CN112001263A - Method and system for selecting reference probe element of linear array scanning remote sensor - Google Patents
Method and system for selecting reference probe element of linear array scanning remote sensor Download PDFInfo
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Abstract
The embodiment of the invention provides a method and a system for selecting a reference probe element of a linear array scanning remote sensor, wherein the method comprises the following steps: acquiring an original remote sensing image, and correcting the original remote sensing image through relative calibration to obtain a corrected image; calculating to obtain an image average brightness change index, an image information entropy index and an image radiation resolution index based on the original remote sensing image and the corrected image; and calculating the average brightness change index of the image, the entropy index of the image information and the radiation resolution index of the image to obtain a comprehensive probe element score, and selecting the probe element with the highest comprehensive probe element score as a reference probe element for relative calibration. The embodiment of the invention provides a scoring method based on the distribution characteristics of the output code values of the probe elements aiming at the problem of how to select the reference probe elements in the relative calibration process of the linear array scanning remote sensor, selects the probe elements with the highest score as the reference probe elements, and selects the most appropriate reference probe elements from the aspect of the statistical characteristics of the earth observation data.
Description
Technical Field
The invention relates to the technical field of remote sensing, in particular to a method and a system for selecting a reference probe element of a linear array scanning remote sensor.
Background
In line scan imaging remote sensing data, a stripe phenomenon is very common, for example, a Medium Resolution Spectral Imager (MERSI) in Fengyun No. three (FY-3) is a 10/40 probe line array instrument, and a stripe phenomenon exists in an original image uploaded on a satellite and downloaded from the satellite. Such streaking is generally accomplished by aligning the probes relative to each other and removing the streaks.
Currently, there are mainly three types of relative calibration methods: one is to adopt on-satellite normalization, that is, to adopt the electronic technology, to linearly transform and integrate the originally sampled code value (Digital Number, DN) according to the relative difference of the radiation response between the probes, and then to download. This approach can greatly suppress streaking, but is often not complete. Another class is computer image algorithm based cancellation such as wavelet transforms, median filtering, etc. The algorithms can obtain good fringe strip eliminating effect aiming at a single image. The method has the disadvantages that the algorithm depends on the image to extract correction parameters, but the parameters are not stable enough and can not meet the requirement of automatic processing; meanwhile, due to the fact that the image is subjected to nonlinear processing, reflectivity information contained in the processed image is irreversibly changed, and errors which are difficult to quantitatively evaluate are introduced for subsequent quantitative application. In time complexity, different algorithms consume different time and labor, but often affect the processing timeliness of the business system. And in the other category, the method for matching the empirical distribution function is more discussed, and the method is stable and reliable, has low time complexity and hardly influences the quantitative application.
In the relative calibration method, the reference probe element is a key technical parameter, different probe elements in a channel are selected as the reference probe element, the quality of the remote sensing image after relative calibration is different, the existing method for selecting the reference probe element generally adopts the probe element with the most sensitive radiation response as the reference probe element, but the method adopting a single measurement standard cannot completely reflect the influence condition of the image after the reference probe element is selected and corrected.
Disclosure of Invention
The embodiment of the invention provides a method and a system for selecting a reference probe element of a linear array scanning remote sensor, which are used for overcoming the defects in the prior art and realizing the selection of the most appropriate reference probe element.
In a first aspect, an embodiment of the present invention provides a method for selecting a linear array scanning remote sensor reference probe, including:
acquiring an original remote sensing image, and correcting the original remote sensing image through relative calibration to obtain a corrected image;
calculating to obtain an image average brightness change index based on the original remote sensing image and the corrected image;
calculating to obtain an image information entropy index based on the original remote sensing image and the corrected image;
calculating to obtain an image radiation resolution index based on the original remote sensing image and the corrected image;
and calculating the image average brightness change index, the image information entropy index and the image radiation resolution index to obtain a comprehensive probing element score, and selecting the probing element with the highest comprehensive probing element score as a relative calibration reference probing element.
Further, the calculating to obtain an image average brightness change index based on the original remote sensing image and the corrected image specifically includes:
selecting any probe element as a temporary reference probe element, and constructing a first relative calibration lookup table;
correcting the original remote sensing image based on the first relative calibration lookup table to obtain a first corrected original image;
calculating the absolute difference value of the average code values of the first correction original image and the original remote sensing image;
sequentially selecting the rest probe elements in the channel as temporary reference probe elements, repeating the steps, and constructing an average code value absolute difference data set;
and calculating the image average brightness change index based on the average code value absolute difference data set.
