CN117392433A - Fine recognition method for different freshwater resource types by combining SAR (synthetic aperture radar) and optical image - Google Patents
Fine recognition method for different freshwater resource types by combining SAR (synthetic aperture radar) and optical image Download PDFInfo
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Abstract
The invention relates to a method for finely identifying different freshwater resource types by combining SAR and optical images, which comprises the following steps: acquiring a time sequence SAR image and a time sequence optical image and corresponding synthesized images; constructing a normalized flooding index NDHF; performing multi-scale segmentation on the synthesized image to generate a homogeneous unit object of the image; identifying the minimum range of fresh water resources by adopting a threshold method; constructing a decision tree algorithm based on spectral characteristics to identify a flooding area; and constructing a decision tree algorithm based on geometric shape characteristics to identify rivers, lakes, ponds and culture ponds. The beneficial effects of the invention are as follows: the invention comprehensively uses SAR backscattering characteristics, optical spectrum characteristics and geometric shape characteristics to construct different decision tree distinguishing algorithms so as to obtain the careful distinction of different fresh water resource types and the spatial range of different fresh water resource type distribution, thereby being beneficial to timely and accurately grasping the distribution and change profile of the existing fresh water resource and realizing reasonable distribution and scientific scheduling of the fresh water resource.
Description
Technical Field
The invention relates to the technical field of remote sensing image processing, in particular to a method for finely identifying different freshwater resource types by combining SAR and optical images.
Background
The fresh water ecological system has the functions of regulating climate, conserving water sources, protecting fresh water biodiversity and the like, and is a foundation for stable production and harvest of agriculture and industrial development. With the increasing prominence of global water resource shortage, fine identification and management of fresh water resources has become particularly important. The freshwater ecosystem can be divided into subsystems such as lake ecosystem, pond ecosystem (pond and culture pond), river ecosystem (river and flooding area) and the like according to the water surface type.
The optical remote sensing imaging range is large, the advantages of high spatial resolution and abundant spectral information are achieved, the observation cost is low, the short-time repeated observation can be realized, and the optical remote sensing imaging method is widely applied to aspects of fresh water resource investigation and monitoring. The high spatial resolution and spectral characteristics of multispectral remote sensing have strong recognition capability on the microcosmic aspect, but multispectral remote sensing is finally influenced by weather, sunlight and the like, and all-weather imaging can not be realized all the day. The synthetic aperture radar SAR can penetrate through cloud layers to reflect the backward scattering information of ground objects, and can make up for the defect that optical remote sensing cannot observe all the time and all the weather in monitoring. In addition, the SAR satellite images are all formed by side view imaging, so that the shape and structure characteristics of the ground object can be reflected well, and the ground object information identification is facilitated. Therefore, the single-source remote sensing data can not solve the problem of fine recognition of fresh water resources.
Disclosure of Invention
The invention aims at overcoming the defects of the prior art, and provides a method for finely identifying different freshwater resource types by combining SAR and optical images.
In a first aspect, a method for finely identifying different freshwater resource types by combining SAR and optical images is provided, comprising:
step 1, acquiring a sequential SAR image and a sequential optical image, preprocessing the sequential SAR image and the sequential optical image, generating a corresponding median, maximum and minimum synthesized image for the preprocessed sequential SAR image, and generating a corresponding median synthesized image for the preprocessed sequential optical image;
step 2, synthesizing an image according to the maximum value and the minimum value of the time sequence SAR image, selecting fresh water and non-fresh water type sample points, acquiring SAR backward scattering coefficient data, and constructing a normalized flooding index NDHF;
step 3, multi-scale segmentation is carried out on the synthesized image, and a homogeneous unit object of the image is generated;
step 4, calculating spectral characteristics of the homogeneous unit object, combining gradient parameters of digital elevation model DEM data, and identifying a minimum range of fresh water resources by adopting a threshold method;
step 5, synthesizing images by relying on the maximum value and the minimum value of SAR, calculating the flooding index NDHF of each homogeneous unit object, and constructing a decision tree algorithm based on spectral characteristics to identify a flooding area;
step 6, merging homogeneous unit objects, calculating the geometric shape characteristics of each homogeneous unit object, and constructing a decision tree algorithm based on the geometric shape characteristics to identify rivers, lakes, ponds and culture ponds;
and 7, converting the identified fresh water raster data into vector data to obtain identification results of different fresh water resource types.
