CN112001263B - Method and system for selecting reference probe of linear array scanning remote sensor - Google Patents
Method and system for selecting reference probe 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 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 (3) obtaining the comprehensive score of the probe element by calculating the average brightness change index, the entropy index of the image information and the radiation resolution index of the image, and selecting the probe element with the highest comprehensive score of the probe element as a reference probe element for relative calibration. Aiming at the problem of how to select the reference probe in the relative calibration process of the linear array scanning remote sensor, the embodiment of the invention provides a scoring method based on the distribution characteristics of the output code values of the probe, selects the probe with the highest score as the reference probe, and selects the most suitable reference probe from the angle 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 of a linear array scanning remote sensor.
Background
In the linear array scanning imaging remote sensing data, the stripe phenomenon is very common, for example, in a wind cloud No. three (FY-3), a resolution spectrum imager (Medium Resolution Spectral Imager, MERSI) is a linear array instrument with 10/40 probe elements, and the stripe phenomenon exists in the original image downloaded from the star. Such banding is typically accomplished by relative scaling between the probe elements to remove the fringes.
Currently, there are three main types of relative scaling methods: one is to use on-board normalization, that is, electronic technology, to linearly transform and round the code value (DN) of the original sample according to the relative difference of the radiation responses among the probe elements, and then to download. This approach can greatly inhibit streaking, but is often not thorough. The other is based on computer image algorithm cancellation such as wavelet transform, median filtering, etc. The algorithms can obtain good fringe stripe elimination effect for a single image. The method has the defects that the algorithm relies on the image to extract correction parameters, but the parameters are not stable enough and can not meet the requirement of automatic processing; meanwhile, as the images are subjected to nonlinear processing, the reflectivity information contained in the processed images is irreversibly changed, and errors which are difficult to quantitatively evaluate are introduced for subsequent quantitative application. In terms of time complexity, different algorithms are different in time consuming, but often affect the processing timeliness of the service system. In addition, the method for matching the empirical distribution function has more discussion, is stable and reliable, has low time complexity and hardly affects quantitative application.
In the relative calibration method, reference probe elements are key technical parameters, different probe elements in a channel are selected as the reference probe elements, the quality of remote sensing images after relative calibration is different, the existing method for selecting the reference probe elements 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 reference probe elements on the images after correction.
Disclosure of Invention
The embodiment of the invention provides a method and a system for selecting a reference probe of a linear array scanning remote sensor, which are used for solving the defects in the prior art and realizing the selection of the most suitable reference probe.
In a first aspect, an embodiment of the present invention provides a method for selecting a reference probe of a linear array scanning remote sensor, 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 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 score of the probe elements, and selecting the probe element with the highest comprehensive score of the probe elements as a reference probe element for relative calibration.
Further, the calculating to obtain the 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 an average code value absolute difference value between the first corrected 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 average brightness change index of the image based on the average code value absolute difference data set.
Further, the calculating to obtain the image information entropy 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 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 the corrected image information entropy of the second corrected 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 the 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 quantity of the third correcting 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 quantity data set.
Further, the method for 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 score of the probe element specifically comprises the following steps:
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 achieved 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, correcting the original remote sensing image through relative calibration, and obtaining a corrected image;
the first processing module 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 is used for calculating an image information entropy index based on the original remote sensing image and the corrected image;
the third processing module is used for calculating 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 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 score of the probe elements, and selecting the probe element with the highest comprehensive score of the probe elements as a reference probe element for relative calibration.
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 capable of running on the processor, where the processor implements the steps of any one of the methods for selecting the reference probe of the line-scan remote sensor when the processor executes the program.
In a fourth aspect, embodiments of the present invention also provide a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of a method for selecting a reference probe of a line scan remote sensor as described in any of the above.
