CN114445717B - Remote sensing image processing method and system for land resource management - Google Patents
Remote sensing image processing method and system for land resource management Download PDFInfo
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Abstract
The invention relates to the technical field of image processing, in particular to a remote sensing image processing method and a remote sensing image processing system for land resource management, wherein the method comprises the following steps: marking the ground object on the remote sensing image, and determining a sample point of the initial sample classification of the ground object; acquiring a surface feature spectrum curve corresponding to a surface feature, and performing false color synthesis on wave bands corresponding to a peak point, a valley point and a maximum curvature point in the surface feature spectrum curve to obtain a false color image; obtaining a false color band combination of each band of a false color image corresponding to an RGB channel, and reclassifying the sample points according to the variation consistency of the same type of sample points in different false color band combinations to obtain a middle sample point; determining a representative test vector according to the intermediate sample point; and determining a final sample point by using the representative test vector as a parameter of the PPI algorithm, and performing final sample classification on the remote sensing image according to the final sample point to obtain a classification result.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a remote sensing image processing method and system for land resource management.
Background
The land resource management is generally to collect a remote sensing image through a satellite, and to reflect the land coverage utilization, the environment monitoring, the agricultural estimation and the forest fire according to the remote sensing image, so that the land type identification in the remote sensing image is an essential part, and the corresponding land resource can be accurately managed from the remote sensing image only by accurately classifying the land type.
With the development of computer technology, the method of identifying surface features by manual visual inspection has been gradually eliminated, and the current method of identifying surface features usually adopts a supervision classification method, i.e. firstly, sample points of different types of surface features on an image are selected, and then, a computer classifies pixels on the image according to the similarity between other pixels and the sample points.
However, the selection of classification samples is based on RGB images, so the accuracy of the supervision samples and the purity of the pixels have a great influence on the classification results, and meanwhile, when the reflectivities of three RGB bands of different land types are similar, the situations of wrong classification and missing classification of the land types occur.
Therefore, a method and a system for processing remote sensing images for land resource management are needed.
Disclosure of Invention
The invention provides a remote sensing image processing method and system for land resource management, which aim to solve the existing problems.
The invention relates to a remote sensing image processing method and a system for land resource management, which adopt the following technical scheme: the method comprises the following steps:
marking each ground object on the remote sensing image, and selecting a corresponding number of pixels as sample points of the initial sample classification for each ground object;
acquiring a surface feature spectrum curve corresponding to each surface feature, determining a peak point and a valley point in the surface feature spectrum curve, calculating a maximum curvature point of the surface feature spectrum curve, and performing false color synthesis on a peak point corresponding waveband, a valley point corresponding waveband and a maximum curvature point corresponding waveband in the surface feature spectrum curve to obtain a false color image;
obtaining a pseudo color band combination corresponding to each band in a pseudo color image in an RGB channel, reclassifying sample points according to the variation consistency of similar sample points in different pseudo color band combinations to obtain intermediate sample points, and further reclassifying the sample points according to the variation consistency of the similar sample points in different pseudo color band combinations to obtain the intermediate sample points, wherein the step of reclassifying the sample points according to the variation consistency of the similar sample points in different pseudo color band combinations to obtain the intermediate sample points comprises the following steps: calculating the tone value and the gray value of the same type of sample points in each false color band combination; calculating a first direction chain code corresponding to the tone value of each sample point in the same type of sample points and a second direction chain code corresponding to the gray value; calculating a first similarity of first direction chain codes of different sample points in the same type of initial samples and a second similarity of second direction chain codes; reclassifying the sample points according to the first similarity and the second similarity to obtain intermediate sample points; the step of calculating a first direction chain code corresponding to the tone value of each sample point in the same type of sample points and a second direction chain code corresponding to the gray value comprises the following steps: transferring the image of the first false color band combination to an HIS space, extracting H, namely a hue channel, and acquiring a first hue value corresponding to the image of the first false color band combination according to the hue channel; calculating a second hue value corresponding to the image of the second false color band combination; calculating a direction value of a connection line of the second hue value and the first hue value; calculating a chain code containing 5 direction values and 6 hue values by combining all false color wave bands, wherein the chain code is called a first direction chain code; calculating a second direction chain code according to the method for calculating the first direction chain code;
Calculating the similar distance between every two middle sample points in each similar middle sample point, and determining a representative test vector according to the similar middle sample point with the largest similar distance;
and determining a final sample point by using the representative test vector as a parameter of the PPI algorithm, and performing final sample classification on the remote sensing image according to the final sample point to obtain a classification result.
