CN115147746B - Saline-alkali geological identification method based on unmanned aerial vehicle remote sensing image - Google Patents

Saline-alkali geological identification method based on unmanned aerial vehicle remote sensing image Download PDF

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CN115147746B
CN115147746B CN202211068025.6A CN202211068025A CN115147746B CN 115147746 B CN115147746 B CN 115147746B CN 202211068025 A CN202211068025 A CN 202211068025A CN 115147746 B CN115147746 B CN 115147746B
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CN115147746A (en
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李伯春
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Guangdong Rongqi Intelligent Technology Co Ltd
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Abstract

The invention relates to the technical field of image data processing, in particular to a saline-alkali geological identification method based on an unmanned aerial vehicle remote sensing image, which comprises the following steps: acquiring a remote sensing image to be detected of a target ground area under the saline-alkali condition to be detected; carrying out image enhancement extraction processing on a remote sensing image to be detected; carrying out color space conversion processing on the target remote sensing image; carrying out region division optimization on the color remote sensing image; merging and dividing the initial optimal saline-alkali area; cutting each target saline-alkali area; inputting each target saline-alkali image into a saline-alkali degree classification network, and determining the saline-alkali degree corresponding to the target saline-alkali image; and generating target saline-alkali information representing the saline-alkali condition of the target ground area. The method solves the technical problem of low accuracy of saline-alkali identification of the soil by carrying out image processing on the remote sensing image to be detected, improves the accuracy of saline-alkali identification of the soil, and is mainly applied to identification of the saline-alkali degree of the soil.

Description

Saline-alkali geological identification method based on unmanned aerial vehicle remote sensing image
Technical Field
The invention relates to the technical field of image data processing, in particular to a saline-alkali geological identification method based on an unmanned aerial vehicle remote sensing image.
Background
Under natural environment influence or artificial movement interference, the salinity in soil and groundwater rises to the earth's surface through the moisture of capillary along with the bottom, and waits to the moisture evaporation after, the salinity often gathers in earth's surface soil, forms saline and alkaline land. Large-scale geological salinization not only affects the land quality in the range, but also often causes yield reduction in cultivated land and water pollution, and even causes reduction in biodiversity. In addition, geological salinization may also cause many other serious land problems, with very serious direct or indirect economic losses to human society. Therefore, the saline-alkali identification of the soil is very important. At present, when saline-alkali identification is carried out on soil, the mode that usually adopts is: firstly, acquiring a soil ground image, inputting the soil ground image into a semantic segmentation network, extracting and segmenting the soil ground image through the semantic segmentation network, finally, respectively inputting a plurality of extracted and segmented images obtained after extraction and segmentation into a classification neural network, and detecting the saline-alkali degree of the plurality of extracted and segmented images through the classification neural network to obtain the saline-alkali degree corresponding to each extracted and segmented image.
However, when the above-described manner is adopted, there are often technical problems as follows:
due to the characteristics of the semantic segmentation network, the relation between pixels is not considered when the soil ground image is extracted and segmented through the semantic segmentation network, the space consistency is lacked, and the extraction and segmentation are not sensitive to details, so that the soil ground image is not accurate enough in extraction and segmentation, and therefore, the extracted and segmented images of regions with various saline-alkali degrees possibly exist in the obtained plurality of extracted and segmented images, the saline-alkali degrees corresponding to the segmented images cannot be extracted through accurate determination of a subsequent classification neural network, and further the saline-alkali identification of soil is low in accuracy.
Disclosure of Invention
This summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter.
In order to solve the technical problem of low accuracy of saline-alkali identification of soil, the invention provides a saline-alkali geological identification method based on an unmanned aerial vehicle remote sensing image.
The invention provides a saline-alkali geological identification method based on unmanned aerial vehicle remote sensing images, which comprises the following steps:
acquiring a remote sensing image to be detected of a target ground area of a saline-alkali condition to be detected through a target camera installed on a target unmanned aerial vehicle;
carrying out image enhancement extraction processing on the remote sensing image to be detected to obtain a target remote sensing image;
carrying out color space conversion processing on the target remote sensing image to obtain a color remote sensing image;
carrying out region division optimization on the color remote sensing image to obtain a preliminary optimal saline-alkali region set;
merging and dividing the initial optimal saline-alkali regions in the initial optimal saline-alkali region set to obtain a target saline-alkali region set;
cutting each target saline-alkali area in the target saline-alkali area set, determining a target saline-alkali image corresponding to the target saline-alkali area to obtain a target saline-alkali image set;
inputting each target saline-alkali image in the target saline-alkali image set into a trained saline-alkali degree classification network, and determining the saline-alkali degree corresponding to the target saline-alkali image through the saline-alkali degree classification network;
and generating target saline-alkali information representing the saline-alkali condition of the target ground area according to the saline-alkali degree corresponding to each target saline-alkali image in the target saline-alkali image set.
Further, the color remote sensing image is subjected to region division optimization to obtain a preliminary optimal saline-alkali region set, which includes:
mapping the color remote sensing image into a target undirected graph;
and determining the initial optimal saline-alkali area set through an optimization algorithm according to the target undirected graph.
Further, the objective function of the optimization algorithm is:
Figure 832012DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 664970DEST_PATH_IMAGE002
is the objective function of the optimization algorithm,Fis the value of the objective function of the optimization algorithm,
Figure 933141DEST_PATH_IMAGE003
is a first objective function of the first set of functions,His the value of the first objective function,
Figure 597471DEST_PATH_IMAGE004
，
Figure 574786DEST_PATH_IMAGE005
so as to make
Figure 691777DEST_PATH_IMAGE006
Counting at the bottom
Figure 294797DEST_PATH_IMAGE007
The number of the pairs is logarithmic,
Figure 16896DEST_PATH_IMAGE007
is the second in the target undirected graphiPixel point and the secondjThe probability of a transition between individual pixel points,
Figure 617642DEST_PATH_IMAGE008
，
Figure 436911DEST_PATH_IMAGE009
，
Figure 578042DEST_PATH_IMAGE010
is the second in the target undirected graphiThe sum of the edge weights between each pixel point and each pixel point in the target undirected graph,Iis the number of pixel points in the target undirected graph, the targetSecond in the Infinite drawingiPixel point and the secondjThe edge weight between each pixel point is
Figure 216965DEST_PATH_IMAGE011
，exp() Is an exponential function with a natural constant as the base,
Figure 801661DEST_PATH_IMAGE012
is the second in the target undirected graphiPixel point and the secondjThe euclidean distance between the individual pixel points,
Figure 283458DEST_PATH_IMAGE013
is the second in the target undirected graphiHue value corresponding to each pixel point and the secondjThe difference in hue value corresponding to each pixel point,
Figure 244592DEST_PATH_IMAGE014
is the second in the target undirected graphiSaturation value corresponding to each pixel point and the secondjThe difference in saturation values corresponding to each pixel point,
Figure 659393DEST_PATH_IMAGE015
is the second in the target undirected graphiBrightness value corresponding to each pixel point and the secondjThe difference in luminance values corresponding to each pixel point,Uis the parameter of the model and is,
Figure 742887DEST_PATH_IMAGE016
is the coefficient of the model that is,
Figure 462712DEST_PATH_IMAGE017
is the second objective function of the first function,Objis the value of the second objective function,
Figure 211225DEST_PATH_IMAGE018
is the second in the target undirected graphkThe number of pixel points within a sub-region, a sub-region being a region in the target undirected graph,
Figure 496844DEST_PATH_IMAGE019
and
Figure 485660DEST_PATH_IMAGE020
are respectively the second in the target undirected graphkThe length and width of the smallest circumscribed rectangle corresponding to a sub-region,Kis the number of sub-regions into which the target undirected graph is divided.
