CN117687111A - Dynamic threshold snow accumulation coverage judging method for high-resolution optical satellite - Google Patents
Dynamic threshold snow accumulation coverage judging method for high-resolution optical satellite Download PDFInfo
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Abstract
The invention discloses a dynamic threshold snow accumulation coverage judging method for a high-resolution optical satellite. Expanding pretreatment aiming at a plurality of preset high-resolution optical satellite sensors to obtain a high-resolution satellite reflectivity product; the high-resolution cloud and snow distinction is realized by fusing the product of the cloud detection section of the wind cloud meteorological satellite; based on the optical characteristics and the spatial distribution characteristics of snow, a blue wave band dynamic threshold algorithm is applied to different areas to realize self-adaptive judgment threshold setting; and further, the spatial distribution of snow coverage is obtained through a threshold extraction method, so that snow coverage monitoring is realized. The snow cover judging and identifying method for the high-spatial-resolution optical satellite provided by the invention has good expandability, the snow judging and identifying threshold value is adaptively set, the judging and identifying precision is improved, and the snow surface change can be effectively and accurately monitored in a business mode.
Description
Technical Field
The invention relates to the technical field of quantitative remote sensing inversion of a geographic information system, in particular to a dynamic threshold snow accumulation coverage judgment method for a high-resolution system optical satellite.
Background
Snow contributes to the radiant energy balance of the earth and acts as a broad aquifer, affecting various climates and hydrologic processes. Conventional ground station monitoring cannot accurately acquire a large-range monitoring result, and whether the monitoring range of the ground station is representative directly influences the final monitoring accuracy. With the development of domestic high-resolution satellite technologies, the band information of the optical sensor with high spatial resolution can provide accurate snow coverage information, and meanwhile, large-scale observation is realized.
Currently, snow cover monitoring algorithm research based on optical satellites has been approaching maturity. However, for high spatial resolution optical images, the snow discrimination algorithm still has limitations. Take GF-1/2/6/WFV optical data as an example: the current snow identification is hindered in application due to the lack of short wave infrared channel information which is most critical in snow monitoring and a multi-threshold algorithm based on snow index; secondly, because the high-resolution satellite wave bands are fewer, the cloud snow is difficult to distinguish effectively; in addition, the data size of the high-resolution optical data is large, and a technical support is required for the business accumulated snow identification for a flexible and efficient accumulated snow identification algorithm, so that improvement of the technology is required.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a dynamic threshold snow accumulation coverage judging method for a high-resolution optical satellite, which improves the snow accumulation judging capability of the high-resolution satellite.
In order to solve the technical problems, the invention is solved by the following technical scheme:
a dynamic threshold snow accumulation coverage judging method for high-resolution series optical satellites comprises the following steps:
s1, developing pretreatment for a plurality of preset high-resolution optical satellite sensors to obtain a high-resolution satellite reflectivity product;
s2, realizing high-resolution cloud and snow discrimination by fusing a product of a cloud detection section of a weather satellite;
s3, based on the optical characteristics and the spatial distribution characteristics of snow, a blue wave band dynamic threshold algorithm is applied to different areas to realize self-adaptive judgment threshold setting;
s4, further acquiring the spatial distribution of snow coverage through a threshold extraction method, and realizing snow coverage monitoring;
further, the preprocessing and quality control process of the high-resolution optical satellite data in S1 includes:
s11, radiation calibration;
s12, geographic registration;
s13, orthographic correction and atmospheric correction;
the data preprocessing and quality control method comprises the following steps: for an input GFL 1-level standardized product, geographic registration, radiometric calibration, orthographic correction and atmospheric correction are realized by means of absolute radiometric calibration coefficients, favorable polynomial coefficient equations and a 6S model in sequence.
Further, the high-resolution optical satellite data preset in the S1 includes high-resolution first-satellite wide-range camera remote sensing data, high-resolution second-satellite full-color multispectral camera remote sensing data and high-resolution sixth-satellite wide-range camera remote sensing data.
