CN117789056A - Remote sensing data processing method and device with solar flare and storage medium - Google Patents
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- 238000003672 processing method Methods 0.000 title claims abstract description 13
- 238000003860 storage Methods 0.000 title claims abstract description 9
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 151
- 238000002310 reflectometry Methods 0.000 claims abstract description 35
- 238000013507 mapping Methods 0.000 claims abstract description 29
- 238000000034 method Methods 0.000 claims abstract description 22
- 238000012545 processing Methods 0.000 claims abstract description 13
- 229930002875 chlorophyll Natural products 0.000 claims description 25
- 235000019804 chlorophyll Nutrition 0.000 claims description 25
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- 238000012937 correction Methods 0.000 claims description 13
- 238000007781 pre-processing Methods 0.000 claims description 6
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- 238000004364 calculation method Methods 0.000 description 4
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Abstract
A remote sensing data processing method with solar flare, a device and a storage medium belong to the technical field of remote sensing data processing, and the remote sensing data processing method comprises the following steps: acquiring original water body data in remote sensing data; dividing original water body data into flare pixels affected by flare and pure water pixels not affected by flare; acquiring flare reflection data of more than one flare pixel and pure water reflection data of corresponding pure water pixels, and constructing a first mapping relation; and correcting the flare pixels according to the first mapping relation to obtain corrected water body data. According to the method, the flare pixel affected by the flare is corrected, the remote sensing reflectivity affected by the flare does not need to be removed, and the utilization rate of data is greatly improved.
Description
Technical Field
The invention belongs to the technical field of remote sensing data processing, and particularly relates to a remote sensing data processing method and device with solar flare and a storage medium.
Background
The Sentinel-2 data is remote sensing data provided by a Sentinel-2 satellite, and is mainly used for monitoring and observing the surface of the earth. The satellite is provided with a multispectral imager (MSI), and remote sensing observation can be carried out in 13 different wave bands. These bands cover the visible, near infrared, and short wave infrared spectral ranges, with different spatial and spectral resolutions.
When remote sensing data analysis is carried out on the lake water surface, solar flare is often affected, bright spots are formed in the remote sensing image by the generated strong light spots, and the spots can cause information of the image area to be wrong or even unusable, so that analysis on the lake surface characteristics is greatly affected.
Therefore, there is a need to develop a remote sensing data processing method, device and storage medium with solar flare to solve the problems in the prior art.
Disclosure of Invention
The invention aims to provide a remote sensing data processing method and device with solar flare and a storage medium, which can correct data influenced by the solar flare in remote sensing data so as to solve the problem that the remote sensing data influenced by the solar flare, which is proposed in the background art, cannot be used.
In order to solve the technical problems, the specific technical scheme of the invention is as follows:
a remote sensing data processing method with solar flare comprises the following steps:
acquiring original water body data in remote sensing data;
dividing original water body data into flare pixels affected by flare and pure water pixels not affected by flare;
acquiring flare reflection data of more than one flare pixel and pure water reflection data of corresponding pure water pixels, and constructing a first mapping relation;
and correcting the flare pixels according to the first mapping relation to obtain corrected water body data.
Further, the flare pixel is close to the corresponding pure water pixel in position.
Further, the acquiring the original water body data in the remote sensing data comprises the following steps:
and acquiring remote sensing data and extracting original water body data.
Further, before the extracting the original water body data, the method further includes: and preprocessing the remote sensing data.
Further, the extracting the original water body data comprises the following steps:
comprehensively judging and extracting original water body data in the remote sensing data by utilizing the normalized water body index and the normalized vegetation index and combining the near infrared band remote sensing reflectivity;
the formula of the normalized water index is as follows:；
the formula of the normalized vegetation index is as follows:；
wherein, rrs (Green) is the remote sensing reflectivity of Green light wave band, rrs (NIR) is the remote sensing reflectivity of near infrared wave band, and Rrs (Red) is the remote sensing reflectivity of Red light wave band.
Further, the extracting the original water body data further includes the following steps:
when NDWI >0.8, NDVI <0.01, and Rrs (NIR) <0.11, the raw water data is judged.
Further, the original water body data is divided into flare pixels affected by flare and pure water pixels not affected by flare, and the method is carried out by the following formula:
；
wherein,for the remote sensing reflectivity of short wave infrared band, +.>Is the minimum value of the remote sensing reflectivity of the short wave infrared band.