Further, the preliminary optimal saline-alkali area in the preliminary optimal saline-alkali area set is merged and divided to obtain a target saline-alkali area set, and the method comprises the following steps:
normalizing the target remote sensing image to obtain a target normalized image;
determining a target preliminary region corresponding to each preliminary optimal saline-alkali region in the preliminary optimal saline-alkali region set according to the position of each preliminary optimal saline-alkali region in the color remote sensing image to obtain a target preliminary region set, wherein the target preliminary region in the target preliminary region set is a region in the target normalized image;
dividing a channel value of each channel in a target number of channels of the target normalized image into a preset number of channel levels to obtain a channel level set, wherein each channel of the target normalized image corresponds to the preset number of channel levels, and the number of the channel levels in the channel level set is the power of the preset number of target number;
determining a color channel histogram corresponding to each target preliminary region in the target preliminary region set;
determining a first region merging index between every two target preliminary regions according to the channel level set and color channel histograms corresponding to the two target preliminary regions in the target preliminary region set;
determining a second region merging index between every two target preliminary regions according to every two target preliminary regions in the target preliminary region set;
determining an overall region merging index between every two target preliminary regions according to a first region merging index and a second region merging index between every two target preliminary regions in the target preliminary region set;
when the overall area merging index between two target preliminary areas in the target preliminary area set is larger than a preset judgment threshold value, dividing the two target preliminary areas into the same type of target preliminary area;
and determining the target preliminary region in the same target preliminary region as a target saline-alkali region in the target saline-alkali region set.
Further, the formula for determining the correspondence between the first region merging indicators of the two target preliminary regions is as follows:
Figure 942049DEST_PATH_IMAGE021
wherein the content of the first and second substances,
Figure 572882DEST_PATH_IMAGE022
is the first in the target preliminary region setpA target preliminary region andqa first region merging indicator between the target preliminary regions,Cis the number of channel levels in the set of channel levels,
Figure 978586DEST_PATH_IMAGE023
is the first in the target preliminary region setpThe first of the channel level sets included in the corresponding color channel histogram for each target preliminary regioncThe histogram distribution values at the level of one channel,
Figure 794096DEST_PATH_IMAGE024
is the first in the target preliminary region setqA first one of the channel level sets included in the corresponding color channel histogram for each target preliminary regioncThe histogram distribution values at the level of one channel,
Figure 347568DEST_PATH_IMAGE025
is presetA number greater than 0.
Further, the determining a second region merging indicator between every two target preliminary regions according to every two target preliminary regions in the target preliminary region set includes:
translating the two target preliminary regions to enable the distance between the two target preliminary regions to be zero, and obtaining fitting regions corresponding to the two target preliminary regions;
and determining a second region merging index between the two target preliminary regions according to the two target preliminary regions and the corresponding fitting regions of the two target preliminary regions.
Further, the formula for determining the second region merging indicator between the two target preliminary regions is:
Figure 188616DEST_PATH_IMAGE026
wherein the content of the first and second substances,
Figure 635778DEST_PATH_IMAGE027
is the first in the target preliminary region setpA target preliminary region andqsecond region merging indicators between the target preliminary regions,exp() Is an exponential function with a natural constant as the base,
Figure 169659DEST_PATH_IMAGE028
is the first in the target preliminary region setpA target preliminary region andqthe number of pixel points in the fitting and region corresponding to each target preliminary region,
Figure 131798DEST_PATH_IMAGE029
is the first in the target preliminary region setpA target preliminary region andqthe number of edge pixel points included in the fitting and region corresponding to each target preliminary region,
Figure 838854DEST_PATH_IMAGE030
is the first in the target preliminary region setpA target preliminary region andqthe perimeter of the minimum bounding rectangle corresponding to the fitting and region corresponding to each target preliminary region,
Figure 156834DEST_PATH_IMAGE031
is the first in the target preliminary region setpThe number of pixel points within the individual target initialization region,
Figure 110884DEST_PATH_IMAGE032
is the first in the target preliminary region setpThe number of edge pixels included in each target preliminary region,
Figure 638948DEST_PATH_IMAGE033
is the first in the target preliminary region setpThe perimeter of the minimum bounding rectangle corresponding to each preliminary region of interest,
Figure 810080DEST_PATH_IMAGE034
is the first in the target preliminary region setqThe number of pixel points within the individual target initialization region,
Figure 231834DEST_PATH_IMAGE035
is the first in the target preliminary region setqThe number of edge pixels included in each target preliminary region,
Figure 107518DEST_PATH_IMAGE036
is the first in the target preliminary region setqThe perimeter of the minimum bounding rectangle corresponding to each target preliminary region.
Further, the formula for determining the correspondence of the overall region merging indicator between the two target preliminary regions is as follows:
Figure 857299DEST_PATH_IMAGE037
wherein the content of the first and second substances,
Figure 561950DEST_PATH_IMAGE038
is the first in the target preliminary region setpA target preliminary region andqthe overall region merging index between the target preliminary regions,exp() Is an exponential function with a natural constant as the base,
Figure 385680DEST_PATH_IMAGE022
is the first in the target preliminary region setpA target preliminary region andqa first region merging indicator between the target preliminary regions,
Figure 681533DEST_PATH_IMAGE027
is the first in the target preliminary region setpA target preliminary region andqsecond region merging indicators between the target preliminary regions.
Further, the image enhancement extraction processing is performed on the remote sensing image to be detected to obtain a target remote sensing image, and the image enhancement extraction processing includes:
carrying out image enhancement processing on the remote sensing image to be detected through an image enhancement algorithm to obtain an enhanced image to be detected;
and extracting a target ground area of the enhanced image to be detected to obtain the target remote sensing image.
Further, the training process of the saline-alkali degree classification network comprises the following steps:
constructing a saline-alkali degree classification network;
obtaining a sample saline-alkali image set, wherein a label corresponding to a sample saline-alkali image in the sample saline-alkali image set is the saline-alkali degree;
and training the saline-alkali degree classification network by utilizing the sample saline-alkali image set and the label corresponding to each sample saline-alkali image in the sample saline-alkali image set to obtain the trained saline-alkali degree classification network.