Further, in the high-resolution satellite cloud and snow distinguishing method in the step S2, the product of the cloud detection section of the wind cloud meteorological satellite in the contemporaneous or approximate transit time range is resampled, and then the multi-source remote sensing product is fused, so that the product requirement of cloud and snow distinguishing is met.
Further, the method for distinguishing the high-score satellite cloud snow in the S2 comprises the following steps:
s21, judging the space consistency of the high-resolution optical data and the weather satellite;
s22, setting the transit time difference of the two data, so as to ensure the time consistency of the used weather satellite data;
s23, carrying out geographic registration and space resampling on a product of a cloud detection section of the wind-cloud meteorological satellite based on L1-level geographic registration data of the wind-cloud meteorological satellite;
s24, realizing space registration of wind cloud data and high-score data and realizing cloud mask.
Further, in the product requirement of cloud and snow discrimination by fusing the multi-source remote sensing products, when the space range or the transit time difference between the acquired wind cloud meteorological satellite and the high-resolution satellite monitored by the system exceeds a preset value, the system considers that the wind cloud detection section product is not effective, and the cloud and snow discrimination function is not provided.
Further, the dynamic threshold snow coverage judging method for the high-resolution series optical satellite in the step S4 is characterized in that the spatial distribution of snow coverage is obtained by a threshold extraction method in the step S4, and the principle of the snow coverage judging method is shown in a formula (1):
wherein image (x, y) is the snow identification pixel value, ρ, at the image grid position (x, y) blue And t is a dynamic threshold value calculated by an algorithm for the reflectivity of the blue light wave band.
Further, the step of applying the blue band dynamic threshold algorithm to different areas in step S3 to realize adaptive judgment threshold setting includes:
s31, calculating the average value of the blue wave band of the current research sample area
S32, when the current regionGreater than 0.7, the reflectivity of the area is considered to be dominated by snow, and 0.7 is used as a recognition standard (t) of snow;
s33, whenWhen the reflectivity of the area is smaller than 0.7 and larger than 0.4, the reflectivity of the area is considered to be jointly dominant by snow and non-snow objects, and the reflectivity is in bimodal distribution at the moment;
s34, if the reflectivity does not show the bimodal distribution, thenAs a classification criterion for snow and non-snow;
s35, forIf the area is smaller than 0.4, the area is considered to be mainly dominated by non-snowfield objects, and then 0.4 is used as a snow identification threshold;
s36, realizing snow identification by applying a blue wave band dynamic threshold algorithm.
Further, in the process of realizing snow identification by applying the blue-band dynamic threshold algorithm, the system defaults to adopt the blue-band dynamic threshold method as a priority strategy of snow identification; when the algorithm boundary condition preset in the resolving process is met, replacing the accumulated snow judging strategy; the algorithm boundary conditions are: when the gradient corresponding to the pixel is larger than 35 degrees, adopting a random forest algorithm; ALOSDSM satellite digital elevation data and gradient data derived from same are embedded in system
Further, in the step S33, the valley value in the reflectance frequency distribution diagram is selected as the selected blue light threshold value, so that snow and non-snow can be effectively identified.
The beneficial effects are that: compared with the prior art, the invention has the following beneficial effects:
the dynamic threshold snow accumulation coverage judging method for the high-resolution optical satellite is suitable for domestic high-resolution optical satellite images including GF-1/6WFV and GF-2PMS images, has good expandability, simultaneously merges weather satellite cloud products, improves the cloud and snow distinguishing capability of the high-resolution satellite, and provides a practical tool for high-spatial resolution snow coverage remote sensing drawing. The dynamic threshold snow accumulation coverage judging method for the high-resolution optical satellite provides a set of complete high-resolution optical data preprocessing and quality control functions, and has high algorithm operation efficiency and strong business capability. The invention also realizes high-spatial resolution and high-precision snow coverage judgment by means of a dynamic threshold judgment method.