Further, the calculating based on the original remote sensing image and the corrected image to obtain an image information entropy index specifically includes:
selecting any probe element as a temporary reference probe element, and constructing a second relative calibration lookup table;
correcting the original remote sensing image based on the second relative calibration lookup table to obtain a second corrected original image;
calculating a correction image information entropy of the second correction original image;
sequentially selecting the rest probe elements in the channel as temporary reference probe elements, repeating the steps, and constructing an information entropy data set;
and calculating the image information entropy index based on the information entropy data set.
Further, the calculating to obtain an image radiation resolution index based on the original remote sensing image and the corrected image specifically includes:
selecting any probe element as a temporary reference probe element, and constructing a third relative calibration lookup table;
correcting the original remote sensing image based on the third relative calibration lookup table to obtain a third corrected original image;
calculating the average effective code value amount of the third correction original image;
sequentially selecting the rest probe elements in the channel as temporary reference probe elements, repeating the steps, and constructing an average effective code value data set;
and calculating the image radiation resolution index based on the average effective code value data set.
Further, the calculating the average brightness change index of the image, the entropy index of the image information and the radiation resolution index of the image to obtain the comprehensive probe score specifically includes:
and calculating by adopting a weighting algorithm to obtain the comprehensive score of the probe element.
Further, the weighting algorithm includes an average weighting or a non-average weighting.
Further, the relative scaling is realized by a cumulative probability method.
In a second aspect, an embodiment of the present invention further provides a system for selecting a reference probe of a linear array scanning remote sensor, including:
the acquisition module is used for acquiring an original remote sensing image, and correcting the original remote sensing image through relative calibration to obtain a corrected image;
the first processing module is used for calculating to obtain an image average brightness change index based on the original remote sensing image and the corrected image;
the second processing module is used for calculating to obtain an image information entropy index based on the original remote sensing image and the corrected image;
the third processing module is used for calculating to obtain an image radiation resolution index based on the original remote sensing image and the corrected image;
and the comprehensive module is used for calculating the image average brightness change index, the image information entropy index and the image radiation resolution index to obtain a comprehensive probe element score, and selecting the probe element with the highest comprehensive probe element score as a relative calibration reference probe element.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of any one of the above methods for selecting the reference probe of the linear array scanning remote sensor when executing the program.
In a fourth aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method for selecting the reference probe of the linear scanning remote sensor according to any one of the above-mentioned methods.
According to the method and the system for selecting the reference probe elements of the linear array scanning remote sensor, provided by the embodiment of the invention, a scoring method based on the distribution characteristics of the output code values of the probe elements is given by aiming at the problem of how to select the reference probe elements in the relative calibration process of the linear array scanning remote sensor, the probe elements with the highest score are selected as the reference probe elements, and the most appropriate reference probe elements are selected from the aspect of the statistical characteristics of earth observation data.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for selecting a reference probe of a line-array scanning remote sensor according to an embodiment of the present invention;
fig. 2 is a schematic diagram of frequency distribution and code value cumulative probability of each code value of the probe in the channel 3 according to the embodiment of the present invention;
FIG. 3 is a diagram illustrating a relative scaling search for a channel 3 according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of distribution of absolute difference values and average luminance indexes of an original image according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of distribution of image information entropy along with probe numbers and image information entropy index distribution provided by an embodiment of the present invention;
FIG. 6 is a schematic diagram of distribution of image mean effective code value amounts with probe numbers and image radiation resolution index distribution provided by an embodiment of the present invention;
fig. 7 is a schematic diagram illustrating distribution of probe element comprehensive scores with probe element numbers according to an embodiment of the present invention;
fig. 8 is a schematic structural diagram of a system for selecting a reference probe of a line-scanning remote sensor according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first", "second" and "third" in the embodiments of the present application are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first," "second," and "third" may explicitly or implicitly include at least one of the feature. In the description of the present application, the terms "comprise" and "have", as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a system, product or apparatus that comprises a list of elements or components is not limited to only those elements or components but may alternatively include other elements or components not expressly listed or inherent to such product or apparatus. In the description of the present application, "plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise.
Aiming at the problems in the prior art, the embodiment of the invention adopts a comprehensive scoring method to synthesize the brightness change of the image, the entropy of the image information and the radiation resolution of the image into a single index, and the single index is used for evaluating the potential capability of each probe element as a reference probe element.
Fig. 1 is a schematic flow chart of a method for selecting a reference probe of a line-array scanning remote sensor according to an embodiment of the present invention, as shown in fig. 1, including:
s1, acquiring an original remote sensing image, and correcting the original remote sensing image through relative calibration to obtain a corrected image;
specifically, taking Fengyun No. three as an example, FY-3/MERSI adopts a multielement linear array scanning mode to obtain a ground observation image, wherein channels 1-4 are channels with a resolution of 250 meters, and 40 detecting elements are scanned in parallel; the channels 6-20 are 1000 m resolution channels, 10 probes are swept in parallel. Because the linear array detector has natural radiation response difference, the original image has a stripe phenomenon, so that the relative calibration of the image needs to be completed, the image can be further quantitatively applied after the stripe is removed, a relative calibration lookup table relative to a reference probe element is constructed, the output code values of other probe elements are corrected, and the relative calibration is completed.