The invention has the following beneficial effects:
according to the saline-alkali geological identification method based on the unmanned aerial vehicle remote sensing image, the technical problem that the accuracy of saline-alkali identification of soil is low is solved by image processing of the remote sensing image to be detected, and the accuracy of saline-alkali identification of the soil is improved. Firstly, a target camera installed on a target unmanned aerial vehicle is used for acquiring a remote sensing image to be detected of a target ground area to be detected for saline-alkali conditions. The remote sensing image to be detected contains the information of the target ground area, so that the subsequent image processing of the remote sensing image to be detected can be facilitated, and the saline-alkali condition corresponding to the remote sensing image to be detected can be determined, so that the saline-alkali condition of the target ground area can be determined, and the accuracy of determining the saline-alkali condition of the target ground area can be improved. And secondly, carrying out image enhancement extraction processing on the remote sensing image to be detected to obtain a target remote sensing image. The image enhancement processing is carried out on the remote sensing image to be detected, the image contrast of the remote sensing image to be detected can be improved, the visualization degree of the remote sensing image to be detected is higher, and the subsequent image processing can be conveniently carried out on the remote sensing image to be detected. Secondly, in actual conditions, through the target camera, not only the target ground area but also an area other than the target ground area is often shot on the obtained remote sensing image to be detected. However, the regions other than the target ground region do not need to be subjected to saline-alkali condition judgment, so that the enhanced image to be detected is extracted to obtain the target remote sensing image only shooting the target ground region, subsequent steps can be omitted in the regions other than the target ground region, the calculation amount can be reduced, the occupation of calculation resources can be reduced, and the efficiency of saline-alkali identification on the target ground region can be improved. And then, carrying out color space conversion processing on the target remote sensing image to obtain a color remote sensing image. Since the color remote sensing image may be an HSV image. HSV images tend to have less correlation between the three channels of H (Hue), S (Saturation) and V (Value), and tend to be more visually consistent. Subsequent analysis of H, S and V three channels can be facilitated. And then, carrying out region division optimization on the color remote sensing image to obtain a primary optimal saline-alkali region set. And continuing to merge and divide the initial optimal saline-alkali regions in the initial optimal saline-alkali region set to obtain a target saline-alkali region set. In practical situations, the saline-alkali degree corresponding to each position in the target ground area is often more than one, that is, the target ground area may often include a plurality of areas with different saline-alkali degrees. Therefore, the color remote sensing images are subjected to area division, optimization and combination, a plurality of target saline-alkali areas with different corresponding saline-alkali degrees can be obtained, and the saline-alkali condition of the target ground area can be conveniently and accurately judged subsequently. And then, cutting each target saline-alkali area in the target saline-alkali area set, determining a target saline-alkali image corresponding to the target saline-alkali area, and obtaining a target saline-alkali image set. The different target saline-alkali regions of the corresponding saline-alkali conditions are cut off to obtain the target saline-alkali images in one-to-one correspondence with the saline-alkali degrees, so that the subsequent analysis of the multiple saline-alkali degrees corresponding to the target ground regions and the position of each saline-alkali degree in the target ground regions can be facilitated. Then, inputting each target saline-alkali image in the target saline-alkali image set into a trained saline-alkali degree classification network, and determining the saline-alkali degree corresponding to the target saline-alkali image through the saline-alkali degree classification network. And finally, generating target saline-alkali information representing the saline-alkali condition of the target ground area according to the saline-alkali degree corresponding to each target saline-alkali image in the target saline-alkali image set. Therefore, the method and the device solve the technical problem of low accuracy of saline-alkali identification of the soil by processing the image of the remote sensing image to be detected, and improve the accuracy of the saline-alkali identification of the soil.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions and advantages of the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flow diagram of some embodiments of a method for saline-alkali geological identification based on unmanned aerial vehicle remote sensing images according to the invention;
FIG. 2 is a schematic diagram of an enhanced image to be detected and a target remote sensing image according to the present invention;
FIG. 3 is a schematic diagram of a distance between two target preliminary regions being zero according to the present invention;
fig. 4 is a schematic diagram of a target normalized image, a target saline-alkali region and a target saline-alkali image according to the invention.
Wherein the reference numerals in fig. 2 include: the method comprises the steps of an enhanced image to be detected 201, an image area 202 and a target remote sensing image 203.
The reference numerals in fig. 3 include: a first target preliminary region 301 and a second target preliminary region 302.
The reference numerals in fig. 4 include: the target normalized image 401, the first target saline-alkali region 402, the second target saline-alkali region 403, the first target saline-alkali image 404 and the second target saline-alkali image 405.
Detailed Description
To further explain the technical means and effects of the present invention adopted to achieve the predetermined objects, the following detailed description of the embodiments, structures, features and effects of the technical solutions according to the present invention will be given with reference to the accompanying drawings and preferred embodiments. In the following description, different references to "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
The invention provides a saline-alkali geological identification method based on an unmanned aerial vehicle remote sensing image, which comprises the following steps:
acquiring a remote sensing image to be detected of a target ground area of a saline-alkali condition to be detected through a target camera installed on a target unmanned aerial vehicle;
carrying out image enhancement extraction processing on the remote sensing image to be detected to obtain a target remote sensing image;
carrying out color space conversion processing on the target remote sensing image to obtain a color remote sensing image;
carrying out region division optimization on the color remote sensing image to obtain a preliminary optimal saline-alkali region set;
merging and dividing the initial optimal saline-alkali regions in the initial optimal saline-alkali region set to obtain a target saline-alkali region set;
cutting each target saline-alkali area in the target saline-alkali area set, determining a target saline-alkali image corresponding to the target saline-alkali area, and obtaining a target saline-alkali image set;
inputting each target saline-alkali image in the target saline-alkali image set into a trained saline-alkali degree classification network, and determining the saline-alkali degree corresponding to the target saline-alkali image through the saline-alkali degree classification network;
according to the saline-alkali degree corresponding to each target saline-alkali image in the target saline-alkali image set, and generating target saline-alkali information representing the saline-alkali condition of the target ground area.
The following steps are detailed:
referring to fig. 1, a flow of some embodiments of a method for identifying saline-alkali geology based on unmanned aerial vehicle remote sensing images according to the present invention is shown. The saline-alkali geological identification method based on the unmanned aerial vehicle remote sensing image comprises the following steps:
s1, acquiring a remote sensing image to be detected of a target ground area to be detected for saline-alkali conditions through a target camera installed on a target unmanned aerial vehicle.
In some embodiments, the remote sensing image to be detected of the target ground area to be detected for saline-alkali condition can be acquired by a target camera installed on the target unmanned aerial vehicle.
Wherein, above-mentioned target unmanned aerial vehicle is the unmanned aerial vehicle that can normally fly. The target camera may be a remote sensing camera mounted on the target drone. The target ground area may be a ground area to be detected for saline-alkali conditions. The remote sensing image to be detected can be a remote sensing image of a target ground area to be detected for saline-alkali conditions.
As an example, the target unmanned aerial vehicle may be controlled to move to a target position, and the target camera is used to obtain a remote sensing image to be detected of a target ground area where saline-alkali conditions are to be detected. Wherein the target location may be a location directly above the target ground area. The distance between the target position and the target ground area may be a preset height, and may be set according to actual conditions. The shooting angle of the target camera may be a preset angle, and may be set according to actual conditions. The data set according to the actual situation needs to ensure that the remote sensing image to be detected can be acquired through the target camera.
And S2, carrying out image enhancement extraction processing on the remote sensing image to be detected to obtain a target remote sensing image.
In some embodiments, the remote sensing image to be detected may be subjected to image enhancement extraction processing to obtain a target remote sensing image.
The target remote sensing image can be a remote sensing image to be detected after image enhancement extraction processing.
As an example, this step may include the steps of:
firstly, carrying out image enhancement processing on the remote sensing image to be detected through an image enhancement algorithm to obtain an enhanced image to be detected.
The enhanced image to be detected can be a remote sensing image to be detected after image enhancement processing. Image enhancement algorithms may include, but are not limited to: histogram equalization algorithms, laplacian algorithms, and gamma transformation algorithms.
And secondly, extracting a target ground area of the enhanced image to be detected to obtain the target remote sensing image.
The target remote sensing image can be a region image corresponding to a target ground region.
As an example, as shown in fig. 2, a target ground area extraction process may be performed on the enhanced image to be detected 201 to obtain a target remote sensing image 203. The image area 202 may be an area where the target ground area corresponds to the enhanced image 201 to be detected. And cutting the image area 202 from the enhanced image 201 to be detected to obtain a target remote sensing image 203.