Drawings
FIG. 1 is a flow chart of a dynamic threshold snow coverage judging method for high-resolution series optical satellites of the present invention;
FIG. 2 is a schematic diagram of high-fraction reflectance data obtained using a data preprocessing and quality control method;
FIG. 3 is a technical roadmap of a snow identification algorithm based on a dynamic threshold method according to the present invention;
FIG. 4 is a schematic diagram of a high-score snow cover obtained by a dynamic threshold snow cover identification method for high-score series optical satellites;
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings and examples.
Referring to fig. 1, the present invention discloses a dynamic threshold snow coverage judging method for high-resolution optical satellites, which comprises the following steps: firstly, expanding pretreatment for a plurality of preset high-resolution optical satellite sensors to obtain high-resolution satellite reflectivity products, wherein the high-resolution optical satellite data pretreatment and quality control specifically comprise radiation calibration, geographic registration, orthographic correction and atmospheric correction.
Specifically: the data preprocessing and quality control method comprises the following steps: for the input GF L1 level standardized product, geographic registration, radiometric calibration, orthographic correction and atmospheric correction are realized by means of absolute radiometric calibration coefficients, an RPC model (Rational Polynomial Coefficient) and a 6S model (Second Simulation of the Satellite Signal in the Solar Spectrum) in sequence.
Referring to fig. 2 in combination, fig. 2 is a schematic diagram of high-resolution reflectivity data obtained by using a data preprocessing and quality control module.
The preset high-resolution optical satellite data comprise high-resolution first-satellite wide-range camera remote sensing data, high-resolution second-satellite full-color multispectral camera remote sensing data and high-resolution sixth-satellite wide-range camera remote sensing data, namely wide-range cameras (WFV) and high-resolution cameras (PMS) L1-level standardized products.
Secondly, the cloud and snow distinction of the high-resolution satellites is realized by fusing the product of the cloud detection section of the wind cloud meteorological satellite.
The high-resolution satellite cloud and snow distinguishing method based on the wind cloud meteorological satellite cloud detection section product is characterized in that resampling is carried out on the wind cloud meteorological satellite cloud detection section product (FY-3 CLM) in the same period or a similar transit time range to be used as cloud mask information of high-resolution optical data, and then a multi-source remote sensing product is fused, so that the product requirement of cloud and snow distinguishing is met;
when the difference of the transit time of the acquired wind cloud meteorological satellite and the high-score satellite is more than a preset value, which is monitored by the dynamic threshold snow accumulation coverage judgment method for the high-score optical satellite, the system considers that the wind cloud detection section product is not effective, and the cloud and snow distinguishing function is not provided.
Specifically, the technical flow of the high-score satellite cloud and snow distinguishing is as follows:
firstly, judging the space consistency of high-resolution optical data and wind-cloud meteorological satellites; setting the transit time difference of the two data, and further ensuring the time consistency of the used weather satellite data; expanding geographic registration and space resampling for a product of a cloud detection section of the wind cloud meteorological satellite based on L1-level geographic registration data of the wind cloud meteorological satellite; and the spatial registration of wind cloud data and high score data is realized, and a cloud mask is realized.
When the space range or the transit time difference between the acquired wind cloud meteorological satellite and the high-resolution satellite is monitored to exceed a preset value by the dynamic threshold snow accumulation coverage judging method for the high-resolution optical satellite, the method considers that the wind cloud detection section product is not effective, and the cloud and snow distinguishing function is not provided.
Based on the optical characteristics and the spatial distribution characteristics of snow, the blue wave band dynamic threshold algorithm is applied to different areas to realize self-adaptive judgment threshold setting.
And finally, acquiring the spatial distribution of the snow cover by a threshold extraction method, and realizing snow cover monitoring.
Referring to fig. 3 in combination, fig. 3 is a technical roadmap of a dynamic threshold snow coverage determination method according to the present invention. And realizing snow identification by applying a blue wave band dynamic threshold algorithm. The principle of the snow judging method is shown in a formula (1).
Wherein image (x, y) is the snow identification pixel value, ρ, at the image grid position (x, y) blue And t is a dynamic threshold value calculated by an algorithm for the reflectivity of the blue light wave band.