Further, the method further comprises the following steps: acquiring chlorophyll concentration actual data of more than one actual area corresponding to the corrected water body data;
constructing a second mapping relation between the corrected water body data and the corresponding chlorophyll concentration actual data according to the corrected water body data and the corresponding chlorophyll concentration actual data;
and obtaining chlorophyll concentration inversion data corresponding to the region where the remote sensing data are located according to the corrected water body data and the second mapping relation.
A remote sensing data processing device with solar flare, comprising:
the water body data acquisition module is used for acquiring original water body data in the remote sensing data;
the water body data distinguishing module is used for distinguishing the original water body data into flare pixels affected by flare and pure water pixels not affected by flare;
the water body data correction module is used for acquiring flare reflection data of more than one flare pixel and pure water reflection data of pure water pixels corresponding to the flare pixels and constructing a first mapping relation; and correcting the flare pixel according to the first mapping relation.
A readable storage medium having stored therein computer instructions which when executed by a processor perform the steps of the remote sensing data processing method with solar flare.
According to the invention, the mapping relation between the remote sensing reflectances of each wave band of the flare pixel in the flare influence area and the remote sensing reflectances of the corresponding wave bands of the pure water pixels in the corresponding non-flare influence area is established, and the flare pixel is corrected according to the mapping relation between the remote sensing reflectances of each wave band of the two pixels, so that the remote sensing reflectances influenced by the flare are not required to be removed when the remote sensing data are used, and the utilization rate of the data is greatly increased.
Other features and advantages of the present invention will be disclosed in the following detailed description of the invention and the accompanying drawings.
Drawings
FIG. 1 is a schematic flow chart of the present invention;
FIG. 2 is a schematic view of a remote sensing image after preprocessing according to the present invention;
FIG. 3 is a schematic diagram of a remote sensing image after distinguishing flare pixels from pure water pixels;
FIG. 4 is a schematic diagram of the positions of the obtained flare pixels and the pure water pixels;
FIG. 5 is a graph showing the relationship between the flare area value and the pure water area value;
fig. 6 is a schematic diagram of water chlorophyll concentration inversion for correcting water data.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings for a better understanding of the objects, structures and functions of the present invention. In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured" and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
A remote sensing data processing method with solar flare, as shown in fig. 1 to 6, comprising the following steps:
s1: acquiring original water body data in remote sensing data;
s2: dividing the original water body data into flare pixels affected by flare and pure water pixels not affected by flare;
s3: performing solar flare correction on the flare pixels to obtain corrected water body data;
s4: and calculating chlorophyll concentration inversion data according to the corrected water body data.
The step S1 of acquiring the original water body data in the remote sensing data comprises the following steps:
s101: and acquiring remote sensing data to be processed. In this embodiment, the remote sensing data is Sentinel-2 data.
S102: and preprocessing the remote sensing data to obtain remote sensing preprocessed data. In this embodiment, the preprocessing includes radiometric calibration, image clipping, resampling or atmospheric correction, where radiometric calibration is mainly performed by using a Radiometric Calibration module in ENVI5.6, and the radiometric brightness value at the entrance pupil of the remote sensor is calculated by using an atmospheric radiation transmission model through a reflectivity method. Resampling is mainly performed by an S2 Resampling Processor module in SNAP, and a new grid value is calculated by using distance weighting from a sampling point to surrounding 4 neighborhood pixels through bilinear interpolation. Clipping is performed using the Tools in Tools of ArcMap for removing other pixels that do not need to participate in subsequent calculations.
The atmospheric correction adopts an ACOLITE tool, and atmospheric influence in a remote sensing image is simulated and eliminated through the establishment of an atmospheric optical model, so that the reflectivity corrected by Rayleigh scattering and the reflection coefficient absorbed by aerosol are finally obtained, and a more real and accurate image is obtained.
The pretreated image is shown in fig. 2, the periphery of the lake is affected by solar flare, and the middle part of the lake is not affected.
S103: and extracting the original water body data in the remote sensing pretreatment data.
The method specifically comprises the following steps:
and comprehensively judging and extracting original water body data by utilizing the normalized water body index and the normalized vegetation index and combining the near infrared band remote sensing reflectivity.