In practical situations, through the target camera, not only the target ground area but also an area other than the target ground area is often shot on the obtained remote sensing image to be detected. However, the regions other than the target ground region do not need to be subjected to saline-alkali condition judgment, so that the target ground region extraction processing is performed on the to-be-detected enhanced image to obtain a target remote sensing image only shooting the target ground region, the subsequent steps are not performed on the regions other than the target ground region, the calculation amount can be reduced, the occupation of calculation resources can be reduced, and the efficiency of saline-alkali identification on the target ground region can be improved.
And S3, performing color space conversion processing on the target remote sensing image to obtain a color remote sensing image.
In some embodiments, the target remote sensing image may be subjected to color space conversion processing to obtain a color remote sensing image.
The color remote sensing image can be an HSV image converted from a target remote sensing image.
As an example, the target remote sensing image may be converted into a color remote sensing image by a conventional technique.
And S4, carrying out region division optimization on the color remote sensing image to obtain a preliminary optimal saline-alkali region set.
In some embodiments, the color remote sensing image may be subjected to region division optimization to obtain a preliminary optimal saline-alkali region set.
As an example, this step may include the steps of:
and step one, mapping the color remote sensing image into a target undirected graph.
The target undirected graph can be an undirected graph mapped by the color remote sensing image. The nodes of the target undirected graph can be pixel points in the color remote sensing image. The edges of the target undirected graph can be connecting lines among pixel points in the color remote sensing image.
And secondly, determining the initial optimal saline-alkali area set through an optimization algorithm according to the target undirected graph.
Wherein, the saline and alkaline degree that preliminary optimum saline and alkaline area in the above-mentioned preliminary optimum saline and alkaline area set corresponds can be different. The degree of salt/alkali may be, but is not limited to: normal area, light saline-alkali area, moderate saline-alkali area and severe saline-alkali area.
For example, the objective function of the above optimization algorithm may be:
Figure 918610DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 177684DEST_PATH_IMAGE002
is the objective function of the optimization algorithm described above.FAre the objective function values of the above-described optimization algorithm.
Figure 370768DEST_PATH_IMAGE003
Is the first objective function.HIs the first objective function value.
Figure 588254DEST_PATH_IMAGE004
。
Figure 437261DEST_PATH_IMAGE005
So as to make
Figure 765605DEST_PATH_IMAGE006
Counting at the bottom
Figure 813196DEST_PATH_IMAGE007
Logarithm. For example,
Figure 529479DEST_PATH_IMAGE039
。
Figure 616515DEST_PATH_IMAGE007
is the second in the above target undirected graphiA pixel point andfirst, thejThe transition probability between individual pixel points.
Figure 997818DEST_PATH_IMAGE008
。
Figure 650647DEST_PATH_IMAGE009
。
Figure 537832DEST_PATH_IMAGE010
Is the second in the above target undirected graphiAnd the sum of the edge weight value between each pixel point and each pixel point in the target undirected graph.IIs the number of pixel points in the target undirected graph. The second in the above objective undirected graphiPixel point and the secondjThe edge weight between each pixel point is
Figure 892590DEST_PATH_IMAGE011
。exp() Is an exponential function with a natural constant as the base.
Figure 765999DEST_PATH_IMAGE012
Is the second in the above target undirected graphiPixel point and the secondjThe Euclidean distance between pixel points.
Figure 522602DEST_PATH_IMAGE013
Is the second in the above target undirected graphiHue value (H, hue) corresponding to each pixel point and the secondjDifference values of hue values corresponding to the individual pixel points.
Figure 252792DEST_PATH_IMAGE014
Is the second in the above target undirected graphiSaturation value (S) corresponding to each pixel point and the second pixel pointjThe difference in saturation values corresponding to each pixel point.
Figure 360425DEST_PATH_IMAGE015
Is the second in the above target undirected graphiBrightness Value (V, value) corresponding to each pixel point and the second pixel pointjDifference in luminance values corresponding to each pixel point.UAre the model parameters.
Figure 896580DEST_PATH_IMAGE016
Are the model coefficients.
Figure 258422DEST_PATH_IMAGE017
Is the second objective function.ObjIs the second objective function value.
Figure 408781DEST_PATH_IMAGE018
Is the second in the above target undirected graphkThe number of pixels in each sub-region. The sub-region is a region in the above-described target undirected graph.
Figure 766162DEST_PATH_IMAGE019
And
Figure 761799DEST_PATH_IMAGE020
are respectively the second in the above-mentioned target undirected graphkThe length and width of the minimum bounding rectangle corresponding to a sub-region.KIs the number of sub-regions obtained by dividing the target undirected graph. Wherein the model parametersUAnd coefficient of model
Figure 243727DEST_PATH_IMAGE016
May be preset. For example,Uand (2). Model coefficients
Figure 768250DEST_PATH_IMAGE016
May be positive real numbers.
When the value of the objective functionFAnd when the maximum value is obtained, dividing the obtained area to obtain a primary optimal saline-alkali area.
Due to the fact that
Figure 335628DEST_PATH_IMAGE007
Is the second in the above target undirected graphiPixel point and the secondjThe transition probability between individual pixel points.
Figure 400536DEST_PATH_IMAGE008
。
Figure 736971DEST_PATH_IMAGE009
。
Figure 42181DEST_PATH_IMAGE010
Is the second in the above target undirected graphiAnd the sum of the edge weight value between each pixel point and each pixel point in the target undirected graph.IIs the number of pixel points in the target undirected graph. The second in the above objective undirected graphiPixel point and the secondjThe edge weight between each pixel point is
Figure 159173DEST_PATH_IMAGE011
. So that the first objective function valueHEntropy rate values corresponding to the target undirected graph can be characterized. The larger the entropy value is, the higher the structural similarity of each sub-region obtained after the color remote sensing image is divided is, that is, the greater the similarity between pixel points in the sub-region is, and the target function value isFThe larger the division result, the better the division result. Model coefficients
Figure 231034DEST_PATH_IMAGE016
Can be set according to actual conditions, so that the target function
Figure 421975DEST_PATH_IMAGE002
Adding model coefficients
Figure 350617DEST_PATH_IMAGE016
The result can be more in line with the actual situation. Due to the fact that
Figure 954905DEST_PATH_IMAGE018
Is the second in the above-mentioned target undirected graphkThe number of pixels in each sub-region. The sub-region is a region in the above-described target undirected graph.
Figure 112348DEST_PATH_IMAGE019
And
Figure 938221DEST_PATH_IMAGE020
are respectively the second in the above-mentioned target undirected graphkIndividual subareaThe field corresponds to the length and width of the minimum bounding rectangle. Therefore, it is not only easy to use
Figure 257338DEST_PATH_IMAGE040
Can characterize thekMorphological characteristics of the sub-regions. When the morphological characteristics of the sub-regions are larger, the total number of the divided sub-regionsKThe smaller the number of the division, the more stable the form of the divided sub-region, the more compact the structure in the sub-region, and the objective function valueFThe larger the division result, the better the division result.
And S5, merging and dividing the initial optimal saline-alkali regions in the initial optimal saline-alkali region set to obtain a target saline-alkali region set.
In some embodiments, the preliminary optimal saline-alkali regions in the preliminary optimal saline-alkali region set may be merged and divided to obtain a target saline-alkali region set.
The saline-alkali degree corresponding to each target saline-alkali area in the target saline-alkali area set can be different.