Preferably, in step 1, the sequential SAR image is a Sentinel-1 satellite GRD image, the band is a C band, the polarization mode is VH polarization, and the preprocessing of the sequential SAR image includes: track correction, thermal noise removal, radiometric calibration, doppler topography correction, lee filtering and decibelization; the time sequence optical image is a Sentinel-2 satellite MSI image, the wave bands are red, green, blue and near infrared, and the preprocessing of the time sequence optical image comprises the following steps: radiometric calibration, atmospheric correction, band superposition, mosaic and clipping.
Preferably, in step 2, the calculation formula of the normalized flooding index NDHF is:
wherein VH min Is the VH backscattering coefficient minimum value of the SAR minimum value synthesized image, VH max Is the VH backscatter coefficient maximum value of the SAR maximum value synthesized image.
Preferably, in step 3, the parameters of the multi-scale segmentation include: scale, shape, compactness, and the weight of the composite image.
Preferably, step 4 includes:
step 4.1, calculating the gradient by using digital elevation model DEM data, wherein a gradient calculation formula is as follows:
slope=arctan (elevation difference/horizontal distance)
Wherein Slope represents a gradient, arctan represents an arctangent function;
step 4.2, calculating a normalized water index NDWI, wherein the formula is as follows:
wherein P is green Representing the reflectivity of the green band, P nir Indicating the reflectivity of the near infrared band;
and 4.3, calculating relevant parameters of the fresh water and non-fresh water type sample points, determining an optimal threshold value, and identifying all fresh water.
Preferably, step 4.3 comprises:
step 4.3.1, calculating pixel values of VH corresponding pixels in a median synthetic image of the SAR of fresh water and non-fresh water type sample points, manufacturing a box diagram, and determining a threshold B1 by the average value of the lower quartile of the non-fresh water resource and the upper quartile of the fresh water resource;
step 4.3.2, calculating gradients of fresh water and non-fresh water type sample points, drawing a histogram, and determining a gradient threshold value as B2;
step 4.3.3, calculating pixel values of pixels corresponding to the normalized water index NDWI of the fresh water and non-fresh water type sample points in the optical median synthetic image, manufacturing a box diagram, and determining a threshold B3 by the average value of the lower quartile of the fresh water resource and the upper quartile of the non-fresh water resource;
and 4.3.4, calculating pixel values of pixels corresponding to near infrared wave bands of fresh water and non-fresh water type sample points in an optical median synthetic image, manufacturing a box diagram, determining a threshold B4 by the average value of the upper quartile of fresh water resources and the lower quartile of non-fresh water resources, and identifying all fresh water.
Preferably, in step 5, according to the decision tree algorithm model of the spectral feature, the decision tree algorithm model has a flooding index threshold B5 and an annual VH backscattering coefficient maximum threshold B6, and the determining manner of the flooding index threshold B5 is as follows:
calculating flooding indexes of a flooding area and other fresh water sample points, and making a box diagram, wherein the average value of the lower quartile of the flooding indexes of the flooding sample points and the upper quartile of the flooding indexes of other fresh water is a threshold B5 for distinguishing flooding from other fresh water resource types; the VH backscattering coefficient maximum value threshold B6 is determined in the following manner: calculating the maximum value of the VH backscattering coefficient of the sample points of the flooding area and the non-flooding area, and making a box diagram, and flooding the VH of the sample points max Upper quartile of (v) and VH of non-flooded sample points max The mean of the lower quartile of (c) is a threshold B6 that determines the flooding zone and other freshwater resource types.
Preferably, step 6 includes:
step 6.1, merging the homogeneous unit objects, and performing post-classification treatment;
step 6.2, calculating the rectangular fitting degree, the ellipse fitting degree and the area of each homogeneous unit object;
step 6.3, according to a decision tree algorithm model of geometric shape characteristics, determining a threshold value F1 of rectangular fitting degree and a threshold value F2 of ellipse fitting degree, wherein the determination mode of the threshold value F1 of rectangular fitting degree and the threshold value F2 of ellipse fitting degree is as follows:
step 6.3.1, calculating rectangular fitting degree and ellipse fitting degree of sample points of rivers, lakes, reservoirs, floods, ponds and culture ponds, and making a scatter diagram;
step 6.3.2, determining that the average value of the maximum value of the rectangular fitting degree of the river sample points and the minimum value of the rectangular fitting degree of the sample points of the lakes, the ponds and the culture ponds is a threshold value F1 of the rectangular fitting degree;
and 6.3.3, determining the average value of the maximum value of the ellipse fitting degree of the river sample points and the minimum value of the ellipse fitting degree of the sample points of the lakes, the ponds and the culture ponds as a threshold value F2 of the ellipse fitting degree.