S2, calculating to obtain an image average brightness change index based on the original remote sensing image and the corrected image;
the average brightness of the original remote sensing image can be represented by the average value of all the observation output code values, and the change of the brightness of the image before and after relative calibration is considered to be small, so that an average brightness change index is constructed, and the difference before and after correction is evaluated. And taking one probe element in the channel as a temporary reference probe element, and constructing a relative calibration lookup table by utilizing an accumulative probability method. And correcting the original remote sensing data of other probe elements by using a relative calibration lookup table. And calculating the average code value of the corrected image and the average code value of the original image, and then calculating to obtain the absolute difference value of the two values. And sequentially taking each probe element in the channel as a temporary reference probe element, and repeating the steps to obtain an average code value absolute difference data set corresponding to each probe element. And giving linear scores of the absolute difference values of the mean code values of all the probe elements by taking the minimum value of the absolute difference values of the mean code values as 100 points and the maximum value as 0 point as an index of the change of the mean brightness of the image. And the image average brightness change index is used for describing the difference between the corrected image average brightness and the original image average brightness when the probe element is used as a temporary reference probe element.
S3, calculating to obtain an image information entropy index based on the original remote sensing image and the corrected image;
the image information entropy is used for describing the information quantity contained in the image and can be obtained by calculating the probability corresponding to each output code value. Based on the images after different probe elements are relatively scaled, the image information entropy of the different probe elements is different. Considering that the relatively scaled image should provide as much information as possible, an information entropy index is constructed and the difference in information entropy is evaluated. And taking one probe element in the channel as a temporary reference probe element, and constructing a relative calibration lookup table by utilizing an accumulative probability method. And correcting the original remote sensing data of other probe elements by using a relative calibration lookup table. And calculating the entropy of the image information after correction. And sequentially taking each probe element in the channel as a temporary reference probe element, and repeating the steps to obtain an image information entropy data set corresponding to each probe element. And giving linear scores of the image information entropies of all the probe elements by taking the maximum value of the image information entropies as 100 points and the minimum value of the image information entropies as 0 point as an image information entropy index. And the image information entropy index describes the amount of image information after the original image is corrected when the probe element is used as a temporary reference probe element, and the higher the image information entropy index score is, the larger the amount of implied information is.
S4, calculating to obtain an image radiation resolution index based on the original remote sensing image and the corrected image;
radiation resolution is the ability to distinguish small changes in energy in an image. The remote sensor outputs the gray scale number for a certain incident energy range, and the more the gray scale number is used, the more the image is described more finely for the energy change. And taking one probe element in the channel as a temporary reference probe element, and constructing a relative calibration lookup table by utilizing an accumulative probability method. And correcting the original remote sensing data of other probe elements by using a relative calibration lookup table. And after the image correction, a minimum code value and a maximum code value corresponding to medium energy are taken. And counting the frequency of each code value between the minimum code value and the maximum code value of each probe element of the corrected image, and counting the number of the code values with the frequency not being zero, wherein the number is recorded as the effective code value amount. And calculating the average value of all the probe effective code value quantities as the average effective code value quantity of the temporary reference probe. And sequentially taking each probe element in the channel as a temporary reference probe element, and repeating the steps to obtain an average effective code value data set corresponding to each probe element. And giving a linear score of the average effective code value quantity of each probe element by taking the maximum value of the average effective code value quantity as 100 points and the minimum value as 0 point as an image radiation resolution index. And the radiation resolution index describes the radiation resolution capability of the image after the original remote sensing image is corrected when the probe element is used as a temporary reference probe element, and the radiation resolution is better when the index is larger.
S5, calculating the image average brightness change index, the image information entropy index and the image radiation resolution index to obtain a comprehensive probe element score, and selecting the probe element with the highest comprehensive probe element score as a relative calibration reference probe element.
The fine and stable relative calibration is an important composition function of the remote sensing data ground preprocessing system and has very important significance for subsequent data application. The selection of the reference probe element is an important link of relative calibration, and the comprehensive influence of the adopted reference probe element on the aspects of corrected image brightness, image information quantity, image radiation resolution capability and the like should be considered. And the comprehensive probe element score is based on the image average brightness change index, the image information entropy index and the radiation resolution index, and the numerical value is calculated by adopting a weighted average method. The weights of the three indexes can be the same, and the weight of each index can be adjusted according to the requirements of subsequent applications. And taking the probe element with the highest probe element comprehensive score as a reference probe element for relative calibration.