Further, the step of calculating the maximum curvature point of the surface feature spectrum curve comprises:
acquiring each point on a surface feature spectrum curve;
acquiring principal component directions of the points, wherein each principal component direction corresponds to a characteristic value;
acquiring a maximum principal component direction corresponding to the maximum characteristic value, and recording as a connected domain principal direction;
and calculating the rear principal component direction after each point on the ground object spectrum curve is removed, acquiring the difference value between the rear principal component direction and the maximum principal component direction, and taking the point corresponding to the maximum difference value as the maximum curvature point.
Further, the step of obtaining the false color band combination corresponding to each band in the false color image to the RGB channel includes:
recording wave bands corresponding to each peak point, wave bands corresponding to each valley point and wave bands corresponding to the maximum curvature point in the surface feature spectrum curve as a wave band, b wave band and c wave band in sequence;
RGB is used as a fixed channel, and a wave band a, a wave band b and a wave band c are respectively corresponding to the RGB channel to obtain 6 false color wave band combinations.
Further, the step of reclassifying the sample points according to the first similarity and the second similarity to obtain intermediate sample points comprises:
acquiring similar sample points of which the first similarity of every two first direction chain codes is greater than 90%;
calculating average first direction chain codes of all similar sample points with the first similarity larger than 90%;
calculating average direction chain codes of other types of sample points according to a method for calculating average first direction chain codes of similar sample points;
calculating the first final similarity of the average direction chain codes of the first direction chain codes corresponding to the sample points with the similarity smaller than 90% in the sample points of the same type and the sample points of other types;
acquiring a second final similarity corresponding to the second similarity according to the mode of acquiring the first final similarity according to the first similarity;
and obtaining sample points corresponding to the first direction chain codes with the first final similarity larger than 90% and sample points corresponding to the second direction chain codes with the second final similarity larger than 90%, adding the sample points corresponding to the first direction chain codes with the first final similarity larger than 90% and the sample points corresponding to the second direction chain codes with the second final similarity larger than 90% into the corresponding sample points of other classes, completing the re-classification of the sample points, obtaining the classified sample points and recording the classified sample points as intermediate sample points.
Further, the step of calculating the similarity distance between every two middle sample points in each similar middle sample point and determining the representative test vector according to the similar middle sample point with the maximum similarity distance includes:
obtaining the similarity between every two middle sample points in the same kind of middle sample points, wherein the similarity is the similar distance;
adding the similar distances between each intermediate sample point and all other intermediate sample points in the same type of intermediate sample points to obtain an intermediate sample point corresponding to the maximum distance sum value;
and taking the intermediate sample points corresponding to the maximum distance sum value as pixels with highest purity in each type of intermediate sample points, and taking vector values of the pixels as representative test vectors in an n-dimensional sample space, wherein n is the number of wave bands corresponding to the satellite for acquiring the remote sensing image.
Further, the step of determining the final sample point using the representative test vector as a parameter of the PPI algorithm includes:
taking the representative test vector as a parameter of a PPI algorithm, and processing the remote sensing image by utilizing the PPI algorithm of the representative test vector to obtain a PPI image;
and selecting the pixel point with the maximum pixel gray value in each ground feature in the PPI image as a final sample point.
The invention also discloses a processing system of the remote sensing image processing method for land resource management, which comprises the following steps:
the information extraction unit is used for marking each ground object on the remote sensing image, and each ground object selects a corresponding number of pixels as sample points of the initial sample classification;
the image synthesis unit is used for acquiring a ground object spectrum curve corresponding to each ground object in the spectrum library, determining a peak point and a valley point in the ground object spectrum curve, calculating a maximum curvature point of the ground object spectrum curve, and performing false color synthesis on a wave band corresponding to the peak point, a wave band corresponding to the valley point and a wave band corresponding to the maximum curvature point in the ground object spectrum curve to obtain a false color image;
the first information processing unit is used for acquiring a false color waveband combination in a false color image, wherein each waveband corresponds to an RGB channel, and calculating the hue value and the gray value of the same type of sample point in each false color waveband combination;
the second information processing unit is used for reclassifying the sample points according to the consistency of the hue value changes and the consistency of the gray value changes of the same type of sample points in different false color band combinations;
the third information processing unit is used for calculating the maximum homogeneous sample point with the maximum distance from each sample point to the homogeneous sample points after reclassification and determining a representative test vector according to the maximum homogeneous sample point;
And the classification unit is used for determining a final sample point according to the representative test vector as a parameter of the PPI algorithm, and performing final sample classification on the remote sensing image according to the final sample point to obtain a classification result.
