CN114993268A - Water depth inversion method and device combined with Catboost and storage medium - Google Patents
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Abstract
The invention discloses a water depth inversion method, a water depth inversion device and a water depth inversion storage medium which are combined with Catboost, belongs to the technical field of optical remote sensing, and comprises the following steps: acquiring multispectral remote sensing image data of a position to be detected and preprocessing the multispectral remote sensing image data; based on the preprocessed multispectral remote sensing image data, acquiring radiance data of different wave bands of a position to be detected by adopting a multi-value extraction method; inputting the radiance data into a prefabricated traditional water depth inversion model and a Catboost water depth inversion model, and respectively solving the water depth data of the position to be detected; and comparing, evaluating and analyzing the water depth data solved by the traditional water depth inversion model and the Catboost water depth inversion model, and solving the inversion value of the final water depth data. According to the invention, the traditional water depth inversion model and the Catboost water depth inversion model are combined, and water depth inversion is carried out at the same time, so that more accurate water depth data values can be obtained after comparison, evaluation and analysis.
Description
Technical Field
The invention relates to a water depth inversion method and device combined with Catboost and a storage medium, and belongs to the technical field of optical remote sensing.
Background
Accurate water depth information provides powerful support for guaranteeing the safety of ships, protecting marine ecological environment and developing and utilizing marine resources.
The traditional water depth measuring method consumes time, material resources, manpower and financial resources, is not easy to carry out in remote water areas and shallow sea areas, and has high requirements on sea surface conditions.
In recent decades, the development of remote sensing technology has been rapid, and many researchers have proposed a water depth inversion method based on remote sensing images by using the relationship between the remote sensing images and the water depth.
In the multispectral remote sensing water depth inversion, the water depth inversion model is roughly divided into a theoretical analytical model and a semi-theoretical semi-empirical model.
When the theoretical analytical model and the semi-theoretical semi-empirical model are calculated, certain real physical data (such as a plurality of water body optical parameters) need to be acquired, so that the inversion process is complicated, the generalization capability of the model is weak, and the inversion accuracy is low.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a water depth inversion method, a water depth inversion device and a water depth inversion storage medium which are combined with a Catboost, and solves the problems that a certain practical physical data needs to be acquired during calculation by adopting a theoretical analytical model and a semi-theoretical semi-empirical model, so that the inversion process is complicated, the generalization capability of the model is weak, and the inversion accuracy is low in the prior art.
In order to achieve the above object, the present invention provides a water depth inversion method combining the castboost, comprising the following steps:
acquiring multispectral remote sensing image data of a position to be detected and preprocessing the multispectral remote sensing image data;
based on the preprocessed multispectral remote sensing image data, acquiring radiance data of different wave bands of a position to be detected by adopting a multi-value extraction method;
inputting the radiance data into a pre-trained traditional water depth inversion model and a pre-trained Catboost water depth inversion model, and respectively solving water depth data of a position to be detected;
and comparing, evaluating and analyzing the water depth data solved by the traditional water depth inversion model and the Catboost water depth inversion model, and solving the inversion value of the final water depth data.
Further, training the Catboost water depth inversion model comprises the following steps:
acquiring multispectral remote sensing image data of a plurality of sample positions and preprocessing the multispectral remote sensing image data;
acquiring radiance data of different wave bands of the position of a sample point by adopting a multi-value extraction method based on the preprocessed multispectral remote sensing image data of the sample position;
acquiring an actual measurement water depth value of each sample position, and constructing a data set corresponding to the actual measurement water depth value and the radiance data;
and selecting a Catboost model, taking the radiance data in the data set as an input value, taking the actually measured water depth value in the data set as an output value, training and adjusting parameters of the Catboost model, and obtaining a Catboost water depth inversion model for inverting the water depth data.
Further, the step of obtaining the measured water depth value of each sample position includes the following steps:
and acquiring water depth data of each sample position and tide data at the imaging moment of the remote sensing image data, carrying out tide correction on the water depth data according to the tide data, and taking the corrected water depth data as an actually measured water depth value.