According to the embodiment of the invention, a scoring method based on the distribution characteristics of the output code values of the probe elements is provided by aiming at the problem of how to select the reference probe elements in the relative calibration process of the linear array scanning remote sensor, the probe elements with the highest score are selected as the reference probe elements, and the most appropriate reference probe elements are selected from the aspect of the statistical characteristics of the earth observation data.
Based on the above embodiment, step S2 in the method specifically includes:
selecting any probe element as a temporary reference probe element, and constructing a first relative calibration lookup table;
correcting the original remote sensing image based on the first relative calibration lookup table to obtain a first corrected original image;
calculating the absolute difference value of the average code values of the first correction original image and the original remote sensing image;
sequentially selecting the rest probe elements in the channel as temporary reference probe elements, repeating the steps, and constructing an average code value absolute difference data set;
and calculating the image average brightness change index based on the average code value absolute difference data set.
Specifically, in the first step, a probe is used as a temporary reference probe to construct a relative scaling lookup table. Selecting a probe i of a channel as a temporary reference probe, firstly counting the output original code values DN of the probe in the earth observation image*Frequency of (1)i(DN*) Then, calculating a code value accumulative probability distribution function of the earth observation image, which is as follows:
in the formula, PiFor the code value cumulative probability distribution function, maxDN is the maximum design output code value of the channel, the quantization level of the MERSI channel is 12bit, and then maxDN is 4095.
The undetermined probe element j is treated in the same way to obtain a code value accumulative probability distribution function PjAfter relative calibration, the code value distribution output by probe j should be the same as probe i, and then the corresponding relationship between the output code value after relative calibration of probe j and the original code value can be obtained by using the following formula:
in the formula (I), the compound is shown in the specification,
is PiThe inverse function of (c).
Because of the discrete-type values, a static code value lookup table can be constructed for the relative scaling of probe j to temporary reference probe i:
in the formula, LUTjLook-Up Table for Probe j (Look-Up-Table, LUT)。
And secondly, correcting the original image by using a relative calibration lookup table. The code value of each pixel in the original image is converted into the corrected code value by using the lookup table according to the number of the probe element and the size of the code value, and the relative calibration of the remote sensing image is completed, wherein the relative calibration is as follows:
where N is the number of all probes in a channel, and N is 40 for a 250 meter resolution channel (channels 1-4, 24, 25) of MERSI; the 1000 meter resolution channel (channels 5-19) N is 10.
And thirdly, calculating the absolute difference of the average code values of the images before and after correction. Calculating the average code value of the relatively scaled image
The calculation formula is as follows:
in the formula, numerator is sum of code values of all pixels, and denominator NumberAllPixalsIs the number of total pixels.
Calculating the mean code value of the original image
The calculation formula is as follows:
furthermore, the absolute difference value delta DN between the relatively scaled image average code value and the original image average code valuemeanIt can be calculated by the following channel equation.
And fourthly, sequentially taking each probe element in the channel as a temporary reference probe element, repeating the first step to the third step, and constructing a mean code value absolute difference data set delta DNSetmeanThe following formula:
ΔDNSetmean＝{ΔDNmean(i),i＝1,2,…,N}
and fifthly, calculating the overall brightness change index. Calculating the linear grade of the average brightness variation before and after the image relative calibration when each probe element is taken as a temporary reference probe element by taking the minimum value and the maximum value of the absolute difference value of the average code values as 100 points and 0 point as the index of the image average brightness variationbrightThe calculation formula is shown as follows:
when the absolute difference of the average code values is minimum, IbrightThe value of (2) is 100, and the average brightness change of the image before and after relative calibration is minimum; when the absolute difference of the mean code values is maximum, IbrightIs 0, otherwise, IbrightThe values of (c) are distributed between 0 and 100.
Based on any of the above embodiments, step S3 in the method specifically includes:
selecting any probe element as a temporary reference probe element, and constructing a second relative calibration lookup table;
correcting the original remote sensing image based on the second relative calibration lookup table to obtain a second corrected original image;
calculating a correction image information entropy of the second correction original image;
sequentially selecting the rest probe elements in the channel as temporary reference probe elements, repeating the steps, and constructing an information entropy data set;
and calculating the image information entropy index based on the information entropy data set.
Specifically, in the first step, one probe in the channel is used as a temporary reference probe, and a relative calibration lookup table is constructed by utilizing an accumulative probability method. One of the selected channelsTaking each probe i as a temporary reference probe, firstly counting the DN of each original code value output by the probe in the earth observation image*Frequency of (1)i(DN*) Then, calculating a code value accumulative probability distribution function of the earth observation image, which is as follows:
in the formula, PiFor the code value cumulative probability distribution function, maxDN is the maximum design output code value for the channel, and if the channel quantization level is 12 bits, maxDN is 4095.