According to the method and the system for selecting the reference probe of the linear array scanning remote sensor, a scoring method based on the distribution characteristics of the output code values of the probe is provided by aiming at the problem of how to select the reference probe in the relative calibration process of the linear array scanning remote sensor, the probe with the highest score is selected as the reference probe, and the most suitable reference probe is selected from the point of view 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 of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for selecting a reference probe of a linear array scanning remote sensor according to an embodiment of the present invention;
fig. 2 is a schematic diagram of code value frequency distribution and code value cumulative probability of each probe element in a channel 3 according to an embodiment of the present invention;
FIG. 3 is a relative scaled look-up representation of channel 3 intent provided by an embodiment of the present invention;
FIG. 4 is a schematic diagram of an image correction average code value, an absolute difference value of an original image and an image average brightness index distribution according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of distribution of image information entropy with the number of the probe and distribution of image information entropy indexes according to the embodiment of the invention;
FIG. 6 is a schematic diagram showing the distribution of the average effective code value quantity of an image according to the number of the probe elements and the distribution of the radiation resolution index of the image according to the embodiment of the invention;
FIG. 7 is a schematic diagram showing the distribution of the comprehensive scores of the probe cells with the numbers of the probe cells according to the embodiment of the invention;
FIG. 8 is a schematic structural diagram of a system for selecting reference probe elements of a linear array 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
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The terms "first," "second," and "third" in the present embodiments 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 defining "a first", "a second", and "a third" may explicitly or implicitly include at least one such feature. In the description of the present application, the terms "comprise" and "have," along with any variations thereof, are intended to cover non-exclusive inclusions. For example, a system, article, or apparatus that comprises a list of elements is not limited to only those elements or units listed but may alternatively include other elements not listed or inherent to such article, or apparatus. In the description of the present application, the meaning of "plurality" means at least two, for example, two, three, etc., unless specifically defined otherwise.
Aiming at the problems existing in the prior art, the embodiment of the invention adopts a comprehensive evaluation method to integrate the brightness change of the image, the entropy of the image information and the radiation resolution of the image into a single index for evaluating the potential capability of each probe element as a reference probe element.
Fig. 1 is a flow chart of a method for selecting a reference probe of a linear 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 multi-element linear array scanning mode to acquire an earth observation image, wherein channels 1-4 are 250 m resolution channels, and 40 probe elements are scanned; the channels 6-20 are 1000 m resolution channels, 10 probe elements are scanned. Because the linear array detector has natural radiation response difference, the original image is striped, the relative calibration of the image is required to be completed, the stripes can be removed, the quantitative application can be further carried out, 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 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 observed output code values, and the difference before and after correction is estimated by constructing an average brightness change index in consideration of the fact that the brightness of the image is required to change slightly before and after relative calibration. And constructing a relative calibration lookup table by using a probe in the channel as a temporary reference probe and utilizing an accumulated probability method. And correcting the original remote sensing data of other probe elements by using the relative calibration lookup table. And calculating an average code value of the corrected image and an average code value of the original image, and then calculating to obtain an absolute difference value of the two. 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 taking the minimum value of the absolute difference value of the average code value as 100 minutes and the maximum value as 0 minutes to give linear scores of the absolute difference values of the average code values of all the probe elements as an image average brightness change index. And (3) an image average brightness change index, describing the difference condition of the average brightness of the corrected image and the average brightness of the original image when the probe is used as a temporary reference probe.
S3, calculating 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 through calculation of probabilities corresponding to the output code values. Based on the images of different relative scaling of the probe elements, the entropy of the image information of the 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 constructing a relative calibration lookup table by using a probe in the channel as a temporary reference probe and utilizing an accumulated probability method. And correcting the original remote sensing data of other probe elements by using the relative calibration lookup table. And calculating the entropy of the corrected image information. 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 taking the maximum value of the image information entropy as 100 points and the minimum value as 0 point, and giving out the linear score of the image information entropy of each probe element as an image information entropy index. And when the probe is used as a temporary reference probe, correcting the quantity of the image information after the original image, wherein the higher the image information entropy index score is, the larger the information content 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 resolution ability to distinguish small changes in energy in an image. The number of gray levels output by the remote sensor for a range of incident energies, the greater the number of gray levels used, the finer the image description of the energy change. And constructing a relative calibration lookup table by using a probe in the channel as a temporary reference probe and utilizing an accumulated probability method. And correcting the original remote sensing data of other probe elements by using the relative calibration lookup table. And after the image is corrected, taking the minimum code value and the maximum code value corresponding to the medium energy. Counting the frequency of each code value of each probe element between the minimum code value and the maximum code value of the corrected image, counting the number of code values with the frequency of the code value not being zero, and marking the number as the effective code value quantity. And calculating the average value of the effective code value quantities of all the probe elements to be used as the average effective code value quantity of the temporary reference probe element. 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 taking the maximum value of the average effective code value quantity as 100 minutes and the minimum value as 0 minute, and giving out the linear score of the average effective code value quantity of each probe element as an image radiation resolution index. And when describing the probe as a temporary reference probe, correcting the radiation resolution capability of the original remote sensing image, wherein the radiation resolution is better as the index is larger.