First, calculate the average value of blue band in the current research sample regionWhen +.>Greater than 0.7, the reflectivity of the area is considered to be dominated by snow, and 0.7 is used as a recognition standard (t) of snow; when->When the reflectivity of the area is smaller than 0.7 and larger than 0.4, the reflectivity of the area is considered to be jointly dominant by snow and non-snow objects, and the reflectivity is in bimodal distribution. At the moment, the valley value in the reflectivity frequency distribution diagram is selected as a selected blue light threshold value, so that snow and non-snow can be effectively judged; if the reflectivity does not exhibit a bimodal distribution, the reflection is +.>As a classification criterion for snow and non-snow; but for->If the area is smaller than 0.4, the area is considered to be mainly dominated by non-snowfield objects, and then 0.4 is used as a snow identification threshold;
in the process of realizing snow judging by applying a blue-band dynamic threshold algorithm, the system defaults to adopt the blue-band dynamic threshold method as a priority strategy of snow judging; when the algorithm boundary condition preset in the resolving process is met, replacing the accumulated snow judging strategy; the algorithm boundary conditions are: when the gradient corresponding to the pixel is larger than 35 degrees, adopting a random forest algorithm; ALOS DSM satellite digital elevation data and gradient data derived therefrom are embedded in the system.
Please refer to fig. 4 in combination with a schematic diagram of a high score snow coverage obtained by using a high score satellite dynamic threshold snow identification method.
In summary, the dynamic threshold snow coverage judging method for the high-resolution optical satellite is suitable for domestic high-resolution optical satellite images including GF-1/6WFV and GF-2PMS images, has good expandability, simultaneously merges weather satellite cloud products, improves the cloud and snow distinguishing capability of the high-resolution satellite, and provides a practical tool for high-spatial resolution snow coverage remote sensing drawing. The dynamic threshold snow accumulation coverage judging method for the high-resolution optical satellite provides a complete set of high-resolution optical data preprocessing and quality control method, and has high algorithm operation efficiency and strong business capability. The snow accumulation and coverage judging method for the dynamic threshold value of the high-resolution optical satellite has good expandability, adaptively sets the snow accumulation judging threshold value, improves the judging precision, and can efficiently and accurately monitor the surface change of the snow in a business mode.
The above examples are only illustrative of the preferred embodiments of the present invention and do not limit the spirit and scope of the present invention. Various modifications and improvements of the technical scheme of the present invention will fall within the protection scope of the present invention without departing from the design concept of the present invention, and the technical content of the present invention is fully described in the claims.
Claims (10)
1. A dynamic threshold snow accumulation coverage judging method for high-resolution optical satellites is characterized by comprising the following steps:
s1, developing pretreatment for a plurality of preset high-resolution optical satellite sensors to obtain a high-resolution satellite reflectivity product;
s2, realizing high-resolution satellite cloud and snow discrimination by fusing a product of a cloud detection section of the wind cloud meteorological satellite;
s3, based on the optical characteristics and the spatial distribution characteristics of snow, a blue wave band dynamic threshold algorithm is applied to different areas to realize self-adaptive judgment threshold setting;
s4, further acquiring the spatial distribution of snow coverage through a threshold extraction method, and realizing snow coverage monitoring.
2. The method for determining the dynamic threshold snow coverage for high-resolution optical satellites according to claim 1, wherein the preprocessing and quality control process of the high-resolution optical satellite data in S1 comprises:
s11, radiation calibration;
s12, geographic registration;
s13, orthographic correction and atmospheric correction;
the data preprocessing and quality control method comprises the following steps: for an input GF L1 level standardized product, geographic registration, radiometric calibration, orthographic correction and atmospheric correction are realized by means of absolute radiometric calibration coefficients, an advantageous polynomial coefficient equation and a 6S model in sequence.