The undetermined probe element j is treated in the same way to obtain a code value accumulative probability distribution function PjAfter relative calibration, the code value distribution output by probe j should be the same as probe i, and then the corresponding relationship between the output code value after relative calibration of probe j and the original code value can be obtained by using the following formula:
in the formula (I), the compound is shown in the specification,
is PiThe inverse function of (c).
Because of the discrete-type values, a static code value lookup table can be constructed for the relative scaling of probe j to temporary reference probe i:
in the formula, LUTjLook-Up Table (LUT) for probe j.
And secondly, correcting the original remote sensing data of other probe elements by using a relative calibration lookup table. The code value of each pixel in the original image is converted into the corrected code value by using the lookup table according to the number of the probe element and the size of the code value, and the relative calibration of the remote sensing image is completed, wherein the relative calibration is as follows:
wherein N is the number of all probes in the channel.
And thirdly, calculating the entropy of the corrected image information. After calculating the relative calibration, the frequency hist (DN) corresponding to each code value of the image, and further calculating the probability p (DN) corresponding to each code value, as follows:
in the formula, NumberAllPixalsIs the number of total pixels.
The relative scaled image information entropy H can be calculated by the following formula:
and fourthly, sequentially taking each probe element in the channel as a temporary reference probe element, and repeating the first step to the third step to obtain an image information entropy data set HSet corresponding to each probe element.
HSeteffective-DN＝{H(i),i＝1,2,…,N}
And fifthly, calculating the entropy index of the image information. Calculating the linear score of the image information entropy as an image information entropy index and marking as I when each probe element is taken as a temporary reference probe element by taking the maximum value and the minimum value of the image information entropy as 100 points and 0 pointentropyThe calculation formula is shown as follows:
when the entropy of the image information is at a maximum value, IentropyThe value of (2) is 100, and the image information amount after relative calibration is maximum when the probe i is taken as a temporary reference probe; when the entropy of the image information is minimum, IentropyIs 0, otherwise, IentropyThe values of (c) are distributed between 0 and 100.
Based on any of the above embodiments, step S4 in the method specifically includes:
selecting any probe element as a temporary reference probe element, and constructing a third relative calibration lookup table;
correcting the original remote sensing image based on the third relative calibration lookup table to obtain a third corrected original image;
calculating the average effective code value amount of the third correction original image;
sequentially selecting the rest probe elements in the channel as temporary reference probe elements, repeating the steps, and constructing an average effective code value data set;
and calculating the image radiation resolution index based on the average effective code value data set.
Specifically, in the first step, one probe in the channel is used as a temporary reference probe, and a relative calibration lookup table is constructed by utilizing an accumulative probability method. Selecting a probe i of a channel as a temporary reference probe, firstly counting the input code values DN of the probe in the earth observation image*Frequency of (1)i(DN*) Then, a code value cumulative probability distribution function of the earth observation image is calculated as follows
In the formula, PiFor the code value cumulative probability distribution function, maxDN is the maximum design output code value for the channel, and if the channel quantization level is 12 bits, maxDN is 4095.
The undetermined probe element j is treated in the same way to obtain a code value accumulative probability distribution function PjAfter relative calibration, the code value distribution output by probe j should be the same as probe i, and then the corresponding relationship between the output code value after relative calibration of probe j and the original code value can be obtained by using the following formula:
in the formula (I), the compound is shown in the specification,
is PiThe inverse function of (c).
Because of the discrete-type values, a static code value lookup table can be constructed for the relative scaling of probe j to temporary reference probe i:
in the formula, LUTjLook-Up Table (LUT) for probe j.
And secondly, correcting the original remote sensing data of other probe elements by using a relative calibration lookup table. The code value of each pixel in the original image is converted into the corrected code value by using the lookup table according to the number of the probe element and the size of the code value, and the relative calibration of the remote sensing image is completed, wherein the relative calibration is as follows:
wherein N is the number of all probes in the channel.
And thirdly, calculating the average effective code value quantity. And the minimum code value and the maximum code value corresponding to the medium incident energy after the image correction. After the statistical image is corrected, the frequency of each code value of each probe element between the minimum code value and the maximum code value, and the number of the code values with the frequency not being zero is counted and recorded as the effective code value quantity. And calculating the average value of all the probe effective code value quantities as the average effective code value quantity of the temporary reference probe.
After the image correction, the cumulative probability distribution P (DN) of all image code values is counted, and the range between 1% and 99% of the image cumulative probability is set as the medium energy distribution range of the image. The code value for which 1% corresponds is labeled as the least significant value DNminWith 99% of the corresponding code value marked as the most significant code value DNmaxCalculated as follows:
DNmin＝arg(P(DN)＝1％),DNmax＝arg(P(DN)＝99％)
the probabilities corresponding to the minimum effective code value and the maximum effective code value may also use other probability values than 1% and 99%, and may be appropriately adjusted according to the requirements of the relatively scaled subsequent specific application scenarios.