Further, the conventional water depth inversion model includes: log-transformed ratio models, dual-band linear regression models, and multi-band linear regression models.
Further, the pre-processing comprises:
atmospheric correction is carried out on the multispectral remote sensing image data;
resampling the multispectral remote sensing image data after atmospheric correction;
and the atmospheric correction is used for acquiring the reflectivity of each wave band of the water body.
In a second aspect, the present invention provides a water depth inversion apparatus incorporating Catboost, the apparatus comprising:
the preprocessing module is used for preprocessing the multispectral remote sensing image data;
the radiation brightness acquisition module is used for acquiring radiation brightness data of a position to be detected;
the water depth inversion module is used for solving water depth data in an inversion mode and comprises a traditional model water depth inversion module and a Catboost model water depth inversion module;
and the precision correction module is used for carrying out precision correction on the solved water depth data.
Further, the traditional model water depth inversion module comprises a logarithmic conversion ratio model water depth inversion module, a dual-band linear regression model water depth inversion module and a multi-band linear regression model water depth inversion module.
In a third aspect, the invention provides a storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method of any one of the first aspect.
The invention achieves the following beneficial effects:
compared with the traditional water depth inversion model, the water depth inversion is carried out by combining the CaBoost model, the water depth can be inverted by acquiring the multispectral remote sensing image data of the position to be detected, additional real physical data are not involved, the water depth inversion process can be effectively simplified, and the generalization capability and the inversion accuracy are improved.
Meanwhile, compared with the existing machine learning water depth inversion model, the CaBoost model solves the problems of gradient deviation and prediction deviation of the model, effectively prevents the occurrence of an over-fitting phenomenon, improves the algorithm accuracy and enhances the generalization capability effect;
according to the invention, the water depth inversion module adopts the Catboost and the traditional inversion water depth model to carry out inversion, and various models are subjected to precision comparison evaluation, so that more accurate and reliable water depth data values can be obtained.
Drawings
FIG. 1 is a flowchart of a method for water depth inversion in combination with Catboost according to an embodiment of the present invention;
FIG. 2 is a distribution of positions of a selected region to be measured and a sample in a water depth inversion method combined with Catboost according to an embodiment of the present invention;
FIG. 3 is a comparison diagram of the accuracy evaluation indexes of the models;
FIG. 4 is a scatter plot of inversion results of a log transformed model versus actual water depth values;
FIG. 5 is a scatter plot of the inversion results of a two-band linear regression model versus actual water depth values;
FIG. 6 is a scatter plot of inversion results of a multi-band linear regression model versus actual water depth values;
FIG. 7 is a scatter plot of inversion results of a Catboost water depth inversion model versus actual water depth values;
FIG. 8 is a residual normal distribution plot of the inversion results of the log-transformed ratio model;
FIG. 9 is a residual normal distribution plot of the inversion results of the dual band linear regression model;
FIG. 10 is a residual normal distribution plot of the inversion results of the multiband linear regression model;
FIG. 11 is a residual normal distribution diagram of inversion results of the Catboost water depth inversion model.
Detailed Description
The invention is further described below on the basis of the drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
The first embodiment is as follows:
the embodiment of the invention provides a water depth inversion method combined with Catboost, which is characterized in that the water depth data of a position to be detected is solved by inversion and subjected to precision comparison evaluation on the basis of the traditional water depth inversion method, so that more accurate water depth data values can be obtained, the problems of low inversion precision and weak model generalization capability can be effectively solved, and meanwhile, compared with the traditional water depth inversion model, the water depth inversion model of the Catboost solves the problems of gradient deviation and prediction deviation of the model, the generation of overfitting phenomenon can be effectively prevented, the algorithm accuracy can be effectively improved, and the generalization capability can be enhanced.