3. The method for determining the snow coverage of the dynamic threshold value for the high-resolution optical satellite according to claim 2, wherein the preset high-resolution optical satellite data comprises high-resolution one-satellite wide-range camera remote sensing data, high-resolution two-satellite full-color multispectral camera remote sensing data and high-resolution six-satellite wide-range camera remote sensing data.
4. The method for determining the dynamic threshold snow accumulation coverage for the high-resolution serial optical satellite according to claim 1, wherein the method for distinguishing the high-resolution satellite cloud and snow in S2 is characterized in that the product of a cloud detection section of a wind-cloud meteorological satellite in the contemporaneous or approximate transit time range is resampled, and then a multi-source remote sensing product is fused, so that the product requirement of cloud and snow distinguishing is met.
5. The method for determining the coverage of high-score serial optical satellite-oriented dynamic threshold snow, as set forth in claim 4, wherein the method for distinguishing the high-score satellite cloud and snow in S2 comprises:
s21, judging the space consistency of the high-resolution optical data and the weather satellite;
s22, setting the transit time difference of the two data, so as to ensure the time consistency of the used weather satellite data;
s23, carrying out geographic registration and space resampling on a product of a cloud detection section of the wind-cloud meteorological satellite based on L1-level geographic registration data of the wind-cloud meteorological satellite;
s24, realizing space registration of wind cloud data and high-score data and realizing cloud mask.
6. The method for determining the coverage of snow by a dynamic threshold value for an optical satellite of high-resolution system according to claim 5, wherein the system considers that the product of the wind-cloud detection section is not effective and does not provide the cloud-snow distinguishing function when the space range or the transit time difference between the acquired wind-cloud meteorological satellite and the high-resolution satellite monitored by the system exceeds a preset value in the product requirement of cloud-snow distinguishing by fusing the multi-source remote sensing products.
7. The method for determining snow coverage of dynamic threshold for high-resolution serial optical satellite according to claim 1, wherein the principle of the method for determining snow coverage of blue band dynamic threshold is shown in formula (1):
wherein image (x, y) is the snow identification pixel value, ρ, at the image grid position (x, y) blue And t is a dynamic threshold value calculated by an algorithm for the reflectivity of the blue light wave band.
8. The method for determining the snow coverage of dynamic threshold values for high-resolution serial optical satellites according to claim 7, wherein the step of applying the blue-band dynamic threshold algorithm to different areas in S3 to achieve adaptive determination threshold value setting comprises:
s31, calculating the average value of the blue wave band of the current research sample area
S32, when the current regionGreater than 0.7, the reflectivity of the area is considered to be dominated by snow, and 0.7 is used as a recognition standard (t) of snow;
s33, whenWhen the reflectivity of the area is smaller than 0.7 and larger than 0.4, the reflectivity of the area is considered to be jointly dominant by snow and non-snow objects, and the reflectivity is in bimodal distribution at the moment;
s34, if the reflectivity does not show the bimodal distribution, thenAs a classification criterion for snow and non-snow;
s35, forIf the area is smaller than 0.4, the area is considered to be mainly dominated by non-snowfield objects, and then 0.4 is used as a snow identification threshold;
s36, realizing snow identification by applying a blue wave band dynamic threshold algorithm.
9. The method for identifying the snow coverage of the dynamic threshold value for the high-resolution serial optical satellite according to claim 7, wherein the system defaults to adopt a blue-band dynamic threshold value method as a priority strategy for identifying the snow in the process of identifying the snow by applying the blue-band dynamic threshold value algorithm; when the algorithm boundary condition preset in the resolving process is met, replacing the accumulated snow judging strategy; the algorithm boundary conditions are: when the gradient corresponding to the pixel is larger than 35 degrees, adopting a random forest algorithm; ALOSDSM satellite digital elevation data and grade data derived therefrom are embedded in the system.
10. The method for determining the coverage of snow by using dynamic threshold values for high-resolution serial optical satellites according to claim 8 wherein the valley value in the reflectivity frequency distribution map is selected as the selected blue light threshold value in S33, so that snow and non-snow can be effectively determined.
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