The normalized water index formula is as follows:；
the normalized vegetation index formula is as follows:；
wherein, rrs (Green) is the remote sensing reflectivity of Green light wave band, rrs (NIR) is the remote sensing reflectivity of near infrared wave band, and Rrs (Red) is the remote sensing reflectivity of Red light wave band. And calculating a normalized water body index and a normalized vegetation index, setting a threshold value by combining Rrs (NIR) near infrared band remote sensing reflectivity, performing binarization processing, and dividing remote sensing data into two types of ground objects of water body and road surface. In the actual experimental process, the positions of the real water body and the non-water body region of the remote sensing position can be firstly obtained, and the remote sensing data images are compared, so that the proper threshold value of the classification of the water body and the non-water body is selected, the data is utilized for carrying out analysis experiments, the optimal parameters are searched, and then the parameters are adjusted. For example, in this embodiment, when NDWI >0.8, NDVI <0.01, and Rrs (NIR) <0.11, then the pixel is considered to be a water pixel. Otherwise, the pixel is considered to be the other pixel. In this embodiment, the Sentinel-2 data includes 13 different bands of remote sensing data. The green light wave band remote sensing reflectivity, the near infrared wave band remote sensing reflectivity and the red light wave band remote sensing reflectivity can be directly obtained from Sentinel-2 data.
According to the method and the device, the normalized water body index, the normalized vegetation index and the near-infrared band remote sensing reflectivity threshold are combined, and compared with the method and the device which only judge through the normalized water body index and the normalized vegetation index, the method and the device are higher in accuracy and better in flexibility by combining the near-infrared band remote sensing reflectivity threshold.
Optionally, the step S1 may not perform preprocessing on the remote sensing data, and only includes: acquiring remote sensing data to be processed; and extracting the original water body data in the remote sensing data. The original water body data in the extracted remote sensing data are also as follows: and comprehensively judging and extracting original water body data by utilizing the normalized water body index and the normalized vegetation index and combining the near infrared band remote sensing reflectivity.
The original water body data in the step S2 includes a plurality of water body pixels, and the original water body data is divided into flare pixels affected by flare and pure water pixels not affected by flare, i.e., the plurality of water body pixels are divided into flare pixels affected by flare and pure water pixels not affected by flare. In this embodiment, the following shortwave infrared flare distinguishing formula is used:
；
wherein,for the remote sensing reflectivity of short wave infrared band, +.>Is the minimum value of the remote sensing reflectivity of the short wave infrared band. And the minimum value of the short-wave infrared band remote sensing reflectivity and the short-wave infrared band remote sensing reflectivity is obtained from Sentinel-2 data.
When (when)When the color is a flare pixel;
when (when)When the water is pure water, the water is used as a pure water pixel;
the value of A can be set according to actual conditions, analysis experiments can be carried out on the distinguishing results after setting, optimal distinguishing parameters are found, and then parameter tuning is carried out. Alternatively, in this embodiment, a is 0.2.
The flare distinguishing algorithm is manufactured through the short wave infrared characteristics, and accuracy is high. As shown in fig. 3, the gray part of the water body is a flare pixel, and the purple is a pure water pixel.
The step S3 includes the steps of:
s301: and acquiring flare reflection data of more than one flare pixel and pure water reflection data of pure water pixels corresponding to the flare pixels, and constructing a first mapping relation.
And the flare pixel and the pure water pixel corresponding to the flare pixel are close to or adjacent to each other in position and are close to the boundary between the flare pixel and the pure water pixel.
In this embodiment, the more than one flare pixel elements include at least one flare pixel element affected by flare light, at least one flare pixel element affected by flare moderate, and at least one flare pixel element affected by flare heavy; the flare pixels of different flare effects can be divided by the short wave infrared flare distinguishing formula.
The flare reflection data are remote sensing reflectances of a plurality of flare pixels or each wave band, the pure water reflection data are remote sensing reflectances of a plurality of pure water pixels or each wave band, and the pure water reflection data can be directly obtained from Sentinel-2 data.
As shown in fig. 4, in the present embodiment, the obtained flare pixel includes a flare region 1, a flare region 2, and a flare region 3, and pure water pixels corresponding to the flare region 1, the flare region 2, and the flare region 3 are a pure water region 1, a pure water region 2, and a pure water region 3.
Table 1 below shows remote sensing reflectances at 11 wavelengths for flare region 1, flare region 2, and flare region 3, and pure water region 1, pure water region 2, and pure water region 3. Part of the data is shown in fig. 5.