S5, obtaining a comprehensive score of the probe element by calculating the average brightness change index of the image, the entropy index of the image information and the radiation resolution index of the image, and selecting the probe element with the highest comprehensive score of the probe element as a reference probe element for relative calibration.
The fine and stable relative calibration is an important constituent function of the ground preprocessing system of the remote sensing data, and has very important significance for subsequent data application. The selection of the reference probe is an important link of relative calibration, and the comprehensive influence of the adopted reference probe on the aspects of corrected image brightness, image information quantity, image radiation resolution capability and the like should be considered. The comprehensive score of the probe element is calculated by adopting a weighted average method based on 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 requirement of the subsequent application. And taking the probe element with the highest comprehensive score of the probe elements 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 element with the highest score is selected as the reference probe element, and the most suitable reference probe element is selected from the angle of the statistical characteristics of 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 an average code value absolute difference value between the first corrected 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 average brightness change index of the image based on the average code value absolute difference data set.
Specifically, in the first step, a relative calibration lookup table is constructed by taking one probe as a temporary reference probe. Selecting one probe element i of a channel as a temporary reference probe element, firstly counting each original code value DN output by the probe element in an earth observation image * Frequency hist of (2) i (DN * ) Then, calculating a code value cumulative probability distribution function of the earth observation image, wherein the code value cumulative probability distribution function is as follows:
wherein P is i For the cumulative probability distribution function of the code values, maxDN is the maximum designed output code value of the channel, the quantization level of the MERSI channel is 12 bits, and then the maxDN is 4095.
The undetermined probe j is processed in the same way to obtain a code value cumulative probability distribution function P j After the relative calibration, the code value distribution output by the probe element j is the same as the probe element i, and the corresponding relation between the output code value and the original code value of the probe element j after the relative calibration can be obtained by using the following formula:
in the method, in the process of the invention,is P i Is an inverse function of (c).
Because of the discrete values, a static code value lookup table of relative scaling of the probe element j to the temporary reference probe element i can be constructed:
in the formula, LUT j Look-Up-Table (LUT) for the probe element j.
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 a corrected code value by using a 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 following formula is as follows:
wherein N is the number of all probe elements in the channel, N is 40 for the 250 meter resolution channels (channels 1-4, 24, 25) of MERSI; the 1000 meter resolution channel (channels 5-19) N is 10.
And thirdly, calculating the absolute difference value of the average code values of the images before and after correction. Calculating average code values of relatively scaled imagesThe calculation formula is as follows:
wherein, the numerator is the sum of the code values of all pixels, and the denominator Number AllPixals Is the number of total pixels.
Calculating the average code value of the original imageThe calculation formula is as follows:
further, an absolute difference DeltaDN between the relatively scaled image average code value and the original image average code value mean The calculation can be performed as follows.
Fourth, 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 code value absolute difference data set delta DNSet mean The following formula:
ΔDNSet mean ＝{ΔDN mean (i),i＝1,2,…,N}
and fifthly, calculating an overall brightness change index. Calculating the linear scores of the average brightness variation before and after the relative calibration of the images when each probe element is used as a temporary reference probe element by taking the minimum value of the absolute difference of the average code value as 100 points and the maximum value as 0 point as the average brightness variation index of the images, and marking as I bright The formula is shown as follows:
when the absolute difference of the average code value is the minimum value, I bright The value of (2) is 100, and the average brightness change of the images before and after relative scaling is minimum; when the absolute difference of the average code value is the maximum value, I bright The value of (2) is 0, in other cases, I bright The number of (2) is 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 the corrected image information entropy of the second corrected 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 a first step, one in the channelThe probe is used as a temporary reference probe, and a relative calibration lookup table is constructed by using an accumulated probability method. Selecting a probe element i of a channel as a temporary reference probe element, firstly counting out each original code value DN of the probe element in a ground observation image * Frequency hist of (2) i (DN * ) Then, calculating a code value cumulative probability distribution function of the earth observation image, wherein the code value cumulative probability distribution function is as follows:
wherein P is i For the cumulative probability distribution function of the code values, maxDN is the maximum designed output code value of the channel, and if the channel quantization level is 12 bits, the maxDN is 4095.