Respectively counting the occurrence frequency hist of each probe code valuej(DN),j∈[0,N]After the lookup table is corrected, the individual code values of part of the probes will not appear in the corrected image, so the corresponding histj(DN) is 0. Flag for marking effective output code value of each probe elementj(DN) represented by the formula:
counting the number Num of effective output code values of each probe element between the minimum effective code value and the maximum effective code valueDNThe calculation formula is as follows:
calculating average value Num of all channel probe effective output code value quantityeffective-DNThe calculation formula is as follows:
the average effective code value amount describes the average effective code value amount required for describing medium incident energy, namely the radiation resolving power of the corrected image, by taking the probe element as a temporary reference probe element.
And fourthly, sequentially taking each probe element in the channel as a temporary reference probe element, repeating the first step to the third step, and constructing an average effective code value quantity data set Numset corresponding to each probe elementeffective-DN。
NumSeteffective-DN＝{Numeffective-DN(i),i＝1,2,…,N}
And fifthly, calculating the radiation resolution index. Calculating the linear score of the average effective code value of each probe element by taking the maximum value of the average effective code value as 100 points and the minimum value as 0 point as an image radiation resolution index and marking as Ienergy-resolutionThe calculation formula is shown below.
When the average effective code value amount is the maximum value, Ienergy-resolutionThe value of (A) is 100; when the average effective code value amount is the minimum value, Ienergy-resolutionIs 0, otherwise, Ienergy-resolutionThe values of (c) are distributed between 0 and 100.
Based on any of the above embodiments, step S5 in the method specifically includes:
and calculating by adopting a weighting algorithm to obtain the comprehensive score of the probe element.
Wherein the weighting algorithm comprises an average weighting or a non-average weighting.
Specifically, the comprehensive probe element score is calculated by adopting a weighted average method on the basis of an image average brightness change index, an image information entropy index and a radiation resolution index. The weights of the three indexes can be the same, and the weight of each index can be adjusted according to the requirements of subsequent applications. And taking the probe element with the highest comprehensive probe element score as a reference probe element for relative calibration.
The probe composite score can be calculated using the following formula:
Iestimate(i)＝ωbrightIbright(i)+ωentropyIentropy(i)+ωenergy-resolutionIenergy-resolution(i)
wherein I is the number of probe element, IestimateFor comprehensive scoring of probe elements, IbrightIs an index of change in average brightness of an image, IentropyAs entropy index of image information, Ienergy-resolutionIs an image radiation resolution index; omegabright，ωentropyAnd ωenergy-resolutionWeights of three indices, respectively, require ωbright+ωentropy+ωenergy-resolution1, typically take ωbright＝ωentropy＝ωenergy-resolution＝1/3。
With IestimateThe highest probe is the reference probe, i.e. istandard＝argmax(Iestimate(i))，istandardNamely the reference probe number.
The following is a case of the embodiment of the present invention using FY-3D medium resolution spectral imager (MERSI) as an example. Taking channel 3 as an example, the earth observation data of MERSI between 2018-4-10 and 2018-4-18 is counted.
The frequency map (hist) of the code values of each probe is counted, as shown in the left diagram in fig. 2, and the code value cumulative probability distribution function p (dn) is shown in the right diagram in fig. 2. As can be seen from the data distribution in the graph, the radiation response difference exists between the channel 3 probes.
The relative scaling lookup table constructed by the method described in the foregoing embodiment with the probe No. 26 as the temporary reference probe is shown in fig. 3.
Similarly, a relative scaling look-up table may be constructed using other probes in the channel as temporary reference probes.
First, according to the method described in the foregoing embodiment, the absolute difference of the average code values of the respective probes is calculated, as shown in the left diagram of fig. 4, and the overall brightness change index is calculated as shown in the right diagram of fig. 4. And taking different probe elements as temporary reference pixels, wherein the difference between the corrected image and the original average brightness is different. The left panel in fig. 4 shows the least difference between probe No. 10 and the most difference between probe No. 14. After linear scoring, the overall brightness change index of probe No. 10 is 100, while the overall brightness change index of probe No. 14 is 0, and the indexes of other probes are between 0 and 100.
Next, according to the method described in the foregoing embodiment, the entropy of the image information of each probe element is calculated, as shown in the left diagram in fig. 5, and the entropy index of the image information is calculated as shown in the right diagram in fig. 5. And taking different probe elements as temporary reference pixels, and correcting the entropy of the image information. The left image in fig. 5 shows that the entropy of the information of the No. 20 probe image is the largest, and the entropy of the information of the No. 14 probe image is the smallest. After linear scoring, the entropy index of the image information of probe No. 20 is 100, and the entropy index of the image information of probe No. 14 is 0.