In a second aspect, a system for finely identifying different freshwater resource types of a joint SAR and an optical image is provided, and the method for finely identifying different freshwater resource types of the joint SAR and the optical image in the first aspect is performed, and includes:
the acquisition module is used for acquiring the time sequence SAR image and the time sequence optical image, preprocessing the time sequence SAR image and the time sequence optical image, generating corresponding median, maximum and minimum synthesized images for the preprocessed time sequence SAR image, and generating corresponding median synthesized images for the preprocessed time sequence optical image;
the construction module is used for synthesizing the images according to the maximum value and the minimum value of the time sequence SAR image, acquiring SAR backward scattering coefficient data and constructing a normalized flooding index NDHF;
the generation module is used for carrying out multi-scale segmentation on the synthesized image to generate a homogeneous unit object of the image;
the first calculation module is used for calculating the spectral characteristics of the homogeneous unit object, combining gradient parameters of Digital Elevation Model (DEM) data, and identifying the minimum range of fresh water resources by adopting a threshold method;
the second calculation module is used for synthesizing images by relying on the maximum value and the minimum value of SAR, calculating the flooding index NDHF of each homogeneous unit object, and constructing a decision tree algorithm based on spectral characteristics to identify a flooding area;
the third calculation module is used for merging the homogeneous unit objects, calculating the geometric shape characteristics of each homogeneous unit object, and constructing a decision tree algorithm based on the geometric shape characteristics to identify rivers, lakes, ponds and culture ponds;
and the conversion module is used for converting the identified fresh water raster data into vector data to obtain identification results of different fresh water resource types.
In a third aspect, a computer storage medium having a computer program stored therein is provided; the computer program when run on a computer causes the computer to execute the method for finely identifying different freshwater resource types of the combined SAR and optical image according to any one of the first aspect.
The beneficial effects of the invention are as follows: based on the time sequence SAR and the optical image, the SAR backscattering characteristics, the optical spectrum characteristics and the geometric shape characteristics of different fresh water resources are extracted, and the decision tree algorithm for distinguishing different fresh water resource types is constructed by comprehensively using the SAR backscattering characteristics, the optical spectrum characteristics and the geometric shape characteristics, so that the fine distinguishing of different fresh water resource types and the spatial range of different fresh water resource type distribution are obtained, the current fresh water resource distribution and change profile can be mastered timely and accurately, and reasonable distribution and scientific scheduling of fresh water resources are realized. The method has a certain reference significance for the fine and automatic extraction of the fresh water in a large-scale or terrain complex area.
Drawings
FIG. 1 is a flow chart of a method for fine recognition of different freshwater resource types by combining SAR and optical images;
FIG. 2 is a graph of a local effect of multi-scale segmentation of a joint SAR and optical image;
FIG. 3 is a graph showing normalized water index (NDWI), near infrared band (NIR) reflectivity, VH backscattering coefficient median values for sample points of freshwater resource types and non-freshwater resource types;
FIG. 4 is a diagram of normalized flooding index (NDHF) of a flooding domain and other fresh water resources;
FIG. 5 is a schematic diagram of a decision tree model;
FIG. 6 is a diagram showing the result of fresh water identification;
fig. 7 is a schematic diagram of extraction of different types of fresh water resources.
Detailed Description
The invention is further described below with reference to examples. The following examples are presented only to aid in the understanding of the invention. It should be noted that it will be apparent to those skilled in the art that modifications can be made to the present invention without departing from the principles of the invention, and such modifications and adaptations are intended to be within the scope of the invention as defined in the following claims.
