set, for example, the data w q such as 1 is 1, the data w q such as 2 is 0.8,3, and the data w q such as 0.8,3 is 0.6.
From the original observed data quality Q, according to the time weight w t, the distance weight w d and the quality weight w q, the observed data quality Q X after quality synergy is obtained:
QX＝wt×wd×wq×Q
in a preferred embodiment, in step 3, the hyperspectral remote sensing image data and the satellite observation data are cooperatively processed according to satellite observation data, which specifically includes:
For a hyperspectral remote sensing image observed by a satellite, generating atmospheric state data such as water vapor content, atmospheric transmittance, aerosol optical thickness and the like by adopting an atmospheric parameter inversion method based on the image; and generating atmospheric state data such as atmospheric transmittance, aerosol optical thickness and the like by adopting a polarized aerosol correlation method for polarized data acquired by a polarized remote sensor observed by a satellite. And carrying out cross comparison on the atmospheric state data inverted by the two methods, and calculating to obtain atmospheric state parameter data of satellite observation, wherein the atmospheric state parameter data is used for the coordination of satellite-ground observation data. The calculation may be implemented by using an existing method, such as a weighted average algorithm, which is not described herein.
In a preferred embodiment, in step 4, the ground observation data after the cooperative processing and the satellite observed atmospheric state parameter data are subjected to the cooperative processing of the satellite earth observation data, specifically:
on the basis of the cooperation of ground observation data and satellite observation data, hyperspectral remote sensing imaging is enabled to have the atmospheric state data of satellite and ground approximately synchronous observation at the same time, and optimized atmospheric observation data is formed through a weighted average model.
Based on the optimized atmospheric observation data, the existing correlation model of the atmospheric parameters and the observation data is adopted to calculate and generate the atmospheric parameters for the atmospheric correction of the hyperspectral remote sensing image.
In a preferred embodiment, the performing the atmospheric correction on the hyperspectral remote sensing image data based on the atmospheric observation data in step 5 specifically includes:
based on the atmospheric parameters, an existing atmospheric correction radiation transmission model or an empirical model is adopted, an atmospheric correction lookup table is established, and atmospheric correction is carried out on the hyperspectral remote sensing image to generate atmospheric correction data.
In an embodiment of the invention, a hyperspectral remote sensing image satellite-ground cooperative atmospheric correction system is provided, which comprises a data acquisition module, a first data cooperative processing module, a second data cooperative processing module, a third data cooperative processing module and a correction module;
The data acquisition module is used for acquiring ground observation data, satellite observation data and hyperspectral remote sensing image data;
The first data cooperative processing module is used for carrying out ground observation data cooperative processing on the ground observation data and quasi-synchronizing the ground observation data with the hyperspectral remote sensing image;
The second data cooperative processing module is used for carrying out satellite observation data cooperative processing on the hyperspectral remote sensing image data and the satellite observation data to generate satellite observation atmospheric state parameter data;
The third data cooperative processing module is used for carrying out cooperative processing on the ground observation data after cooperative processing and the satellite observed atmospheric state parameter data to generate the atmospheric observation data with the satellite and the ground approximately synchronous observation;
and the correction module is used for carrying out atmosphere correction on the hyperspectral remote sensing image data based on the atmosphere observation data to generate atmosphere correction data.
In an embodiment of the invention, there is provided a computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of the embodiments described above.
In one embodiment of the invention, there is provided a computing device comprising: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of the embodiments described above.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Claims (5)
1. A hyperspectral remote sensing image satellite-ground cooperative atmosphere correction method is characterized by comprising the following steps of:
Acquiring ground observation data, satellite observation data and hyperspectral remote sensing image data;
Carrying out ground observation data cooperative processing on ground observation data, and carrying out quasi-synchronization on the ground observation data and hyperspectral remote sensing images;
carrying out satellite observation data cooperative processing on the hyperspectral remote sensing image data and the satellite observation data to generate satellite observation atmospheric state parameter data;
the ground observation data after cooperative processing and the atmospheric state parameter data observed by the satellite are subjected to cooperative processing of the satellite ground observation data, and the atmospheric observation data with the satellite and the ground approximately synchronous observation are generated;
Performing atmospheric correction on the hyperspectral remote sensing image data based on the atmospheric observation data to generate atmospheric corrected earth surface reflectivity data;
the ground observation data is subjected to ground observation data cooperative processing, including time cooperation, space cooperation and quality cooperation;
The time cooperation: determining a time weight according to the difference of the acquisition time of the ground observation data and the acquisition time of the hyperspectral remote sensing image, and generating the ground observation data quasi-synchronous with the hyperspectral remote sensing image through resampling;
The spatial synergy: fitting and resampling are carried out through physical or mathematical simulation through the spatial relationship between the position of the ground observation site and the position of the hyperspectral imaging region, so as to generate ground observation data with the same imaging range as the hyperspectral remote sensing image; the quality is cooperated: processing the quality of the observed data, classifying the observed data according to a plurality of grades, and setting the quality weight of each grade;
the performing satellite observation data cooperative processing on hyperspectral remote sensing image data and satellite observation data comprises the following steps:
Calculating and generating atmospheric state data of the water vapor content, the atmospheric transmittance and the aerosol optical thickness for the hyperspectral remote sensing image observed by the satellite; calculating and generating atmospheric state data of atmospheric transmittance and aerosol optical thickness for polarization data acquired by a polarized remote sensor observed by a satellite; cross comparison is carried out on the two kinds of atmospheric state data, and atmospheric state parameter data observed by satellites are obtained through calculation;
The cooperative processing of the ground observation data after cooperative processing and the atmospheric state parameter data observed by the satellite into the planetary ground observation data includes:
On the basis of the cooperation of ground observation data and satellite observation data, hyperspectral remote sensing imaging is enabled to have the atmospheric state data of satellite and ground approximately synchronous observation at the same time, and optimized atmospheric observation data is formed through a weighted average model.
2. The method of claim 1, wherein the performing atmospheric correction on the hyperspectral remote sensing image data based on the atmospheric observation data comprises:
According to the optimized atmospheric observation data, calculating and generating atmospheric parameters;
According to the atmospheric parameters, an atmospheric correction radiation transmission model or an empirical model is adopted, an atmospheric correction lookup table is established, and atmospheric correction is carried out on the hyperspectral remote sensing image, so that atmospheric correction data are generated.
3. A hyperspectral remote sensing image star-earth collaborative atmosphere correction system for implementing the correction method of claim 1 or 2, comprising: the system comprises a data acquisition module, a first data cooperative processing module, a second data cooperative processing module, a third data cooperative processing module and a correction module;
the data acquisition module is used for acquiring ground observation data, satellite observation data and hyperspectral remote sensing image data;
The first data cooperative processing module performs ground observation data cooperative processing on ground observation data and quasi-synchronizes the ground observation data with the hyperspectral remote sensing image;
The second data cooperative processing module is used for carrying out satellite observation data cooperative processing on the hyperspectral remote sensing image data and the satellite observation data to generate satellite observation atmospheric state parameter data;
the third data cooperative processing module is used for carrying out cooperative processing on the ground observation data after cooperative processing and the satellite-observed atmospheric state parameter data to generate the atmospheric observation data with the satellite and the ground approximately synchronous observation;
and the correction module is used for carrying out atmosphere correction on the hyperspectral remote sensing image data based on the atmosphere observation data to generate atmosphere correction data.
4. A computer readable storage medium storing one or more programs, wherein the one or more programs comprise instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-2.
5. A computing device, comprising: one or more processors, memory, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing any of the methods of claims 1-2.
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