The invention has the beneficial effects that: the invention relates to a remote sensing image processing method and a system for land resource management, which are used for carrying out sample classification on remote sensing images by firstly carrying out sample points of initial sample classification on each ground feature, reclassifying the sample points by utilizing the change consistency of similar sample points in different false color wave band combinations, determining a representative test vector through an intermediate sample point, determining a final sample point by taking the representative test vector as a parameter of a PPI algorithm to carry out sample classification on the remote sensing images, reducing the calculated amount of the PPI algorithm, separating the ground features of other classes mixed in the sample points, obtaining the final sample points of different ground features, realizing the sample classification, improving the classification precision and reducing the occurrence of wrong-classification and missing-classification conditions.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of 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 for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flowchart illustrating the general steps of an embodiment of a method and system for remote sensing image processing for land resource management according to the present invention;
fig. 2 is a flowchart of the step S2 of obtaining the maximum curvature point of the feature spectrum curve;
FIG. 3 is a flow chart of the intermediate sample acquisition of FIG. 1;
FIG. 4 is a flow chart of the reclassification in FIG. 2;
FIG. 5 is a flow chart of the method of FIG. 1 for obtaining representative test vectors.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
An embodiment of the remote sensing image processing method for land resource management of the invention is shown in fig. 1, and the method comprises the following steps:
s1, before classifying the surface features, researchers determine the surface features to be classified, namely the surface features on the remote sensing image need to be classified into which categories, then mark the corresponding surface feature categories on the image, and each surface feature selects a corresponding number of pixels as sample points.
S2, selecting the sample on the RGB channel corresponding to the same wave band, if there is the ground object with similar reflectivity on the corresponding wave band in the classified ground object, because the ground object with similar reflectivity is represented as the similar color and grey value on the image, the wrong division is easy to generate, in order to avoid directly influencing the subsequent classification result and ensure the purity of the manually selected sample point, firstly obtaining the ground object spectrum curve corresponding to each ground object in the spectrum library, determining the peak point and the valley point in the ground object spectrum curve, calculating the maximum curvature point of the ground object spectrum curve, and carrying out false color synthesis on the peak point corresponding wave band, the valley point corresponding wave band and the maximum curvature point corresponding wave band in the ground object spectrum curve to obtain the false color image.
Specifically, as shown in fig. 2, the step of calculating the maximum curvature point of the surface feature spectrum curve includes: s21, firstly, acquiring each point on the ground object spectrum curve; s22, acquiring principal component directions of the points by using a PCA algorithm to obtain a plurality of principal component directions, wherein each principal component direction is a 2-dimensional unit vector and corresponds to a characteristic value; s23, acquiring the maximum principal component direction corresponding to the maximum characteristic value, and recording as the principal direction of the connected domain; and S24, calculating the rear principal component direction after each point on the earth feature spectrum curve is removed, acquiring the difference value between the rear principal component direction and the maximum principal component direction, and taking the point corresponding to the maximum difference value as the maximum curvature point.
S3, obtaining false color band combination corresponding to each band in the false color image in the RGB channel, and reclassifying the sample points to obtain intermediate sample points according to tone value variation consistency of the same type of sample points in different false color band combinations.
Specifically, the step of S31, acquiring a false color band combination in which each band in the false color image corresponds to a false color band in the RGB channel, includes: s311, recording a wave band corresponding to each peak point, a wave band corresponding to each valley point and a wave band corresponding to the maximum curvature point in the surface feature spectrum curve as an a wave band, a b wave band and a c wave band in sequence; s312, using RGB as a fixed channel, and respectively corresponding the a wave band, the b wave band and the c wave band to the RGB channel to obtain 6 false color wave band combinations.