Again, the average effective code value amount of each probe element is calculated as described in the previous embodiment, as shown in the left diagram of fig. 6, and the calculated image radiation resolution index is shown in the right diagram of fig. 6. And taking different probe elements as temporary reference pixels, and correcting the average effective code value amount. The left diagram in fig. 6 shows that the average effective code value amount for probe No. 20 is the largest and the average effective code value amount for probe No. 14 is the smallest. After linear scoring, the image radiation resolution index of probe 20 is 100, and the image radiation resolution index of probe 14 is 0.
Further, on the basis of the image average brightness change index, the image information entropy index and the radiation resolution index, the equal-weight weighted average method is adopted to calculate the comprehensive probe score, as shown in fig. 7. The highest grade is probe number 19, while the reference probe currently used in business is probe number 26, the comprehensive grade is 76.9, and the ranking is only 15 th. Therefore, it is more suitable to propose that the service uses the probe number 19 as the reference probe.
The selection system of the linear array scanning remote sensor reference probe element provided by the embodiment of the invention is described below, and the selection system of the linear array scanning remote sensor reference probe element described below and the selection method of the linear array scanning remote sensor reference probe element described above can be referred to correspondingly.
Fig. 8 is a schematic structural diagram of a system for selecting a reference probe of a line-scanning remote sensor according to an embodiment of the present invention, as shown in fig. 8, including: an acquisition module 81, a first processing module 82, a second processing module 83, a third processing module 84, and a synthesis module 85; wherein:
the acquisition module 81 is used for acquiring an original remote sensing image, and correcting the original remote sensing image through relative calibration to obtain a corrected image; the first processing module 82 is used for calculating an image average brightness change index based on the original remote sensing image and the corrected image; the second processing module 83 is configured to calculate an image information entropy index based on the original remote sensing image and the corrected image; the third processing module 84 is configured to calculate an image radiation resolution index based on the original remote sensing image and the corrected image; the comprehensive module 85 is configured to calculate the average brightness change index of the image, the entropy index of the image information, and the radiation resolution index of the image to obtain a comprehensive probe element score, and select a probe element with the highest comprehensive probe element score as a reference probe element for relative calibration.
According to the embodiment of the invention, a scoring method based on the distribution characteristics of the output code values of the probe elements is provided by aiming at the problem of how to select the reference probe elements in the relative calibration process of the linear array scanning remote sensor, the probe elements with the highest score are selected as the reference probe elements, and the most appropriate reference probe elements are selected from the aspect of the statistical characteristics of the earth observation data.
Fig. 9 illustrates a physical structure diagram of an electronic device, and as shown in fig. 9, the electronic device may include: a processor (processor)910, a communication Interface (Communications Interface)920, a memory (memory)930, and a communication bus 940, wherein the processor 910, the communication Interface 920, and the memory 930 communicate with each other via the communication bus 940. Processor 910 may invoke logic instructions in memory 930 to perform a method of line scan remote sensor reference probe selection, the method comprising: acquiring an original remote sensing image, and correcting the original remote sensing image through relative calibration to obtain a corrected image; calculating to obtain an image average brightness change index based on the original remote sensing image and the corrected image; calculating to obtain an image information entropy index based on the original remote sensing image and the corrected image; calculating to obtain an image radiation resolution index based on the original remote sensing image and the corrected image; and calculating the image average brightness change index, the image information entropy index and the image radiation resolution index to obtain a comprehensive probing element score, and selecting the probing element with the highest comprehensive probing element score as a relative calibration reference probing element.
Furthermore, the logic instructions in the memory 930 may be implemented in software functional units and stored in a computer readable storage medium when the logic instructions are sold or used as independent products. 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 and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, an embodiment of the present invention further provides a computer program product, where the computer program product includes a computer program stored on a non-transitory computer-readable storage medium, where the computer program includes program instructions, and when the program instructions are executed by a computer, the computer is capable of executing the method for selecting a reference probe of a linear array scanning remote sensor provided in the foregoing method embodiments, where the method includes: acquiring an original remote sensing image, and correcting the original remote sensing image through relative calibration to obtain a corrected image; calculating to obtain an image average brightness change index based on the original remote sensing image and the corrected image; calculating to obtain an image information entropy index based on the original remote sensing image and the corrected image; calculating to obtain an image radiation resolution index based on the original remote sensing image and the corrected image; and calculating the image average brightness change index, the image information entropy index and the image radiation resolution index to obtain a comprehensive probing element score, and selecting the probing element with the highest comprehensive probing element score as a relative calibration reference probing element.