The undetermined probe j is processed in the same way to obtain a code value cumulative probability distribution function P j After the relative calibration, the code value distribution output by the probe element j is the same as the probe element i, and the corresponding relation between the output code value and the original code value of the probe element j after the relative calibration can be obtained by using the following formula:
in the method, in the process of the invention,is P i Is an inverse function of (c).
Because of the discrete values, a static code value lookup table of relative scaling of the probe element j to the temporary reference probe element i can be constructed:
In the formula, LUT j Look-Up-Table (LUT) for the probe element 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 a corrected code value by using a 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 following formula is as follows:
where N is the number of all probe elements in the channel.
And thirdly, calculating the corrected image information entropy. After calculating the relative calibration, the frequency hist (DN) corresponding to each code value of the image is calculated, and then the probability p (DN) corresponding to each code value is calculated, wherein the following formula is as follows:
in the formula, number AllPixals Is 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.
HSet effective-DN ＝{H(i),i＝1,2,…,N}
And fifthly, calculating an image information entropy index. Taking the maximum value of the image information entropy as 100 points and the minimum value as 0 point, calculating the linear score of the image information entropy when each probe element is taken as a temporary reference probe element, and taking the linear score as an image information entropy index and marking as I entropy The formula is shown as follows:
When the entropy of the image information is maximum, I entropy With a value of 100, when the probe element i is taken as a temporary reference probe elementThe relative scaled image information amount is the largest; when the entropy of the image information is the minimum value, I entropy The value of (2) is 0, in other cases, I entropy The number of (2) is 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 quantity of the third correcting 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 quantity data set.
Specifically, in the first step, a probe in a channel is used as a temporary reference probe, and a relative calibration lookup table is constructed by using an accumulated probability method. Selecting a probe element i of a channel as a temporary reference probe element, firstly counting out each original code value DN of the probe element in a ground observation image * Frequency hist of (2) i (DN * ) Then, a code value cumulative probability distribution function of the earth observation image is calculated, as follows
Wherein P is i For the cumulative probability distribution function of the code values, maxDN is the maximum designed output code value of the channel, and if the channel quantization level is 12 bits, the maxDN is 4095.
The undetermined probe j is processed in the same way to obtain a code value cumulative probability distribution function P j After the relative calibration, the code value distribution output by the probe element j is the same as the probe element i, and the corresponding relation between the output code value and the original code value of the probe element j after the relative calibration can be obtained by using the following formula:
in the method, in the process of the invention,is P i Is an inverse function of (c).
Because of the discrete values, a static code value lookup table of relative scaling of the probe element j to the temporary reference probe element i can be constructed:
in the formula, LUT j Look-Up-Table (LUT) for the probe element 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 a corrected code value by using a 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 following formula is as follows:
where N is the number of all probe elements in the channel.
And thirdly, calculating the average effective code value quantity. And after the image is corrected, the minimum code value and the maximum code value corresponding to the medium incident energy are obtained. After the statistic image is corrected, the frequency of each code value between the minimum code value and the maximum code value of each probe element is counted, and the number of code values with the frequency of the code value being different from zero is counted and is recorded as the effective code value quantity. And calculating the average value of the effective code value quantities of all the probe elements to be used as the average effective code value quantity of the temporary reference probe element.
After the image correction, the cumulative probability distribution P (DN) of all the image code values is counted, and the image cumulative probability of 1% to 99% is set as the medium energy distribution range of the image. The 1% corresponding code value is marked as the minimum effective code value DN min 99% of the corresponding code values are marked as maximally validCode value DN max The following formula is calculated:
DN min ＝arg(P(DN)＝1％),DN max ＝arg(P(DN)＝99％)
the probability corresponding to the minimum effective code value and the maximum effective code value can also use other probability values except 1% and 99%, and can be properly adjusted according to the requirements of the subsequent specific application scenes of the relative calibration.
Respectively counting the occurrence frequency hist of each probe element code value j (DN),j∈[0,N]After the lookup table is corrected, the individual code values of part of the probe elements do not appear in the corrected image, so the corresponding hist j (DN) =0. Marking the effective output code value flag of each probe element j (DN) as shown in the following formula:
counting the number Num of the effective output code values of each probe element between the minimum effective code value and the maximum effective code value DN The formula is as follows:
calculating the average value Num of the number of the code values effectively output by all the probe elements of the channel effective-DN The formula is as follows:
the average effective code value quantity describes the average effective code value quantity required by the corrected image to describe medium incident energy under the condition that the probe element is taken as a temporary reference probe element, namely the radiation resolution capability of the corrected image.