Example 1:
complementary information of different data sources in the same area can be enriched by the multi-source remote sensing image (multispectral and SAR), so that uncertainty of a single data source is reduced, ambiguity is reduced, and accordingly complete and consistent information description of a target is formed. In addition, unlike conventional freshwater resource identification and extraction, the essence of fine classification requires that the remote sensing technology not only realize the fine distinction of different freshwater resource types, but also can accurately judge the spatial range of the distribution of different freshwater resource types. The multisource remote sensing image has the information expression capability of providing more complete fresh water resources, and has the advantage of more information compared with the single-source remote sensing image to realize the fine monitoring of the fresh water resources.
The embodiment of the application provides a method for finely identifying different freshwater resource types by combining SAR and optical images, which is shown in figure 1 and comprises the following steps:
step 1, acquiring a time sequence SAR image and a time sequence optical image, preprocessing the time sequence SAR image and the time sequence optical image, generating corresponding median, maximum and minimum synthesized images for the preprocessed time sequence SAR image, and generating corresponding median synthesized images for the preprocessed time sequence optical image.
Specifically, the sequential SAR image is a Sentinel-1 satellite GRD image, the wave band is a C wave band, the polarization mode is VH polarization, and the preprocessing of the sequential SAR image comprises the following steps: track correction, thermal noise removal, radiometric calibration, doppler topography correction, lee filtering and decibelization; the time sequence optical image is a Sentinel-2 satellite MSI image, the wave bands are red, green, blue and near infrared, and the preprocessing of the time sequence optical image comprises the following steps: radiometric calibration, atmospheric correction, band superposition, mosaic and clipping.
And 2, synthesizing images according to the maximum value and the minimum value of the time sequence SAR image, selecting fresh water and non-fresh water type sample points, acquiring SAR backward scattering coefficient data, and constructing a normalized flooding index NDHF.
Specifically, depending on minimum and maximum SAR images, selecting flood areas, rivers, lakes, ponds and culture pond sample points according to google earth high-resolution images and actual investigation, acquiring SAR backward scattering coefficient data, and constructing a normalized flooding index NDHF, wherein the NDHF calculation formula is as follows:
wherein VH min Is the VH backscattering coefficient minimum value of the SAR minimum value synthesized image, VH max Is the VH backscatter coefficient maximum value of the SAR maximum value synthesized image.
And 3, performing multi-scale segmentation on the composite image to generate a homogeneous unit object of the image.
Specifically, the parameters of the multi-scale segmentation include: scale, shape, compactness, and weight of the composite image. For example, the parameter scale, shape, compactness are 5, 0.1, 0.8, respectively, and the median composite image, maximum composite image, minimum composite image, and optical median composite image weights are 1, respectively.
And 4, calculating spectral characteristics of the homogeneous unit object, combining gradient parameters of digital elevation model DEM data, and identifying the minimum range of the fresh water resource by adopting a threshold method.
And 5, synthesizing images by relying on the maximum value and the minimum value of SAR, calculating the flooding index NDHF of each homogeneous unit object, and constructing a decision tree algorithm based on spectral characteristics to identify a flooding area.
And 6, merging the homogeneous unit objects, calculating the geometric shape characteristics of each homogeneous unit object, and constructing a decision tree algorithm based on the geometric shape characteristics to identify rivers, lakes, ponds and culture ponds.
And 7, converting the identified fresh water raster data into vector data to obtain identification results of different fresh water resource types.
Example 2:
based on embodiment 1, embodiment 2 of the present application provides a more specific method for finely identifying different freshwater resource types by combining SAR and optical images, which includes:
step 1, acquiring a time sequence SAR image and a time sequence optical image, preprocessing the time sequence SAR image and the time sequence optical image, generating corresponding median, maximum and minimum synthesized images for the preprocessed time sequence SAR image, and generating corresponding median synthesized images for the preprocessed time sequence optical image.
And 2, synthesizing images according to the maximum value and the minimum value of the time sequence SAR image, selecting fresh water and non-fresh water type sample points, acquiring SAR backward scattering coefficient data, and constructing a normalized flooding index NDHF.
Step 3, as shown in fig. 2, the composite image is subjected to multi-scale segmentation to generate a homogeneous unit object of the image.
And 4, calculating spectral characteristics of the homogeneous unit object, combining gradient parameters of digital elevation model DEM data, and identifying the minimum range of the fresh water resource by adopting a threshold method.