In another aspect, an embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program is implemented by a processor to execute the method for selecting the reference probe of the linear array scanning remote sensor provided in the foregoing embodiments, where the method includes: acquiring an original remote sensing image, and correcting the original remote sensing image through relative calibration to obtain a corrected image; calculating to obtain an image average brightness change index based on the original remote sensing image and the corrected image; calculating to obtain an image information entropy index based on the original remote sensing image and the corrected image; calculating to obtain an image radiation resolution index based on the original remote sensing image and the corrected image; and calculating the image average brightness change index, the image information entropy index and the image radiation resolution index to obtain a comprehensive probing element score, and selecting the probing element with the highest comprehensive probing element score as a relative calibration reference probing element.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for selecting a reference probe element of a linear array scanning remote sensor is characterized by comprising the following steps:
acquiring an original remote sensing image, and correcting the original remote sensing image through relative calibration to obtain a corrected image;
calculating to obtain an image average brightness change index based on the original remote sensing image and the corrected image;
calculating to obtain an image information entropy index based on the original remote sensing image and the corrected image;
calculating to obtain an image radiation resolution index based on the original remote sensing image and the corrected image;
and calculating the image average brightness change index, the image information entropy index and the image radiation resolution index to obtain a comprehensive probing element score, and selecting the probing element with the highest comprehensive probing element score as a relative calibration reference probing element.
2. The method for selecting the linear array scanning remote sensor reference probe element according to claim 1, wherein the calculating based on the original remote sensing image and the corrected image to obtain an image average brightness change index specifically comprises:
selecting any probe element as a temporary reference probe element, and constructing a first relative calibration lookup table;
correcting the original remote sensing image based on the first relative calibration lookup table to obtain a first corrected original image;
calculating the absolute difference value of the average code values of the first correction original image and the original remote sensing image;
sequentially selecting the rest probe elements in the channel as temporary reference probe elements, repeating the steps, and constructing an average code value absolute difference data set;
and calculating the image average brightness change index based on the average code value absolute difference data set.
3. The method for selecting the linear array scanning remote sensor reference probe element according to claim 1, wherein the step of calculating an image information entropy index based on the original remote sensing image and the corrected image specifically comprises:
selecting any probe element as a temporary reference probe element, and constructing a second relative calibration lookup table;
correcting the original remote sensing image based on the second relative calibration lookup table to obtain a second corrected original image;
calculating a correction image information entropy of the second correction original image;
sequentially selecting the rest probe elements in the channel as temporary reference probe elements, repeating the steps, and constructing an information entropy data set;
and calculating the image information entropy index based on the information entropy data set.
4. The method for selecting the remote sensor reference probe element of the linear array scanning according to claim 1, wherein the step of calculating the image radiation resolution index based on the original remote sensing image and the corrected image specifically comprises the steps of:
selecting any probe element as a temporary reference probe element, and constructing a third relative calibration lookup table;
correcting the original remote sensing image based on the third relative calibration lookup table to obtain a third corrected original image;
calculating the average effective code value amount of the third correction original image;
sequentially selecting the rest probe elements in the channel as temporary reference probe elements, repeating the steps, and constructing an average effective code value data set;
and calculating the image radiation resolution index based on the average effective code value data set.
5. The method for selecting the remote sensor reference probe element for linear array scanning according to claim 1, wherein the step of calculating the image average brightness change index, the image information entropy index and the image radiation resolution index to obtain the probe element comprehensive score specifically comprises the steps of:
and calculating by adopting a weighting algorithm to obtain the comprehensive score of the probe element.
6. The method of claim 5, wherein the weighting algorithm comprises an average weighting or a non-average weighting.
7. A method for selecting a remote sensor reference probe as claimed in any one of claims 1 to 6, wherein the relative scaling is achieved by cumulative probability.
8. A system for selecting a reference probe element of a linear array scanning remote sensor is characterized by comprising:
the acquisition module is used for acquiring an original remote sensing image, and correcting the original remote sensing image through relative calibration to obtain a corrected image;
the first processing module is used for calculating to obtain an image average brightness change index based on the original remote sensing image and the corrected image;
the second processing module is used for calculating to obtain an image information entropy index based on the original remote sensing image and the corrected image;
the third processing module is used for calculating to obtain an image radiation resolution index based on the original remote sensing image and the corrected image;
and the comprehensive module is used for calculating the image average brightness change index, the image information entropy index and the image radiation resolution index to obtain a comprehensive probe element score, and selecting the probe element with the highest comprehensive probe element score as a relative calibration reference probe element.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program performs the steps of the method of selecting a remote sensor reference probe as claimed in any one of claims 1 to 7.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of a method for reference probe selection for a line scanning remote sensor according to any of claims 1 to 7.
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