S32, because the similar reflectivity of different ground objects in the RGB corresponding wave bands can cause that other ground objects are mixed in the same ground object sample point when selecting the classification sample, thereby affecting the accuracy of the classification result, in order to ensure the accuracy of the classification result, because the change conditions of the tone value and the gray value of the same ground object in the false color wave band combinations should be consistent, the step of reclassifying the sample point according to the tone value change consistency of the same sample point in different false color wave band combinations to obtain the intermediate sample point comprises: as shown in fig. 3, in particular, S321, calculating hue values and gray values of the same-type sample points in each pseudo-color band combination; s322, calculating a first direction chain code corresponding to the tone value of each sample point in the same type of sample points and a second direction chain code corresponding to the gray value; specifically, the image of the first false color band combination is transferred to the HIS space, H, namely a hue channel, is extracted, and a first hue value corresponding to the image of the first false color band combination is obtained according to the hue channel; calculating a second hue value corresponding to the image of the second false color band combination; calculating a direction value of a connection line of the second hue value and the first hue value; calculating a chain code containing 5 direction values and 6 hue values by combining all false color wave bands, wherein the chain code is called a first direction chain code; s323, calculating a second direction chain code according to the method for calculating the first direction chain code; s324, calculating a first similarity of the first direction chain codes and a second similarity of the second direction chain codes of different sample points in the same type of sample points; s325, reclassifying the sample points according to the first similarity and the second similarity to obtain intermediate sample points, specifically, as shown in fig. 4, S3251, obtaining similar sample points with the first similarity of every two first direction chain codes being greater than 90%; s3252, calculating average first direction chain codes of all similar sample points with the first similarity larger than 90%; s3253, calculating average direction chain codes of other types of sample points according to a method for calculating average first direction chain codes of similar sample points; s3254, calculating the first final similarity of the first direction chain codes corresponding to the sample points with the similarity smaller than 90% in the same type of sample points and the average direction chain codes of the other type of sample points; s3255, acquiring a second final similarity corresponding to the second similarity according to the mode of acquiring the first final similarity according to the first similarity; s3256, obtaining sample points corresponding to first direction chain codes with the first final similarity larger than 90% and sample points corresponding to second direction chain codes with the second final similarity larger than 90%, adding the sample points corresponding to the first direction chain codes with the first final similarity larger than 90% and the sample points corresponding to the second direction chain codes with the second final similarity larger than 90% into the corresponding sample points of other categories, completing reclassification of the sample points, obtaining the classified sample points and marking the sample points as intermediate sample points.
S4, calculating the similar distance between every two middle sample points in each middle sample point of the same kind, and determining a representative test vector according to the middle sample point of the same kind with the largest similar distance; as shown in fig. 5, specifically, S41, obtaining similarity between every two middle sample points in the middle sample points of the same type, where the similarity is a similar distance; s42, summing the similar distances between each intermediate sample point and all other intermediate sample points in the same type of intermediate sample points to obtain an intermediate sample point corresponding to the maximum distance sum value; and S43, taking the middle sample point corresponding to the maximum distance sum value as the pixel with the highest purity in each type of middle sample point, and taking the vector value of the pixels as a representative test vector in an n-dimensional sample space, wherein n is the number of the wave bands corresponding to the satellite for acquiring the remote sensing image, for example, if an OLI sensor on a Landsat-8 satellite has 9 wave bands, n = 9.
S5, determining a final sample point by taking the representative test vector as a parameter of the PPI algorithm, carrying out final sample classification on the remote sensing image according to the final sample point to obtain a classification result, specifically, taking the representative test vector as a parameter of the PPI algorithm, and processing the remote sensing image by using the PPI algorithm of the representative test vector to obtain a PPI image; and selecting the pixel point with the maximum pixel grey value in each ground feature in the PPI image as a final sample point, and classifying the final sample point by using a classifier to obtain a classification result.