Specifically, step 4 includes:
step 4.1, calculating the gradient by using Digital Elevation Model (DEM) data, wherein the Digital Elevation Model (DEM) data are ALOS-12.5 m DEM data, and a gradient calculation formula is as follows:
slope=arctan (elevation difference/horizontal distance)
Wherein Slope represents a gradient, arctan represents an arctangent function;
step 4.2, spectral features including VH backscattering coefficient, near infrared band (NIR) reflectivity and normalized water index (Normalized Difference Water Index, NDWI), the calculation formula of the normalized water index NDWI is:
wherein P is green Representing the reflectivity of the green band, P nir Indicating the reflectivity of the near infrared band;
and 4.3, selecting fresh water and non-fresh water type sample points, calculating related parameters, determining an optimal threshold value, and identifying all fresh water.
Step 4.3 comprises:
step 4.3.1, selecting fresh water and non-fresh water type sample points, calculating pixel values of VH corresponding pixels in a median synthetic image of SAR of the fresh water and non-fresh water type sample points, manufacturing a box diagram, and determining a threshold B1 by the average value of the lower quartile of non-fresh water resources and the upper quartile of fresh water resources;
step 4.3.2, calculating gradients of fresh water and non-fresh water type sample points, drawing a histogram, and determining a gradient threshold value as B2;
step 4.3.3, calculating pixel values of pixels corresponding to the normalized water index NDWI of the fresh water and non-fresh water type sample points in the optical median synthetic image, manufacturing a box diagram, and determining a threshold B3 by the average value of the lower quartile of the fresh water resource and the upper quartile of the non-fresh water resource;
and 4.3.4, calculating pixel values of pixels corresponding to near infrared wave bands of fresh water and non-fresh water type sample points in an optical median synthetic image, manufacturing a box diagram, determining a threshold B4 by the average value of the upper quartile of fresh water resources and the lower quartile of non-fresh water resources, and identifying all fresh water.
And 5, as shown in fig. 4, synthesizing an image by relying on the maximum value and the minimum value of SAR, calculating the flooding index NDHF of each homogeneous unit object, and constructing a decision tree algorithm of spectral characteristics to identify a flooding area.
In step 5, according to the decision tree algorithm model of the spectral feature, the method has a flooding index threshold B5 and an annual VH backscattering coefficient maximum threshold B6, and the determining manner of the flooding index threshold B5 is as follows:
calculating flooding indexes of a flooding area and other fresh water sample points, and making a box diagram, wherein the average value of the lower quartile of the flooding indexes of the flooding sample points and the upper quartile of the flooding indexes of other fresh water is an optimal threshold B5 for distinguishing flooding from other fresh water resource types;
the VH backscattering coefficient maximum value threshold B6 is determined in the following manner:
calculating the maximum value of the VH backscattering coefficient of the sample points of the flooding area and the non-flooding area, and making a box diagram, and flooding the VH of the sample points max Upper quartile of (v) and VH of non-flooded sample points max Lower quartile of (2)The mean value of (2) is a threshold B6 that determines the flooding zone and other freshwater resource types.
And 6, combining the homogeneous unit objects, calculating the geometric shape characteristics of each homogeneous unit object, and constructing a decision tree algorithm based on the geometric shape characteristics to identify rivers, lakes, ponds and culture ponds as shown in fig. 5.
The step 6 comprises the following steps:
and 6.1, merging the homogeneous unit objects, and performing post-classification treatment.
And 6.2, calculating the rectangular fitting degree, the ellipse fitting degree and the area of each homogeneous unit object.
The rectangular fitting feature and the ellipse fitting feature are specifically defined as follows:
rectangular fitting characteristics: the degree of matching of image objects to a cuboid of similar size and scale is described. 0 indicates no match and 1 indicates a perfect match. Is based on a cuboid of the same volume as the image object under consideration. The ratio of the rectangular parallelepiped is equal to the ratio of the length, width, and thickness of the image object. The volume of the image object outside the rectangle is compared with the volume inside the cuboid without the image object filled.
Ellipse fitting features describe how well an image object matches an ellipsoid of similar size and scale. 0 indicates no match and 1 indicates a perfect match. Is based on ellipsoids having the same volume as the image object under consideration. The ratio of ellipsoids is equal to the ratio of the length, width and thickness of an image object. The volume of the image objects outside the ellipsoid is compared to the volume of the image objects inside the ellipsoid that are not filled.