The embodiment of the invention provides a water depth inversion method combined with a Catboost model, which comprises the following steps as shown in figures 1 to 2:
the method comprises the following steps: acquiring multispectral remote sensing image data of a position to be detected and preprocessing the multispectral remote sensing image data:
in the embodiment of the invention, an area near the Wasabia is selected as an area to be measured to carry out inversion of water depth data, and multispectral remote sensing image data of a research area of the Wasabia is obtained through Sentinel-2;
atmospheric correction and resampling are carried out on collected multispectral remote sensing image data of the region to be detected: remote sensing data of a region to be measured is downloaded from an European Space Agency (ESA) to multi-spectral remote sensing image data of a sentinel level 2L 1C, the product is an atmospheric apparent reflectivity product which is only subjected to orthorectification and geometric fine rectification, and the L2A level data needs to be produced automatically. In the embodiment of the invention, a Sen2cor plug-in unit issued by the European Space Agency (ESA) is adopted to carry out atmospheric correction on L1C product data to obtain the real reflectivity of each wave band of a water body. In an embodiment of the present invention, because there are three different resolutions between the bands of Sentinel-2, the SNAP software is used to resample all bands to 10 m.
Step two: based on the preprocessed multispectral remote sensing image data, acquiring radiance data of different wave bands of a position to be detected by adopting a multi-value extraction method:
in the embodiment of the invention, based on preprocessed multispectral remote sensing image data, a multi-value extraction endpoint method is utilized in ArcGIS, and longitude and latitude coordinates are used for positioning to acquire the radiance data of each wave band of a position to be detected;
step three: and inputting the radiance data into a prefabricated traditional water depth inversion model and a Catboost water depth inversion model, and solving the water depth data of the position to be measured respectively.
Step four: and comparing, evaluating and analyzing the water depth data solved by the traditional water depth inversion model and the Catboost water depth inversion model, and solving the inversion value of the final water depth data.
In the third step of the invention, prefabricating a Catboost water depth inversion model comprises the following steps:
selecting sample positions in a research area of the Wa lake island, selecting the sample positions in a shallow water range of 0-20m in the embodiment of the invention, selecting 2000 sample positions for modeling, acquiring multispectral remote sensing image data of each sample position and preprocessing the multispectral remote sensing image data;
the process of preprocessing each sample location includes: atmospheric correction and resampling are carried out on the collected multispectral remote sensing image data of each sample position: remote sensing data of each sample position is downloaded from the European Space Agency (ESA) to the sentinel 2 # L1C level multispectral remote sensing image data, the product is an atmospheric apparent reflectivity product which is only subjected to orthorectification and geometric precise rectification, and L2A level data need to be produced by self. In the embodiment of the invention, a Sen2cor plug-in unit issued by the European Space Agency (ESA) is adopted to carry out atmospheric correction on L1C product data to obtain the real reflectivity of each wave band of a water body. In the embodiment of the invention, because three different resolutions exist among the bands of the Sentinel-2, the SNAP software is used for resampling all the bands to 10 m;
after preprocessing the multispectral remote sensing image of each sample position, positioning by longitude and latitude coordinates by using a multi-value extraction to point method in ArcGIS, and acquiring radiance data of each wave band of each sample position, wherein in the embodiment of the invention, the radiance values of 12 wave bands of each sample position are acquired;
acquiring actual measurement water depth data of each sample position, and constructing a corresponding data set based on the actual measurement water depth data of each sample position and the radiation brightness data of each sample position;
selecting a Catboost model, taking radiance data in a data set as input values, recording radiance values of 12 wave bands of each sample position as B1, B2, B3, B4, B5, B6, B7, B8, B8A, B9, B11 and B12 as input values, taking actual measurement water depth values in the data set as output values, training the Catboost model under default parameters, traversing a given parameter range by using a GridSearchCV function, and finding out parameters enabling the model to have the highest precision, wherein the parameter selection combination is as follows: the model is trained by using the group of parameters, and the obtained model is a Catboost water depth inversion model;
the method for acquiring the actually measured water depth value of each sample position comprises the following steps:
and acquiring water depth data of each sample position and tide data of the remote sensing image data imaging moment of each sample position, carrying out tide correction on the water depth value according to the tide data, and taking the corrected water depth value as an actually measured water depth value.