The invention also discloses a processing system of the remote sensing image processing method for land resource management, which comprises the following steps: the remote sensing image classification system comprises an information extraction unit, an image synthesis unit, a first information processing unit, a second information processing unit, a third information processing unit and a classification unit, wherein the information extraction unit is used for marking each ground object on a remote sensing image, and each ground object selects a corresponding number of pixels as sample points for initial sample classification; the image synthesis unit is used for acquiring a ground object spectrum curve corresponding to each ground object in the spectrum library, determining a peak point and a valley point in the ground object spectrum curve, calculating a maximum curvature point of the ground object spectrum curve, and performing false color synthesis on a wave band corresponding to the peak point, a wave band corresponding to the valley point and a wave band corresponding to the maximum curvature point in the ground object spectrum curve to obtain a false color image; the first information processing unit is used for acquiring a false color waveband combination in a false color image, wherein each waveband corresponds to an RGB channel, and calculating the hue value and the gray value of the same type of sample point in each false color waveband combination; the second information processing unit is used for reclassifying the sample points according to the consistency of the hue value changes and the consistency of the gray value changes of the same type of sample points in different false color band combinations; the third information processing unit is used for calculating the maximum homogeneous sample point with the maximum distance from each sample point to the homogeneous sample points after reclassification and determining a representative test vector according to the maximum homogeneous sample point; and the classification unit is used for determining a final sample point according to the representative test vector as a parameter of the PPI algorithm, and performing final sample classification on the remote sensing image according to the final sample point to obtain a classification result.
In summary, the invention provides a method and a system for processing remote sensing images for land resource management, which perform sample classification on each ground object by using sample points for initial sample classification, reclassify the sample points by using the variation consistency of the same type of sample points in different false color band combinations, determine a representative test vector by using intermediate sample points, determine a final sample point by using the representative test vector as a parameter of a PPI algorithm to perform sample classification on the remote sensing images, reduce the calculated amount of the PPI algorithm, separate the ground objects mixed with other types in the sample points, obtain the final sample points of different ground objects, and realize sample classification, thereby improving the classification precision and reducing the occurrence of wrong-classification and missing-classification conditions.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and should not be taken as limiting the scope of the present invention, which is intended to cover any modifications, equivalents, improvements, etc. within the spirit and scope of the present invention.
Claims (7)
1. A remote sensing image processing method for land resource management is characterized by comprising the following steps:
marking each ground object on the remote sensing image, and selecting a corresponding number of pixels as sample points of the initial sample classification for each ground object;
Acquiring a surface feature spectrum curve corresponding to each surface feature, determining a peak point and a valley point in the surface feature spectrum curve, calculating a maximum curvature point of the surface feature spectrum curve, and performing false color synthesis on a peak point corresponding waveband, a valley point corresponding waveband and a maximum curvature point corresponding waveband in the surface feature spectrum curve to obtain a false color image;
the method comprises the steps of obtaining a false color band combination corresponding to each band in a false color image in an RGB channel, reclassifying sample points according to the variation consistency of similar sample points in different false color band combinations to obtain a middle sample point, and reclassifying the sample points according to the variation consistency of the similar sample points in different false color band combinations to obtain the middle sample point: calculating the tone value and the gray value of the same type of sample points in each false color band combination; calculating a first direction chain code corresponding to the tone value of each sample point in the same type of sample points and a second direction chain code corresponding to the gray value; calculating a first similarity of first direction chain codes of different sample points in the same type of initial samples and a second similarity of second direction chain codes; reclassifying the sample points according to the first similarity and the second similarity to obtain intermediate sample points; the step of calculating a first direction chain code corresponding to the hue value of each sample point in the same type of sample points and a second direction chain code corresponding to the gray value comprises the following steps: transferring the image of the first false color band combination to an HIS space, extracting H, namely a hue channel, and acquiring a first hue value corresponding to the image of the first false color band combination according to the hue channel; calculating a second hue value corresponding to the image of the second false color band combination; calculating a direction value of a connection line of the second hue value and the first hue value; calculating a chain code containing 5 direction values and 6 hue values by combining all false color wave bands, wherein the chain code is called a first direction chain code; calculating a second direction chain code according to the method for calculating the first direction chain code;
Calculating the similar distance between every two middle sample points in each similar middle sample point, and determining a representative test vector according to the similar middle sample point with the largest similar distance;
and determining a final sample point by using the representative test vector as a parameter of the PPI algorithm, and performing final sample classification on the remote sensing image according to the final sample point to obtain a classification result.
2. A method of remote sensing image processing for land resource management as recited in claim 1 in which the step of calculating the point of maximum curvature of the feature spectral curve comprises:
acquiring each point on a ground object spectrum curve;
acquiring principal component directions of the points, wherein each principal component direction corresponds to a characteristic value;
acquiring a maximum principal component direction corresponding to the maximum characteristic value, and recording as a connected domain principal direction;
and calculating the rear principal component direction after each point on the ground object spectrum curve is removed, acquiring the difference value between the rear principal component direction and the maximum principal component direction, and taking the point corresponding to the maximum difference value as the maximum curvature point.