As can be seen from fig. 5, the flare area value is higher than the pure water area value and has a certain correlation.
In this embodiment, the first mapping relationship is obtained by polynomial regression.
Specifically, the first mapping relationship, namely, the solar flare correction formula is as follows:；
wherein x is the remote sensing reflectivity of the flare pixel, and z is approximately equal to the remote sensing reflectivity of the corresponding band of the corresponding pure water pixel.、/>Is->According to the calculationAnd obtaining a solution regression coefficient.
Optionally, at least one solar flare correction formula is provided, and the number of the solar flare correction formulas may be the same as the number of the wave bands.
In the present embodiment of the present invention,。
wherein x is the remote sensing reflectivity of each wave band of the flare pixel, and z is the remote sensing reflectivity after correction of each wave band.
S302: correcting the flare pixels according to a first mapping relation to obtain a plurality of corrected pixels; and obtaining corrected water body data according to the plurality of corrected pixels and the pure water pixels.
And the correction pixel is obtained after the flare pixel is substituted into the first mapping relation. In this embodiment, the correction water body data is composed of a plurality of correction pixels and pure water pixels.
According to the method, after the mapping relation between the remote sensing reflectances of the adjacent flare pixels and the pure water pixels is established, the bands of the remote sensing data are corrected band by band, and compared with the mapping relation which is not established between the adjacent pixels, the accuracy of the method is higher, and the corrected error is smaller.
Step S4 is to calculate chlorophyll concentration inversion data according to the corrected water body data, and specifically comprises the following steps:
s401: and acquiring more than one piece of corrected water body data and chlorophyll concentration actual data of an actual area corresponding to the corrected water body data. In this embodiment, the actual chlorophyll concentration data may be obtained by in-situ detection.
S402: and selecting a proper sensitive wave band, and constructing a second mapping relation of the sensitive wave band with respect to the corrected water body data and the corresponding chlorophyll concentration actual data according to the corrected water body data and the corresponding chlorophyll concentration actual data.
The second mapping relation, namely the water chlorophyll concentration inversion algorithm, is as follows:
;
wherein,、/>、/>、/>is->Obtaining according to solving polynomial regression coefficients; b2, B3, B4 and B5 are sensitive band remote sensing reflectivities. The sensitive band may be selected empirically, with more than one selected.
In this embodiment, B2 (490 nm), B3 (560 nm), B4 (665 nm) and B5 (705 nm) are used as sensitive bands, a polynomial combination mode is selected, modeling is performed according to measured data, and an inversion algorithm of chlorophyll concentration Chl of water quality is obtained:
s403: and obtaining chlorophyll concentration inversion data corresponding to the region where the remote sensing data are located according to the corrected water body data and the second mapping relation. In this embodiment, the chlorophyll concentration inversion data may be obtained by substituting the corrected water data into a water quality chlorophyll concentration inversion algorithm.
According to the method, all flare pixels are corrected band by band through the mapping relation between the remote sensing reflectances of adjacent or similar flare pixels and each band of pure water pixels, blue light, green light, red light and red edge light are selected for modeling by combining measured data and water spectrum characteristics, and a water chlorophyll concentration inversion algorithm is manufactured, and the method has good accuracy as shown in fig. 6.
And the method also comprises a step S5, wherein the step S5 is used for calculating the confidence coefficient.
The confidence coefficient calculation formula in the step S5 includes the following:
；
wherein,for confidence, j is the index number of the type pixel, n is the number of the specified type pixel, ++>Inversion value for i pel, ">The measured value of the ground of the i pixel is obtained. The index number of the type pixel and the number of the appointed type pixel are obtained from Sentinel-2 data, the inversion value of the i pixel is obtained from chlorophyll concentration inversion data, and the ground measured value of the i pixel is obtained from chlorophyll concentration actual data.
The confidence coefficient calculation formula combines remote sensing data and ground actual measurement data, fully utilizes the advantages of the remote sensing data and the ground actual measurement data, and improves the analysis accuracy. The specific pixels can be accurately positioned through the pixel index numbers, and the accuracy of inversion results is improved; the dynamic change of the pixels of a specific type can be monitored through the change of the quantity of the pixels of the specific type; by comparing the inversion value with the actual measurement value, the inversion model can be calibrated and optimized, and the generalization capability and applicability of the model are improved.