As an example, this step may include the steps of:
firstly, normalizing the target remote sensing image to obtain a target normalized image.
The target normalized image can be a normalized target remote sensing image.
And secondly, determining a target preliminary region corresponding to each preliminary optimal saline-alkali region in the preliminary optimal saline-alkali region set according to the position of each preliminary optimal saline-alkali region in the color remote sensing image, and obtaining a target preliminary region set.
The target preliminary region in the target preliminary region set may be a region in the target normalized image.
For example, the position of the preliminary optimal saline-alkali region in the color remote sensing image can be determined as the target position. And determining the region where the target position in the target normalized image is located as a target preliminary region corresponding to the preliminary optimal saline-alkali region. Wherein, the shape and size of the preliminary optimal saline-alkali region may be the same as the shape and size of the target preliminary region corresponding to the preliminary optimal saline-alkali region.
Because the color remote sensing image is the target remote sensing image after color space conversion, actual scenes corresponding to pixel points at the same position in the color remote sensing image and the target remote sensing image can be the same. The initial optimal saline-alkali region in the initial optimal saline-alkali region set can be a region in the color remote sensing image. The target preliminary region in the set of target preliminary regions may be a region in the target normalized image. The target normalized image may be a normalized target remote sensing image. Therefore, the actual scenes corresponding to the pixel points at the same position in the color remote sensing image, the target remote sensing image and the target normalized image can be the same. Therefore, the actual scenes corresponding to the image areas at the same position in the color remote sensing image, the target remote sensing image and the target normalized image can be the same. Therefore, the first position may be the same as the second position. The first location may be a location of the preliminary optimal saline-alkali region in the color remote sensing image. The second location may be a location of a preliminary region of interest in the normalized image of interest corresponding to the preliminary optimal saline-alkali region.
And thirdly, dividing the channel value of each channel in the target quantity of channels of the target normalized image into a preset quantity of channel grades to obtain a channel grade set.
The target number may be the number of channels of the target normalized image. For example, the channels of the target normalized image may be an R (Red), G (Green), and B (Blue) channel in RGB mode, respectively. The target number may be 3. Each channel of the target normalized image may correspond to a preset number of channel levels. The preset number may be a preset number. For example, the preset number may be 16. The number of channel levels in the channel level set may be raised to the power of a preset number of target numbers. For example, the number of channel levels in the set of channel levels described above may be 16 to the power of 3.
For example, the preset number may be 2. The channel values of the R channel of the target normalized image may include: 0.1,0.2,0.6 and 0.7. The channel values of the R channel of the target normalized image may be divided into 2 channel levels. Where 0.1 and 0.2 may be one channel level. 0.6 and 0.7 may be another channel rank.
And fourthly, determining a color channel histogram corresponding to each target preliminary region in the target preliminary region set.
The color channel histogram may be a histogram in which the channel level is used as a horizontal axis value and the number of target pixel points is used as a vertical axis value. The target pixel point can be a pixel point of which the pixel value in the target preliminary region belongs to the channel level.
And fifthly, determining a first region merging index between the two target preliminary regions according to the color channel histograms corresponding to each two target preliminary regions in the target preliminary region set and the channel level set.
For example, the above formula for determining the correspondence between the first region merging indicators of the two target preliminary regions may be:
Figure 739135DEST_PATH_IMAGE021
wherein the content of the first and second substances,
Figure 434690DEST_PATH_IMAGE022
is the first in the set of target preliminary regionspA target preliminary region andqand merging indexes of the first region between the target preliminary regions.CIs the number of channel levels in the set of channel levels described above.
Figure 115070DEST_PATH_IMAGE023
Is the first in the set of target preliminary regionspThe first one of the target preliminary regions in the above channel level set included in the corresponding color channel histogramcHistogram distribution values at each channel level.
Figure 932984DEST_PATH_IMAGE024
Is the first in the set of target preliminary regionsqThe first one of the target preliminary regions in the above channel level set included in the corresponding color channel histogramcHistogram distribution values at each channel level. The histogram distribution value may be a vertical axis value of the color channel histogram.
Figure 449548DEST_PATH_IMAGE025
Is a preset number greater than 0.
Figure 198061DEST_PATH_IMAGE025
May be a very small number.
Figure 421363DEST_PATH_IMAGE025
The effect of (3) is mainly to prevent the denominator from being 0.
Due to the fact that
Figure 862708DEST_PATH_IMAGE023
Is the first in the set of target preliminary regionspThe first one of the target preliminary regions in the above channel level set included in the corresponding color channel histogramcHistogram distribution values at each channel level.
Figure 600988DEST_PATH_IMAGE024
Is the first in the set of target preliminary regionsqThe first one of the target preliminary regions in the above channel level set included in the corresponding color channel histogramcHistogram distribution values at each channel level. Therefore, the first and second electrodes are formed on the substrate,
Figure 966242DEST_PATH_IMAGE041
can characterize thepA target preliminary region andqthe difference in histogram distribution values of the c-th channel level of the respective target preliminary regions. Since only the difference between the two needs to be determined here, and the positive and negative need not be considered, the method has the advantages of simple structure, low cost and high reliability
Figure 824476DEST_PATH_IMAGE042
The difference between the two can be characterized, an
Figure 175737DEST_PATH_IMAGE042
The larger the difference between the two. Therefore, the first and second electrodes are formed on the substrate,
Figure 729209DEST_PATH_IMAGE043
the difference between the p-th target preliminary region and the q-th target preliminary region can be characterized and then squared to obtain
Figure 22787DEST_PATH_IMAGE044
The difference between the p-th target preliminary region and the q-th target preliminary region may be made more conspicuous.
Figure 610895DEST_PATH_IMAGE025
The denominator can be prevented from being 0.
Figure 144775DEST_PATH_IMAGE044
The smaller the size of the tube is,
Figure 575757DEST_PATH_IMAGE045
the larger. Therefore it is firstpA target preliminary region andqfirst region merging indicator between target preliminary regions
Figure 548392DEST_PATH_IMAGE022
The larger, thepA target preliminary region andqthe more similar the individual target preliminary regions, the more likely they can be merged together.
And sixthly, determining a second region merging index between the two target preliminary regions according to every two target preliminary regions in the target preliminary region set.
For example, this step may include the following sub-steps:
and a first substep of translating the two target preliminary regions to make the distance between the two target preliminary regions zero, and obtaining fitting regions corresponding to the two target preliminary regions.
The fitting union region corresponding to the two target preliminary regions may be a region obtained by merging the two target preliminary regions.
For example, when the distance between the two target preliminary regions is greater than zero, the two target preliminary regions are translated to make the distance between the two target preliminary regions zero, and the fitting regions corresponding to the two target preliminary regions are obtained.
For another example, when the distance between the two target preliminary regions is zero, the two target preliminary regions do not need to be translated, and the two target preliminary regions can be directly merged together to obtain a fit region corresponding to the two target preliminary regions. As shown in fig. 3, the distance between the first target preliminary region 301 and the second target preliminary region 302 is zero. The first target preliminary region 301 and the second target preliminary region 302 may be two target preliminary regions.
And a second sub-step of determining a second region merging indicator between the two target preliminary regions according to the two target preliminary regions and the corresponding fitted regions of the two target preliminary regions.