Step 6.3, as shown in fig. 6, according to the decision tree algorithm model of the geometric feature, the determination modes of the threshold value F1 of the rectangular fitting degree and the threshold value F2 of the elliptical fitting degree, and the determination modes of the threshold value F1 of the rectangular fitting degree and the threshold value F2 of the elliptical fitting degree are as follows:
step 6.3.1, calculating rectangular fitting degree and ellipse fitting degree of sample points of rivers, lakes, reservoirs, floods, ponds and culture ponds, and making a scatter diagram;
step 6.3.2, determining that the average value of the maximum value of the rectangular fitting degree of the river sample points and the minimum value of the rectangular fitting degree of the sample points of the lakes, the ponds and the culture ponds is a threshold value F1 of the rectangular fitting degree;
and 6.3.3, determining the average value of the maximum value of the ellipse fitting degree of the river sample points and the minimum value of the ellipse fitting degree of the sample points of the lakes, the ponds and the culture ponds as a threshold value F2 of the ellipse fitting degree.
The decision tree algorithm model S1 can take a fixed value of 1.1 hectare and S2 can take a fixed value of 8 hectare.
And 7, converting the identified fresh water raster data into vector data to obtain identification results of different fresh water resource types.
Based on the decision tree algorithm model, as shown in fig. 7, (a) is a recognition result of the type of the freshwater resource in Fuling area of Chongqing city, (B) (C) (D) (E) corresponds to the amplifying effect of the B, C, D and E in (a), and (B ') (C') (D ') (E') corresponds to the SAR image of the B, C, D and E in (a).
In this embodiment, the same or similar parts as those in embodiment 1 may be referred to each other, and will not be described in detail in this application.
Example 3:
based on embodiments 1 and 2, embodiment 3 of the present application provides a fine recognition system for different freshwater resource types combining SAR and optical images, including:
the acquisition module is used for acquiring the time sequence SAR image and the time sequence optical image, preprocessing the time sequence SAR image and the time sequence optical image, generating corresponding median, maximum and minimum synthesized images for the preprocessed time sequence SAR image, and generating corresponding median synthesized images for the preprocessed time sequence optical image;
the construction module is used for synthesizing the images according to the maximum value and the minimum value of the time sequence SAR image, acquiring SAR backward scattering coefficient data and constructing a normalized flooding index NDHF;
the generation module is used for carrying out multi-scale segmentation on the synthesized image to generate a homogeneous unit object of the image;
the first calculation module is used for calculating the spectral characteristics of the homogeneous unit object, combining gradient parameters of Digital Elevation Model (DEM) data, and identifying the minimum range of fresh water resources by adopting a threshold method;
the second calculation module is used for synthesizing images by relying on the maximum value and the minimum value of SAR, calculating the flooding index NDHF of each homogeneous unit object, and constructing a decision tree algorithm based on spectral characteristics to identify a flooding area;
the third calculation module is used for merging the homogeneous unit objects, calculating the geometric shape characteristics of each homogeneous unit object, and constructing a decision tree algorithm based on the geometric shape characteristics to identify rivers, lakes, ponds and culture ponds;
and the conversion module is used for converting the identified fresh water raster data into vector data to obtain identification results of different fresh water resource types.
Specifically, the system provided in this embodiment is a system corresponding to the methods provided in embodiments 1 and 2, and therefore, the portions in this embodiment that are the same as or similar to those in embodiment 1 may be referred to each other, and will not be described in detail in this application.
In summary, the method extracts the SAR backscattering characteristics, the optical spectrum characteristics and the geometric shape characteristics of different fresh water resources based on the time sequence SAR and the optical images, and comprehensively uses the SAR backscattering characteristics, the optical spectrum characteristics and the geometric shape characteristics to construct a decision tree algorithm for distinguishing different fresh water resource types, so as to obtain the careful distinction of different fresh water resource types and the spatial range of the distribution of different fresh water resource types, thereby being beneficial to timely and accurately grasping the distribution and the change profile of the existing fresh water resources and realizing reasonable distribution and scientific scheduling of the fresh water resources. The method has a certain reference significance for the fine and automatic extraction of the fresh water in a large-scale or terrain complex area.