In the third step of the invention, prefabricating the traditional water depth inversion model comprises the following steps:
firstly, the traditional water depth inversion model comprises the following steps: a log-conversion ratio model, a dual-band linear regression model, and a multi-band linear regression model;
the prefabricated logarithm conversion ratio model comprises:
and (3) using SPSS software, and carrying out nonlinear fitting by introducing a blue-green waveband radiation brightness value and an actually measured water depth value and utilizing an L-M algorithm to obtain unknown parameters. The model formula is as follows:
wherein Z is water depth data obtained by inversion, m 1 、m 0 Is a regression coefficient; b (lambda) i )、R(λ j ) The reflectivity of blue and green wave bands respectively; n is a fixed coefficient of blue and green wave bands, and the blue and green wave bands are adopted because visible light (such as blue light and green light) with shorter wavelength has stronger penetrating power to a water body, so that the underwater topography condition can be reflected.
The prefabricated dual band linear regression model comprises:
presetting reflectivity ratios of 2 wave bands on different substrate types to be unchanged, establishing a dual-wave band linear regression model, using a linear fitting function in SPSS software, taking the radiation brightness value of a blue-green wave band of a modeling point as an independent variable, taking water depth data as a dependent variable, and using the linear fitting function to iterate to obtain unknown parameters of the model, wherein the model formula is as follows:
wherein Z is water depth data obtained by inversion, L (lambda) n ) Receiving λ for the sensor n Radiation value of band, n is band number, L s (λ n ) Is λ n Radiation value of band deepwater zone, wherein 1 、λ 2 The wave bands are blue wave band and green wave band. Regression analysis calculation is carried out through part of actually measured water depth data, pixel values of the images and the selected average pixel value of the deep water area to obtain experience parameters a and b corresponding to a single wave band and multiple wave bands, and then the experience parameters are applied to other water body areas to invert the water depth.
The pre-formed multiband linear regression model comprises:
by utilizing a linear fitting function in SPSS software, the radiation brightness values of 12 wave bands of each sample position used for modeling are used as independent variables, the actually measured water depth value is used as a dependent variable, iteration is carried out by utilizing the linear fitting function, and unknown parameters of the model can be obtained, wherein the model formula is as follows:
wherein Z is water depth data obtained by inversion, a 1 、a 2 …a N B is a model coefficient; i is 1, … N, N is the number of wave bands participating in calculation; l (lambda) i ) Receiving λ for the sensor i Radiation values of the bands; l is s (λ i ) Is λ i The radiation value of the wave band deep water area.
Example two:
the second embodiment of the invention provides a water depth inversion device combined with Catboost, which comprises:
the preprocessing module is used for preprocessing the multispectral remote sensing image data;
the radiation brightness acquisition module is used for acquiring radiation brightness data of a position to be detected;
the water depth inversion module is used for inverting and solving water depth data and comprises a traditional model water depth inversion module and a Catboost model water depth inversion module;
and the precision correction module is used for carrying out precision correction on the solved water depth data.
The traditional model water depth inversion module comprises a logarithmic conversion ratio model water depth inversion module, a dual-waveband linear regression model water depth inversion module and a multiband linear regression model water depth inversion module.
Example three:
a third embodiment of the present invention provides a storage medium, and the program implements the steps of the method according to any one of the embodiments when executed by a processor.
In order to verify the accuracy of the Catboost water depth inversion model in the invention, as shown in fig. 3 to 11, the Catboost water depth inversion model and the traditional water depth inversion model are compared and analyzed, the accuracy evaluation is carried out by using a decision coefficient (R ^2), a Root Mean Square Error (RMSE), a Mean Absolute Error (MAE) and a Mean Relative Error (MRE) as accuracy evaluation indexes, fig. 3 is the accuracy evaluation result of the Catboost water depth inversion model and the traditional water depth inversion model, a distribution scatter diagram of the water depth inversion values obtained by the models shown in fig. 4 and fig. 7 and the actually measured water depth values obtained by actual measurement is drawn, and a residual distribution histogram shown in fig. 8 to fig. 11 is drawn.