For example, the formula for determining the correspondence between the second region merging indicators of the two target preliminary regions may be:
Figure 600793DEST_PATH_IMAGE026
wherein the content of the first and second substances,
Figure 289263DEST_PATH_IMAGE027
is the first in the set of target preliminary regionspA target preliminary region andqsecond region merging indicators between the target preliminary regions.exp() Is an exponential function with a natural constant as the base.
Figure 489431DEST_PATH_IMAGE028
Is the first in the set of target preliminary regionspA target preliminary region andqand the number of pixel points in the fitting and region corresponding to each target preliminary region.
Figure 265757DEST_PATH_IMAGE029
Is the first in the set of target preliminary regionspA target preliminary region andqand the number of edge pixel points included in the fitting and region corresponding to each target preliminary region.
Figure 687511DEST_PATH_IMAGE030
Is the first in the set of target preliminary regionspA target preliminary region andqand the perimeter of the minimum bounding rectangle corresponding to the fitting and region corresponding to each target preliminary region.
Figure 563195DEST_PATH_IMAGE031
Is the first in the set of target preliminary regionspThe number of pixel points in each target initial region.
Figure 312976DEST_PATH_IMAGE032
Is the first in the set of target preliminary regionspThe number of edge pixel points included in each target preliminary region.
Figure 548785DEST_PATH_IMAGE033
Is the first in the set of target preliminary regionspThe perimeter of the minimum bounding rectangle corresponding to each target preliminary region.
Figure 575778DEST_PATH_IMAGE034
Is the first in the set of target preliminary regionsqThe number of pixel points in each target initial region.
Figure 871630DEST_PATH_IMAGE035
Is the first in the set of target preliminary regionsqThe number of edge pixel points included in each target preliminary region.
Figure 780812DEST_PATH_IMAGE036
Is the first in the set of target preliminary regionsqThe perimeter of the minimum bounding rectangle corresponding to each target preliminary region.
Figure 633361DEST_PATH_IMAGE046
The closer to 0 the value of (A) is, thepA target preliminary region andqthe more similar the respective target preliminary regions.
Figure 92024DEST_PATH_IMAGE047
Can realize the pair
Figure 247193DEST_PATH_IMAGE046
Normalization is performed to facilitate comparison, an
Figure 892938DEST_PATH_IMAGE046
The larger the size of the tube, the larger the tube,
Figure 549179DEST_PATH_IMAGE047
the smaller, thepA target preliminary region andqsecond region merging indicator between target preliminary regions
Figure 613081DEST_PATH_IMAGE027
The larger, thepA target preliminary region andqthe more similar the individual target preliminary regions, the more likely they can be merged together.
And seventhly, determining an overall region merging index between every two target preliminary regions according to the first region merging index and the second region merging index between every two target preliminary regions in the target preliminary region set.
For example, the above formula for determining the correspondence between the two target preliminary regions and the overall region merging indicator may be:
Figure 188419DEST_PATH_IMAGE037
wherein, the first and the second end of the pipe are connected with each other,
Figure 818332DEST_PATH_IMAGE038
is the first in the set of target preliminary regionspA target preliminary region andqintegral region merging finger between target preliminary regionsAnd (4) marking.exp() Is an exponential function with a natural constant as the base.
Figure 199634DEST_PATH_IMAGE022
Is the first in the set of target preliminary regionspA target preliminary region andqand merging indexes of the first region between the target preliminary regions.
Figure 180360DEST_PATH_IMAGE027
Is the first in the set of target preliminary regionspA target preliminary region andqand second region merging indexes between the target preliminary regions.
Due to the fact thatpA target preliminary region andqfirst region merging indicator between target preliminary regions
Figure 474069DEST_PATH_IMAGE022
The larger, thepA target preliminary region andqthe more similar the individual target preliminary regions, the more likely they can be merged together. First, thepA target preliminary region andqsecond region merging indicator between target preliminary regions
Figure 359985DEST_PATH_IMAGE027
The larger, thepA target preliminary region andqthe more similar the individual target preliminary regions, the more likely they can be merged together. Therefore it is firstpA target preliminary region andqthe larger the overall region merging index between the target preliminary regions is, thepA target preliminary region andqthe more similar the individual target preliminary regions, the more likely they can be merged together. And is
Figure 967815DEST_PATH_IMAGE048
Realize the pair
Figure 255577DEST_PATH_IMAGE049
Normalization is performed to facilitate comparison, an
Figure 48084DEST_PATH_IMAGE049
The larger the size of the tube is,
Figure 906450DEST_PATH_IMAGE048
the larger. In addition, the overall region merging index integrates the first region merging index and the second region merging index, and the accuracy of determining the overall region merging index is improved.
And eighthly, dividing the two target preliminary regions into the same target preliminary region when the overall region merging index between the two target preliminary regions in the target preliminary region set is greater than a preset judgment threshold value.
The determination threshold may be a minimum overall region merging indicator that considers that the two target preliminary regions are not the same type of target preliminary region. For example, the decision threshold may be 0.75. The same kind of target preliminary region may include target preliminary regions having the same degree of salt and alkali.
And ninthly, determining the target preliminary region in the same target preliminary region as the target saline-alkali region in the target saline-alkali region set.
And S6, cutting each target saline-alkali area in the target saline-alkali area set, determining a target saline-alkali image corresponding to the target saline-alkali area, and obtaining a target saline-alkali image set.
In some embodiments, each target saline-alkali region in the target saline-alkali region set may be cut, and a target saline-alkali image corresponding to the target saline-alkali region is determined, so as to obtain a target saline-alkali image set.
As an example, as shown in fig. 4, a first target saline-alkali region 402 and a second target saline-alkali region 403 in a target normalized image 401 may be subjected to a cutting process, so as to obtain a first target saline-alkali image 404 and a second target saline-alkali image 405. Wherein the first target saline alkali area 402 and the second target saline alkali area 403 can be two target saline alkali areas. The first target saline-alkali image 404 and the second target saline-alkali image 405 may be two target saline-alkali images.
And S7, inputting each target saline-alkali image in the target saline-alkali image set into the trained saline-alkali degree classification network, and determining the saline-alkali degree corresponding to the target saline-alkali image through the saline-alkali degree classification network.
In some embodiments, each target saline-alkali image in the target saline-alkali image set may be input into a trained saline-alkali degree classification network, and the saline-alkali degree corresponding to the target saline-alkali image is determined through the saline-alkali degree classification network.
The saline-alkali degree classification network can be used for judging the saline-alkali degree corresponding to the target saline-alkali image. The saline-alkali degree classification network can be a classification recognition neural network.
As an example, the training process of the salinity classification network may include the following steps:
firstly, constructing a saline-alkali degree classification network.
This step can be implemented by the prior art, and is not described herein again.
And secondly, acquiring a sample saline-alkali image set.
The sample saline-alkali image in the sample saline-alkali image set can be a ground image with known saline-alkali degree. The label that sample saline-alkali image in above-mentioned sample saline-alkali image set corresponds can be saline-alkali degree.
And thirdly, training a saline-alkali degree classification network by using the sample saline-alkali image set and labels corresponding to the sample saline-alkali images in the sample saline-alkali image set to obtain the trained saline-alkali degree classification network.
Wherein, the loss function of the training saline-alkali degree classification network can be a cross entropy loss function.
This step can be implemented by the prior art, and is not described herein again.
And S8, generating target saline-alkali information representing the saline-alkali condition of the target ground area according to the saline-alkali degree corresponding to each target saline-alkali image in the target saline-alkali image set.
In some embodiments, the target saline-alkali information representing the saline-alkali condition of the target ground area may be generated according to the saline-alkali degree corresponding to each target saline-alkali image in the target saline-alkali image set.