Claims (10)
1. The method for finely identifying different freshwater resource types by combining SAR and optical images is characterized by comprising the following steps of:
step 1, acquiring a sequential SAR image and a sequential optical image, preprocessing the sequential SAR image and the sequential optical image, generating a corresponding median, maximum and minimum synthesized image for the preprocessed sequential SAR image, and generating a corresponding median synthesized image for the preprocessed sequential optical image;
step 2, synthesizing an image according to the maximum value and the minimum value of the time sequence SAR image, selecting fresh water and non-fresh water type sample points, acquiring SAR backward scattering coefficient data, and constructing a normalized flooding index NDHF;
step 3, multi-scale segmentation is carried out on the synthesized image, and a homogeneous unit object of the image is generated;
step 4, calculating spectral characteristics of the homogeneous unit object, combining gradient parameters of digital elevation model DEM data, and identifying a minimum range of fresh water resources by adopting a threshold method;
step 5, synthesizing images by relying on the maximum value and the minimum value of SAR, calculating the flooding index NDHF of each homogeneous unit object, and constructing a decision tree algorithm based on spectral characteristics to identify a flooding area;
step 6, merging homogeneous unit objects, calculating the geometric shape characteristics of each homogeneous unit object, and constructing a decision tree algorithm based on the geometric shape characteristics to identify rivers, lakes, ponds and culture ponds;
and 7, converting the identified fresh water raster data into vector data to obtain identification results of different fresh water resource types.
2. The method for finely identifying different freshwater resource types by combining SAR and optical images according to claim 1, wherein in the step 1, the sequential SAR image is a Sentinel-1 satellite GRD image, the wave band is a C wave band, the polarization mode is VH polarization, and the preprocessing of the sequential SAR image comprises: track correction, thermal noise removal, radiometric calibration, doppler topography correction, lee filtering and decibelization; the time sequence optical image is a Sentinel-2 satellite MSI image, the wave bands are red, green, blue and near infrared, and the preprocessing of the time sequence optical image comprises the following steps: radiometric calibration, atmospheric correction, band superposition, mosaic and clipping.
3. The method for finely identifying different freshwater resource types by combining SAR and optical images according to claim 2, wherein in step 2, the calculation formula of the normalized flooding index NDHF is:
wherein VH min Is the VH backscattering coefficient minimum value of the SAR minimum value synthesized image, VH max Is the VH backscatter coefficient maximum value of the SAR maximum value synthesized image.
4. The method for fine recognition of different freshwater resource types by combining SAR and optical imaging according to claim 3, wherein in step 3, the parameters of the multi-scale segmentation include: scale, shape, compactness, and weight of the composite image.
5. The method for fine recognition of different freshwater resource types by combining SAR and optical images according to claim 4, wherein step 4 comprises:
step 4.1, calculating the gradient by using digital elevation model DEM data, wherein a gradient calculation formula is as follows:
slope=arctan (elevation difference/horizontal distance)
Wherein Slope represents a gradient, arctan represents an arctangent function;
step 4.2, calculating a normalized water index NDWI, wherein the formula is as follows:
wherein P is green Representing the reflectivity of the green band, P nir Indicating near infrared reflectivity;
and 4.3, calculating relevant parameters of the fresh water and non-fresh water type sample points, determining an optimal threshold value, and identifying all fresh water.
6. The method for fine recognition of different freshwater resource types by combining SAR and optical imaging according to claim 5, wherein step 4.3 comprises:
step 4.3.1, calculating pixel values of VH corresponding pixels in the SAR median synthetic image of the freshwater and non-freshwater type sample points, and making a box diagram, and determining a threshold B1 by the average value of the lower quartile of the non-freshwater resource and the upper quartile of the freshwater resource;
step 4.3.2, calculating gradients of fresh water and non-fresh water type sample points, drawing a histogram, and determining a gradient threshold value as B2;
step 4.3.3, calculating pixel values of pixels corresponding to the normalized water index NDWI of the fresh water and non-fresh water type sample points in the optical median synthetic image, manufacturing a box diagram, and determining a threshold B3 by the average value of the lower quartile of the fresh water resource and the upper quartile of the non-fresh water resource;
and 4.3.4, calculating pixel values of pixels corresponding to near infrared wave bands of fresh water and non-fresh water type sample points in an optical median synthetic image, manufacturing a box diagram, determining a threshold B4 by the average value of the upper quartile of fresh water resources and the lower quartile of non-fresh water resources, and identifying all fresh water.