A remote sensing data processing device with solar flare, comprising:
the water body data acquisition module is used for acquiring original water body data in the remote sensing data;
the water body data distinguishing module is used for distinguishing the original water body data into flare pixels and pure water pixels;
the water body data correction module is used for acquiring flare reflection data of more than one flare pixel and pure water reflection data of pure water pixels corresponding to the flare pixels and constructing a first mapping relation; correcting the flare pixels according to a first mapping relation;
the chlorophyll concentration calculation module is used for calculating chlorophyll concentration inversion data according to the corrected water body data;
and the confidence coefficient calculating module is used for calculating the confidence coefficient of the result.
A readable storage medium having stored therein computer instructions which when executed by a processor implement the steps of the remote sensing data processing method with solar flare.
It will be understood that the invention has been described in terms of several embodiments, and that various changes and equivalents may be made to these features and embodiments by those skilled in the art without departing from the spirit and scope of the invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the invention without departing from the essential scope thereof. Therefore, it is intended that the invention not be limited to the particular embodiment disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Furthermore, it should be understood that although the present disclosure describes embodiments, not every embodiment is provided with a separate embodiment, and that this description is provided for clarity only, and that the disclosure is not limited to the embodiments described in detail below, and that the embodiments described in the examples may be combined as appropriate to form other embodiments that will be apparent to those skilled in the art.
Claims (10)
1. The remote sensing data processing method with solar flare is characterized by comprising the following steps of:
acquiring original water body data in remote sensing data;
dividing original water body data into flare pixels affected by flare and pure water pixels not affected by flare;
acquiring flare reflection data of more than one flare pixel and pure water reflection data of corresponding pure water pixels, and constructing a first mapping relation;
and correcting the flare pixels according to the first mapping relation to obtain corrected water body data.
2. The method for processing remote sensing data with solar flare according to claim 1, wherein the flare pixel is positioned close to the corresponding pure water pixel.
3. The method for processing solar flare remote sensing data according to claim 1, wherein the step of acquiring the original water body data in the remote sensing data comprises the steps of:
and acquiring remote sensing data and extracting original water body data.
4. A method of processing solar flare remote sensing data according to claim 3, further comprising, prior to extracting the raw water data: and preprocessing the remote sensing data.
5. A method of processing solar flare remote sensing data according to claim 3, wherein the extracting of the raw water body data comprises the steps of:
comprehensively judging and extracting original water body data in the remote sensing data by utilizing the normalized water body index and the normalized vegetation index and combining the near infrared band remote sensing reflectivity;
the formula of the normalized water index is as follows:
；
the formula of the normalized vegetation index is as follows:
；
wherein, rrs (Green) is the remote sensing reflectivity of Green light wave band, rrs (NIR) is the remote sensing reflectivity of near infrared wave band, and Rrs (Red) is the remote sensing reflectivity of Red light wave band.
6. The method for processing solar flare remote sensing data according to claim 5, wherein the extracting the original water body data further comprises: when NDWI >0.8, NDVI <0.01, and Rrs (NIR) <0.11, the raw water data is judged.
7. The method for processing remote sensing data with solar flare according to any one of claims 1 to 6, wherein the dividing of the original water body data into a flare pixel affected by flare and a pure water pixel not affected by flare is performed by the following formula:；
wherein,for the remote sensing reflectivity of short wave infrared band, +.>Is the minimum value of the remote sensing reflectivity of the short wave infrared band.
8. The method for processing solar flare remote sensing data according to any one of claims 1 to 6, further comprising:
acquiring chlorophyll concentration actual data of more than one actual area corresponding to the corrected water body data;
constructing a second mapping relation between the corrected water body data and the chlorophyll concentration actual data according to the corrected water body data and the chlorophyll concentration actual data;
and obtaining chlorophyll concentration inversion data according to the corrected water body data and the second mapping relation.
9. A remote sensing data processing device with solar flare, comprising:
the water body data acquisition module is used for acquiring original water body data in the remote sensing data;
the water body data distinguishing module is used for distinguishing the original water body data into flare pixels affected by flare and pure water pixels not affected by flare;
the water body data correction module is used for acquiring flare reflection data of more than one flare pixel and pure water reflection data of pure water pixels corresponding to the flare pixels and constructing a first mapping relation; and correcting the flare pixel according to the first mapping relation.
10. A readable storage medium having stored therein computer instructions, which when executed by a processor, implement the steps of the solar flare remote sensing data processing method of any one of claims 1 to 8.
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