As an example, the saline-alkali degrees corresponding to the target saline-alkali image in the target saline-alkali image set may be a moderate saline-alkali area and a severe saline-alkali area, respectively. The generated target saline-alkali information representing the saline-alkali condition of the target ground area can be 'the middle part of the target ground area is a severe saline-alkali area, the rest areas are moderate saline-alkali areas, and the saline-alkali degree is relatively severe'.
According to the saline-alkali geological identification method based on the unmanned aerial vehicle remote sensing image, the technical problem that the accuracy of saline-alkali identification of soil is low is solved by image processing of the remote sensing image to be detected, and the accuracy of saline-alkali identification of the soil is improved. Firstly, a target camera installed on a target unmanned aerial vehicle is used for acquiring a remote sensing image to be detected of a target ground area to be detected for saline-alkali conditions. The remote sensing image to be detected contains the information of the target ground area, so that the subsequent image processing of the remote sensing image to be detected can be facilitated, and the saline-alkali condition corresponding to the remote sensing image to be detected can be determined, so that the saline-alkali condition of the target ground area can be determined, and the accuracy of determining the saline-alkali condition of the target ground area can be improved. And secondly, carrying out image enhancement extraction processing on the remote sensing image to be detected to obtain a target remote sensing image. The image enhancement processing is carried out on the remote sensing image to be detected, the image contrast of the remote sensing image to be detected can be improved, the visualization degree of the remote sensing image to be detected is higher, and the subsequent image processing can be conveniently carried out on the remote sensing image to be detected. In practical situations, the remote sensing image to be detected obtained by the target camera often shoots not only a target ground area but also areas except the target ground area. However, the regions other than the target ground region do not need to be subjected to saline-alkali condition judgment, so that the enhanced image to be detected is extracted to obtain the target remote sensing image only shooting the target ground region, subsequent steps can be omitted in the regions other than the target ground region, the calculation amount can be reduced, the occupation of calculation resources can be reduced, and the efficiency of saline-alkali identification on the target ground region can be improved. And then, carrying out color space conversion processing on the target remote sensing image to obtain a color remote sensing image. Since the color remote sensing image may be an HSV image. HSV images tend to have less correlation between the three channels of H (Hue), S (Saturation) and V (Value), and tend to be more visually consistent. Subsequent analysis of H, S and V three channels can be facilitated. And then, carrying out region division optimization on the color remote sensing image to obtain a primary optimal saline-alkali region set. And continuing to merge and divide the initial optimal saline-alkali regions in the initial optimal saline-alkali region set to obtain a target saline-alkali region set. In practical situations, the saline-alkali degree corresponding to each position in the target ground area is often more than one, that is, the target ground area may often include a plurality of areas with different saline-alkali degrees. Therefore, the color remote sensing images are subjected to area division, optimization and combination, a plurality of target saline-alkali areas with different corresponding saline-alkali degrees can be obtained, and the saline-alkali condition of the target ground area can be conveniently and accurately judged subsequently. And then, cutting each target saline-alkali area in the target saline-alkali area set, determining a target saline-alkali image corresponding to the target saline-alkali area, and obtaining a target saline-alkali image set. The different target saline-alkali regions of the corresponding saline-alkali conditions are cut off to obtain the target saline-alkali images in one-to-one correspondence with the saline-alkali degrees, so that the subsequent analysis of the multiple saline-alkali degrees corresponding to the target ground regions and the position of each saline-alkali degree in the target ground regions can be facilitated. Then, each target saline-alkali image in the target saline-alkali image set is input to a trained saline-alkali degree classification network, and the saline-alkali degree corresponding to the target saline-alkali image is determined through the saline-alkali degree classification network. And finally, generating target saline-alkali information representing the saline-alkali condition of the target ground area according to the saline-alkali degree corresponding to each target saline-alkali image in the target saline-alkali image set. Therefore, the method and the device solve the technical problem of low accuracy of saline-alkali identification of the soil by image processing of the remote sensing image to be detected, and improve the accuracy of the saline-alkali identification of the soil.
The above embodiments are only used to illustrate the technical solutions of the present application, and not to limit the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.

Claims (7)

1. A saline-alkali geological identification method based on unmanned aerial vehicle remote sensing images is characterized by comprising the following steps:
acquiring a remote sensing image to be detected of a target ground area of a saline-alkali condition to be detected through a target camera installed on a target unmanned aerial vehicle;
carrying out image enhancement extraction processing on the remote sensing image to be detected to obtain a target remote sensing image;
carrying out color space conversion processing on the target remote sensing image to obtain a color remote sensing image;
carrying out region division optimization on the color remote sensing image to obtain a preliminary optimal saline-alkali region set;
merging and dividing the initial optimal saline-alkali regions in the initial optimal saline-alkali region set to obtain a target saline-alkali region set;
cutting each target saline-alkali area in the target saline-alkali area set, and determining a target saline-alkali image corresponding to the target saline-alkali area to obtain a target saline-alkali image set;
inputting each target saline-alkali image in the target saline-alkali image set into a trained saline-alkali degree classification network, and determining the saline-alkali degree corresponding to the target saline-alkali image through the saline-alkali degree classification network;
generating target saline-alkali information representing the saline-alkali condition of the target ground area according to the saline-alkali degree corresponding to each target saline-alkali image in the target saline-alkali image set;
the method comprises the following steps of carrying out region division optimization on the color remote sensing image to obtain a primary optimal saline-alkali region set, wherein the method comprises the following steps:
mapping the color remote sensing image into a target undirected graph;
determining the initial optimal saline-alkali area set through an optimization algorithm according to the target undirected graph;
the objective function of the optimization algorithm is:
Figure 34888DEST_PATH_IMAGE002
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE003
is the objective function of the optimization algorithm,Fis the value of the objective function of the optimization algorithm,
Figure 109154DEST_PATH_IMAGE004
is a first objective function of the first set of functions,His the value of the first objective function,
Figure DEST_PATH_IMAGE005
，
Figure 918978DEST_PATH_IMAGE006
so as to make
Figure DEST_PATH_IMAGE007
Counting at the bottom
Figure 53288DEST_PATH_IMAGE008
The number of the pairs is logarithmic,
Figure 135513DEST_PATH_IMAGE008
is the second in the target undirected graphiPixel point and the secondjThe probability of a transition between individual pixel points,
Figure DEST_PATH_IMAGE009
，
Figure 154416DEST_PATH_IMAGE010
，
Figure DEST_PATH_IMAGE011
is the second in the target undirected graphiThe sum of the edge weights between each pixel point and each pixel point in the target undirected graph,Iis the number of pixel points in the target undirected graphiPixel point and the secondjThe edge weight between each pixel point is
Figure 818747DEST_PATH_IMAGE012
，exp() Is an exponential function with a natural constant as the base,
Figure DEST_PATH_IMAGE013
is the second in the target undirected graphiA pixel point and a secondjThe euclidean distance between the individual pixel points,
Figure 123957DEST_PATH_IMAGE014
is the second in the target undirected graphiHue value corresponding to each pixel point and the secondjThe difference in hue value corresponding to each pixel point,
Figure DEST_PATH_IMAGE015
is the second in the target undirected graphiSaturation value corresponding to each pixel point and the secondjThe difference of the saturation values corresponding to each pixel point,
Figure 506528DEST_PATH_IMAGE016
is the second in the target undirected graphiBrightness value corresponding to each pixel point and the secondjThe difference in luminance values corresponding to each pixel point,Uis the parameter of the model and is,
Figure DEST_PATH_IMAGE017
is the coefficient of the model that is,
Figure 312810DEST_PATH_IMAGE018
is the second objective function of the first function,Objis the value of the second objective function,
Figure DEST_PATH_IMAGE019
is the second in the target undirected graphkThe number of pixel points within a sub-region, a sub-region being a region in the target undirected graph,
Figure 819928DEST_PATH_IMAGE020
and
Figure DEST_PATH_IMAGE021
are respectively the second in the target undirected graphkThe length and width of the smallest circumscribed rectangle corresponding to a sub-region,Kthe number of sub-regions obtained by dividing the target undirected graph is obtained;
the preliminary optimal saline-alkali area in the preliminary optimal saline-alkali area set is merged and divided to obtain a target saline-alkali area set, and the method comprises the following steps:
normalizing the target remote sensing image to obtain a target normalized image;
determining a target preliminary region corresponding to each preliminary optimal saline-alkali region in the preliminary optimal saline-alkali region set according to the position of each preliminary optimal saline-alkali region in the color remote sensing image to obtain a target preliminary region set, wherein the target preliminary region in the target preliminary region set is a region in the target normalized image;
dividing a channel value of each channel in a target number of channels of the target normalized image into a preset number of channel levels to obtain a channel level set, wherein each channel of the target normalized image corresponds to the preset number of channel levels, and the number of the channel levels in the channel level set is the power of the preset number of target number;
determining a color channel histogram corresponding to each target preliminary region in the target preliminary region set;
determining a first region merging index between every two target preliminary regions according to the channel level set and color channel histograms corresponding to the two target preliminary regions in the target preliminary region set;
determining a second region merging index between every two target preliminary regions according to every two target preliminary regions in the target preliminary region set;
determining an overall region merging index between every two target preliminary regions according to a first region merging index and a second region merging index between every two target preliminary regions in the target preliminary region set;
when the overall area merging index between two target preliminary areas in the target preliminary area set is larger than a preset judgment threshold value, dividing the two target preliminary areas into the same type of target preliminary area;
and determining the target preliminary region in the same target preliminary region as a target saline-alkali region in the target saline-alkali region set.