7. The method for finely identifying different freshwater resource types by combining SAR and optical images according to claim 6, wherein in step 5, according to the decision tree algorithm model of spectral characteristics, the method comprises a flooding index threshold B5 and an annual VH backscattering coefficient maximum threshold B6, and the method for determining the flooding index threshold B5 is as follows:
calculating flooding indexes of a flooding area and other fresh water sample points, and making a box diagram, wherein the average value of the lower quartile of the flooding indexes of the flooding sample points and the upper quartile of the flooding indexes of other fresh water is a threshold B5 for distinguishing flooding from other fresh water resource types;
the annual VH backscatter coefficient maximum value threshold B6 is determined in the following manner: calculating the maximum value of the VH backscattering coefficient of the sample points of the flooding area and the non-flooding area, and making a box diagram, and flooding the VH of the sample points max Upper quartile of (v) and VH of non-flooded sample points max The mean of the lower quartile of (c) is a threshold B6 that determines flooding and other freshwater resource types.
8. The method for fine recognition of different freshwater resource types by combining SAR and optical images according to claim 7, wherein step 6 comprises:
step 6.1, merging the homogeneous unit objects, and performing post-classification treatment;
step 6.2, calculating the rectangular fitting degree, the ellipse fitting degree and the area of each homogeneous unit object;
step 6.3, according to a decision tree algorithm model of geometric shape characteristics, determining a threshold value F1 of rectangular fitting degree and a threshold value F2 of ellipse fitting degree, wherein the determination mode of the threshold value F1 of rectangular fitting degree and the threshold value F2 of ellipse fitting degree is as follows:
step 6.3.1, calculating rectangular fitting degree and ellipse fitting degree of sample points of rivers, lakes, reservoirs, floods, ponds and culture ponds, and making a scatter diagram;
step 6.3.2, determining that the average value of the maximum value of the rectangular fitting degree of the river sample points and the minimum value of the rectangular fitting degree of the sample points of the lakes, the ponds and the culture ponds is a threshold value F1 of the rectangular fitting degree;
and 6.3.3, determining the average value of the maximum value of the ellipse fitting degree of the river sample points and the minimum value of the ellipse fitting degree of the sample points of the lakes, the ponds and the culture ponds as a threshold value F2 of the ellipse fitting degree.
9. A system for finely identifying different freshwater resource types of a joint SAR and an optical image, for performing the finely identifying method of different freshwater resource types of a joint SAR and an optical image according to any one of claims 1 to 8, comprising:
the acquisition module is used for acquiring the time sequence SAR image and the time sequence optical image, preprocessing the time sequence SAR image and the time sequence optical image, generating corresponding median, maximum and minimum synthesized images for the preprocessed time sequence SAR image, and generating corresponding median synthesized images for the preprocessed time sequence optical image;
the construction module is used for synthesizing the images according to the maximum value and the minimum value of the time sequence SAR image, acquiring SAR backward scattering coefficient data and constructing a normalized flooding index NDHF;
the generation module is used for carrying out multi-scale segmentation on the synthesized image to generate a homogeneous unit object of the image;
the first calculation module is used for calculating the spectral characteristics of the homogeneous unit object, combining gradient parameters of Digital Elevation Model (DEM) data, and identifying the minimum range of fresh water resources by adopting a threshold method;
the second calculation module is used for synthesizing images by relying on the maximum value and the minimum value of SAR, calculating the flooding index NDHF of each homogeneous unit object, and constructing a decision tree algorithm based on spectral characteristics to identify a flooding area;
the third calculation module is used for merging the homogeneous unit objects, calculating the geometric shape characteristics of each homogeneous unit object, and constructing a decision tree algorithm based on the geometric shape characteristics to identify rivers, lakes, ponds and culture ponds;
and the conversion module is used for converting the identified fresh water raster data into vector data to obtain identification results of different fresh water resource types.
10. A computer storage medium having a computer program stored therein; the computer program, when run on a computer, causes the computer to perform the method for finely identifying different freshwater resource types for joint SAR and optical imaging according to any of claims 1 to 8.
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