3. A method as claimed in claim 1, wherein the step of obtaining a false color band combination corresponding to each band in the false color image in the RGB channel comprises:
Recording the corresponding wave band of each peak point, the corresponding wave band of each valley point and the corresponding wave band of the maximum curvature point in the surface feature spectrum curve as a wave band a, a wave band b and a wave band c in sequence;
RGB is used as a fixed channel, and a wave band a, a wave band b and a wave band c are respectively corresponding to the RGB channel to obtain 6 false color wave band combinations.
4. The method of claim 1, wherein the step of reclassifying the sample points according to the first similarity and the second similarity to obtain intermediate sample points comprises:
acquiring similar sample points of which the first similarity of every two first direction chain codes is greater than 90%;
calculating average first direction chain codes of all similar sample points with the first similarity larger than 90%;
calculating average direction chain codes of other types of sample points according to a method for calculating average first direction chain codes of similar sample points;
calculating the first final similarity of the first direction chain codes corresponding to the sample points with the similarity smaller than 90% in the same type of sample points and the average direction chain codes of the sample points in other types;
acquiring a second final similarity corresponding to the second similarity according to the mode of acquiring the first final similarity according to the first similarity;
And obtaining sample points corresponding to the first direction chain codes with the first final similarity larger than 90% and sample points corresponding to the second direction chain codes with the second final similarity larger than 90%, adding the sample points corresponding to the first direction chain codes with the first final similarity larger than 90% and the sample points corresponding to the second direction chain codes with the second final similarity larger than 90% into the corresponding sample points of other classes, completing the re-classification of the sample points, obtaining the classified sample points and recording the classified sample points as intermediate sample points.
5. The remote sensing image processing method for land resource management according to claim 1, wherein the step of calculating the similar distance between every two middle sample points in each similar middle sample point, and determining the representative test vector according to the similar middle sample point with the largest similar distance comprises:
obtaining the similarity between every two middle sample points in the middle sample points of the same kind, wherein the similarity is the similar distance;
summing the similar distances between each intermediate sample point and all other intermediate sample points in the same type of intermediate sample points to obtain an intermediate sample point corresponding to the maximum distance sum value;
and taking the intermediate sample points corresponding to the maximum distance sum value as pixels with highest purity in each type of intermediate sample points, and taking vector values of the pixels as representative test vectors in an n-dimensional sample space, wherein n is the number of wave bands corresponding to the satellite for acquiring the remote sensing image.
6. The remote sensing image processing method for land resource management according to claim 1, wherein the step of determining a final sample point using a representative test vector as a parameter of the PPI algorithm comprises:
taking the representative test vector as a parameter of a PPI algorithm, and processing the remote sensing image by using the PPI algorithm of the representative test vector to obtain a PPI image;
and selecting the pixel point with the maximum pixel grey value in each ground feature in the PPI image as a final sample point.
7. A processing system of a remote sensing image processing method for land resource management according to any one of claims 1-6, characterized in that it comprises:
the information extraction unit is used for marking each ground object on the remote sensing image, and each ground object selects a corresponding number of pixels as sample points for initial sample classification;
the image synthesis unit is used for acquiring a ground object spectrum curve corresponding to each ground object in the spectrum library, determining a peak point and a valley point in the ground object spectrum curve, calculating a maximum curvature point of the ground object spectrum curve, and performing false color synthesis on a wave band corresponding to the peak point, a wave band corresponding to the valley point and a wave band corresponding to the maximum curvature point in the ground object spectrum curve to obtain a false color image;
The first information processing unit is used for acquiring a false color waveband combination corresponding to each waveband in a false color image in an RGB channel and calculating the hue value and the gray value of a similar sample point in each false color waveband combination;
the second information processing unit is used for reclassifying the sample points according to the consistency of the hue value changes and the consistency of the gray value changes of the similar sample points in different false color waveband combinations;
the third information processing unit is used for calculating the maximum homogeneous sample point with the largest distance between each sample point and the homogeneous sample points after reclassification and determining a representative test vector according to the maximum homogeneous sample point;
and the classification unit is used for determining a final sample point according to the representative test vector as a parameter of the PPI algorithm, and performing final sample classification on the remote sensing image according to the final sample point to obtain a classification result.
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