Fourth, 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 data set NumSet corresponding to each probe element effective-DN 。
NumSet effective-DN ＝{Num effective-DN (i),i＝1,2,…,N}
Fifth, the radiation resolution index is calculated. Calculating the linear score of the average effective code value of each probe element as an image radiation resolution index, and marking as I by taking the maximum value of the average effective code value as 100 min and the minimum value as 0 min energy-resolution The calculation formula is shown below.
When the average effective code value amount is the maximum value, I energy-resolution The value of (2) is 100; when the average effective code value is minimum, I energy-resolution The value of (2) is 0, in other cases, I energy-resolution The number of (2) is 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 probe element comprehensive score is based on an image average brightness change index, an image information entropy index and a radiation resolution index, and a weighted average method is adopted to calculate a numerical value. The weights of the three indexes can be the same, and the weight of each index can be adjusted according to the requirement of the subsequent application. The probe element with the highest comprehensive score is used as a reference probe element for relative calibration.
The probe composite score can be calculated using the following formula:
I estimate (i)＝ω bright I bright (i)+ω entropy I entropy (i)+ω energy-resolution I energy-resolution (i)
wherein I is the number of the probe element, I estimate For comprehensive scoring of the probe elements, I bright I is the mean brightness variation index of the image entropy For image informationEntropy index, I energy-resolution A radiation resolution index for the image; omega bright ，ω entropy And omega energy-resolution Weights of three exponents, respectively, require ω bright +ω entropy +ω energy-resolution =1, generally ω bright ＝ω entropy ＝ω energy-resolution ＝1/3。
I is as follows estimate The highest probe is the reference probe, i.e standard ＝argmax(I estimate (i))，i standard The reference probe number is the reference probe number.
The following is an implementation of an embodiment of the present invention using a FY-3D medium resolution spectral imager (MERSI) as an embodiment. Taking channel 3 as an example, earth observation data for MERSI between 2018-4-10 to 2018-4-18 is counted.
The frequency chart (hist) of the code values of each probe element is counted, as shown in the left chart of fig. 2, and the cumulative probability distribution function P (DN) of the code values is shown in the right chart of fig. 2. From the data distribution in the figure, it can be seen that there is a difference in radiation response between the channel 3 probe elements.
The relative calibration lookup table constructed using the probe number 26 as a temporary reference probe according to the method described in the previous embodiment is shown in fig. 3.
Likewise, other probe elements within the channel may be employed as temporary reference probe elements to construct a relative scaling look-up table.
First, according to the method described in the foregoing embodiment, the absolute difference of the average code value of each probe cell is calculated as shown in the left graph of fig. 4, and the calculated overall brightness change index is shown in the right graph of fig. 4. Different probe elements are used as temporary reference pixels, and the corrected image is different from the original average brightness difference. The left plot in fig. 4 shows that probe number 10 has the smallest difference and probe number 14 has the largest difference. After linear scoring, the overall brightness change index of the number 10 probe is 100, while the overall brightness change index of the number 14 probe is 0, and the indexes of other probe are between 0 and 100.
Next, according to the method described in the foregoing embodiment, the image information entropy of each probe element is calculated, as shown in the left graph in fig. 5, and the calculated image information entropy index is shown in the right graph in fig. 5. And taking different probe elements as temporary reference pixels, and correcting the entropy of the image information. The left graph in fig. 5 shows that the information entropy of the number 20 probe element image is the largest and the information entropy of the number 14 probe element image is the smallest. After linear scoring, the image information entropy index of the No. 20 probe is 100, and the image information entropy index of the No. 14 probe is 0.
Again, according to the method described in the foregoing embodiment, the average effective code value amounts of the respective probe elements are calculated 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 quantity. The left graph in fig. 6 shows that the number 20 probe element has the largest average effective code value and the number 14 probe element has the smallest average effective code value. After the linear scoring, the image radiation resolution index of the probe No. 20 is 100, and the image radiation resolution index of the probe No. 14 is 0.
Further, the probe element comprehensive score is calculated by adopting an equal weight weighted average method based on the image average brightness change index, the image information entropy index and the radiation resolution index, as shown in fig. 7. The highest score is 19 # probe elements, and the currently used reference probe elements in business are 26 # probe elements, the comprehensive score is 76.9, and the ranking is only 15 th. Therefore, it is more suitable to propose to use 19 # probe as the reference probe in service.