2. The method for identifying the saline-alkali geology based on the unmanned aerial vehicle remote sensing image according to claim 1, wherein the formula for determining the correspondence of the first region merging index between the two target preliminary regions is as follows:
Figure DEST_PATH_IMAGE023
wherein the content of the first and second substances,
Figure 374669DEST_PATH_IMAGE024
is the first in the target preliminary region setpA target preliminary region andqa first region merging indicator between the target preliminary regions,Cis the number of channel levels in the set of channel levels,
Figure DEST_PATH_IMAGE025
is the first in the target preliminary region setpA first one of the channel level sets included in the corresponding color channel histogram for each target preliminary regioncThe histogram distribution values at the level of one channel,
Figure 978957DEST_PATH_IMAGE026
is the first in the target preliminary region setqA first one of the channel level sets included in the corresponding color channel histogram for each target preliminary regioncThe histogram distribution values at the level of one channel,
Figure DEST_PATH_IMAGE027
is a preset number greater than 0.
3. The method for identifying the saline-alkali geology based on the unmanned aerial vehicle remote sensing image according to claim 1, wherein the determining a second region merging index between two target preliminary regions according to each two target preliminary regions in the target preliminary region set comprises:
translating the two target preliminary regions to enable the distance between the two target preliminary regions to be zero, and obtaining fitting regions corresponding to the two target preliminary regions;
and determining a second region merging index between the two target preliminary regions according to the two target preliminary regions and the corresponding fitting regions of the two target preliminary regions.
4. The method for identifying the saline-alkali geology based on the unmanned aerial vehicle remote sensing image according to claim 3, wherein the formula for determining the correspondence of the second region merging index between the two target preliminary regions is as follows:
Figure DEST_PATH_IMAGE029
wherein the content of the first and second substances,
Figure 667558DEST_PATH_IMAGE030
is the first in the target preliminary region setpA target preliminary region andqsecond region merging indicators between the target preliminary regions,exp() Is an exponential function with a natural constant as the base,
Figure DEST_PATH_IMAGE031
is the first in the target preliminary region setpA target preliminary region andqthe number of pixel points in the fitting and region corresponding to each target preliminary region,
Figure 40902DEST_PATH_IMAGE032
is the first in the target preliminary region setpA target preliminary region andqthe number of edge pixel points included in the fitting and region corresponding to each target preliminary region,
Figure DEST_PATH_IMAGE033
is the first in the target preliminary region setpA target preliminary region andqthe perimeter of the minimum bounding rectangle corresponding to the fitting and region corresponding to each target preliminary region,
Figure 687915DEST_PATH_IMAGE034
is the first in the target preliminary region setpThe number of pixel points within the individual target initialization region,
Figure DEST_PATH_IMAGE035
is the first in the target preliminary region setpThe number of edge pixels included in each target preliminary region,
Figure 513919DEST_PATH_IMAGE036
is the first in the target preliminary region setpThe circumference of the minimum bounding rectangle corresponding to each target preliminary regionThe length of the utility model is long,
Figure DEST_PATH_IMAGE037
is the first in the target preliminary region setqThe number of pixel points within the individual target initialization region,
Figure 537370DEST_PATH_IMAGE038
is the first in the target preliminary region setqThe number of edge pixels included in each target preliminary region,
Figure DEST_PATH_IMAGE039
is the first in the target preliminary region setqThe perimeter of the minimum bounding rectangle corresponding to each target preliminary region.
5. The method for identifying the saline-alkali geology based on the unmanned aerial vehicle remote sensing image according to claim 1, wherein the formula for determining the correspondence of the overall region merging index between the two target preliminary regions is as follows:
Figure DEST_PATH_IMAGE041
wherein, the first and the second end of the pipe are connected with each other,
Figure 968483DEST_PATH_IMAGE042
is the first in the target preliminary region setpA target preliminary region andqthe overall region merging indicators between the target preliminary regions,exp() Is an exponential function with a natural constant as the base,
Figure 973348DEST_PATH_IMAGE024
is the first in the target preliminary region setpA target preliminary region andqa first region merging indicator between the target preliminary regions,
Figure 224332DEST_PATH_IMAGE030
is the first in the target preliminary region setpA target preliminary region andqand second region merging indexes between the target preliminary regions.
6. The method for identifying the saline-alkali geology based on the unmanned aerial vehicle remote sensing image according to claim 1, wherein the step of performing image enhancement extraction processing on the remote sensing image to be detected to obtain a target remote sensing image comprises the following steps:
carrying out image enhancement processing on the remote sensing image to be detected through an image enhancement algorithm to obtain an enhanced image to be detected;
and extracting a target ground area of the enhanced image to be detected to obtain the target remote sensing image.
7. The method for identifying the saline-alkali geology based on the unmanned aerial vehicle remote sensing image according to claim 1, wherein the training process of the saline-alkali degree classification network comprises the following steps:
constructing a saline-alkali degree classification network;
obtaining a sample saline-alkali image set, wherein a label corresponding to a sample saline-alkali image in the sample saline-alkali image set is the saline-alkali degree;
and training the saline-alkali degree classification network by utilizing the sample saline-alkali image set and the label corresponding to each sample saline-alkali image in the sample saline-alkali image set to obtain the trained saline-alkali degree classification network.
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