As shown in fig. 3 to 11, compared with the existing water depth inversion method, the water depth inversion method using the castboost water depth inversion model can effectively solve the problems of poor fitting effect and low precision of the traditional water depth inversion model, and the castboost water depth inversion model has significant effect and excellent advantages for solving the problem of high nonlinearity between water depth data and radiation brightness value in water depth inversion; therefore, the traditional water depth inversion model and the Catboost water depth inversion model are combined, water depth inversion is carried out simultaneously, more accurate water depth data values can be obtained after comparison, evaluation and analysis, and meanwhile, the reliability of inversion results can be effectively improved by adopting a mode of combining various inversion methods to carry out water depth inversion.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (8)
1. A water depth inversion method combined with Catboost is characterized in that: the method comprises the following steps:
acquiring multispectral remote sensing image data of a position to be detected and preprocessing the multispectral remote sensing image data;
based on the preprocessed multispectral remote sensing image data, acquiring radiance data of different wave bands of a position to be detected by adopting a multi-value extraction method;
inputting the radiance data into a pre-trained traditional water depth inversion model and a pre-trained Catboost water depth inversion model, and respectively solving water depth data of a position to be detected;
and comparing, evaluating and analyzing the water depth data solved by the traditional water depth inversion model and the Catboost water depth inversion model, and solving the inversion value of the final water depth data.
2. The water depth inversion method combining Catboost as claimed in claim 1, wherein: the method for training the water depth inversion model of the Catboost comprises the following steps:
acquiring multispectral remote sensing image data of a plurality of sample positions and preprocessing the multispectral remote sensing image data;
acquiring radiance data of different wave bands of the position of a sample point by adopting a multi-value extraction method based on the preprocessed multispectral remote sensing image data of the sample position;
acquiring an actual measurement water depth value of each sample position, and constructing a data set corresponding to the actual measurement water depth value and the radiance data;
and selecting a Catboost model, taking the radiance data in the data set as an input value, taking the actually measured water depth value in the data set as an output value, training and adjusting parameters of the Catboost model, and obtaining a Catboost water depth inversion model for inverting the water depth data.
3. The water depth inversion method combining Catboost as claimed in claim 2, wherein: the method for acquiring the actually measured water depth value of each sample position comprises the following steps:
and acquiring water depth data of each sample position and tide data at the imaging moment of the remote sensing image data, carrying out tide correction on the water depth data according to the tide data, and taking the corrected water depth data as an actually measured water depth value.
4. The water depth inversion method combining Catboost as claimed in claim 1, wherein: the conventional water depth inversion model comprises: log-transformed ratio models, dual-band linear regression models, and multi-band linear regression models.
5. The water depth inversion method combining Catboost as claimed in claim 2, wherein: the pretreatment comprises the following steps:
atmospheric correction is carried out on the multispectral remote sensing image data;
resampling the multispectral remote sensing image data after atmospheric correction;
the atmospheric correction is used for acquiring the reflectivity of each wave band of the water body.
6. A water depth inversion device combined with Catboost is characterized in that: the device comprises:
the preprocessing module is used for preprocessing the multispectral remote sensing image data;
the radiation brightness acquisition module is used for acquiring radiation brightness data of a position to be detected;
the water depth inversion module is used for solving water depth data in an inversion mode and comprises a traditional model water depth inversion module and a Catboost model water depth inversion module;
and the precision correction module is used for carrying out precision correction on the solved water depth data.
7. The water depth inversion device combined with Catboost according to claim 6, wherein: the traditional model water depth inversion module comprises a logarithmic conversion ratio model water depth inversion module, a dual-waveband linear regression model water depth inversion module and a multiband linear regression model water depth inversion module.
8. A storage medium on which a computer program is stored which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 5.
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|---|---|---|---|---|
| CN117274831A (en) * | 2023-09-04 | 2023-12-22 | 大连海事大学 | Offshore turbid water body depth inversion method based on machine learning and hyperspectral satellite remote sensing image |
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