The system for selecting the reference probe of the linear array scanning remote sensor provided by the embodiment of the invention is described below, and the system for selecting the reference probe of the linear array scanning remote sensor described below and the method for selecting the reference probe of the linear array scanning remote sensor described above can be referred to correspondingly.
Fig. 8 is a schematic structural diagram of a system for selecting a reference probe of a linear array 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 configured to acquire an original remote sensing image, correct the original remote sensing image through relative calibration, and obtain a corrected image; the first processing module 82 is configured to calculate an average brightness change index of an image 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 synthesis 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 score of the probe elements, and select the probe element with the highest comprehensive score of the probe elements 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 element with the highest score is selected as the reference probe element, and the most suitable reference probe element is selected from the angle of the statistical characteristics of earth observation data.
Fig. 9 illustrates a physical schematic diagram of an electronic device, as shown in fig. 9, which may include: processor 910, communication interface (Communications Interface), memory 930, and communication bus 940, wherein processor 910, communication interface 920, and memory 930 communicate with each other via communication bus 940. Processor 910 may invoke logic instructions in memory 930 to perform a method of selecting a reference probe for a linear array scanning remote sensor, 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 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 score of the probe elements, and selecting the probe element with the highest comprehensive score of the probe elements as a reference probe element for relative calibration.
Further, the logic instructions in the memory 930 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform 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, random Access Memory), a magnetic disk, or an optical disk, or 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, when the program instructions are executed by a computer, for executing the method for selecting a reference probe of a line-scan 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 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 score of the probe elements, and selecting the probe element with the highest comprehensive score of the probe elements as a reference probe element for relative calibration.
In still 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 when executed by a processor to perform the method for selecting a reference probe of a 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 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 score of the probe elements, and selecting the probe element with the highest comprehensive score of the probe elements as a reference probe element for relative calibration.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the 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 scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (7)
1. A method for selecting a reference probe 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;
obtaining a probe element comprehensive score through calculating the image average brightness change index, the image information entropy index and the image radiation resolution index, and selecting a probe element with the highest probe element comprehensive score as a reference probe element for relative calibration;
the calculating to obtain the 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 an average code value absolute difference value between the first corrected 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;
calculating the average brightness change index of the image based on the average code value absolute difference data set;
the image information entropy index is calculated based on the original remote sensing image and the corrected image, and specifically comprises the following steps:
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 the corrected image information entropy of the second corrected 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;
calculating the image information entropy index based on the information entropy data set;
the image radiation resolution index is calculated based on the original remote sensing image and the corrected image, and specifically comprises the following steps:
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 quantity of the third correcting 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 quantity data set.
2. The method for selecting the reference probe of the linear array scanning remote sensor according to claim 1, wherein the method for obtaining the comprehensive score of the probe by calculating the average brightness change index of the image, the entropy index of the image information and the radiation resolution index of the image specifically comprises the following steps:
and calculating by adopting a weighting algorithm to obtain the comprehensive score of the probe element.
3. The method of claim 2, wherein the weighting algorithm comprises an average weighting or a non-average weighting.
4. A method of selecting reference probe elements for a linear array scanning remote sensor according to any one of claims 1 to 3, wherein the relative scaling is achieved by means of an accumulated probability method.
5. The system for selecting the reference probe of the linear array scanning remote sensor is characterized by comprising the following components:
the acquisition module is used for acquiring an original remote sensing image, correcting the original remote sensing image through relative calibration, and obtaining a corrected image;
the first processing module 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 is used for calculating an image information entropy index based on the original remote sensing image and the corrected image;
the third processing module is used for calculating an image radiation resolution index based on the original remote sensing image and the corrected image;
the comprehensive module is used for 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 comprehensive scores of the probe elements, and selecting the probe element with the highest comprehensive score of the probe elements as a reference probe element for relative calibration;
the first processing module is specifically configured to:
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 an average code value absolute difference value between the first corrected 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;
calculating the average brightness change index of the image based on the average code value absolute difference data set;
the second processing module is specifically configured to:
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 the corrected image information entropy of the second corrected 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;
calculating the image information entropy index based on the information entropy data set;
the third processing module is specifically configured to:
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 quantity of the third correcting 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 quantity data set.
6. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor performs the steps of the method of selecting a reference probe for a linear array scanning remote sensor as claimed in any one of claims 1 to 4 when the program is executed.
7. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the method of selecting a reference probe of a linear array scanning remote sensor as claimed in any one of claims 1 to 4.
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