Aerial remote sensing image scene classification method based on data processing

Abstract

The invention discloses an aerial remote sensing image scene classification method based on data processing, and relates to the technical field of image processing, and the method comprises the following steps: S1, collecting a real-time aerial remote sensing image through a sensor; s2, constructing a remote sensing digital template by using the shadow detection set, and processing pixels of the real-time aerial remote sensing image to obtain remote sensing fitness of each pixel; and S3, processing the remote sensing fitness of all the pixels by using the isolated forest to obtain a classification result of the real-time aerial remote sensing image. According to the method, the remote sensing fitness of the pixels is calculated by using the remote sensing digital template, the feature conditions of the pixels can be quantified, and a reliable basis is provided for subsequent target classification; the isolated forest algorithm can process a large-scale data set, is suitable for real-time aerial remote sensing images containing a large number of pixels, and effectively recognizes abnormal values, thereby judging whether a target exists or not, and completing scene classification.

Description

Technical Field

[0001] The invention relates to the technical field of image processing, and in particular to an aerial remote sensing image scene classification method based on data processing. Background Art

[0002] In recent years, aerial remote sensing and UAV technology have developed rapidly. As an effective information carrier, aerial remote sensing images have rich shape, texture and scene semantic information and are widely used in intelligence reconnaissance, environmental monitoring, natural disaster prevention and water resource protection and other application research. Aerial remote sensing images usually contain complex ground information and various target types. How to accurately and quickly identify whether there are target types in aerial remote sensing images and complete the scene classification of aerial remote sensing images has become an urgent problem to be solved. However, traditional aerial remote sensing image classification methods based on target recognition often rely on manual feature extraction, which is not only time-consuming and labor-intensive, but also difficult to adapt to complex and changing image scenes. It has exceeded the ability of manual real-time interpretation and understanding, and it is difficult to complete classification in a timely and accurate manner. Summary of the invention

[0003] In order to solve the above problems, the present invention proposes a method for scene classification of aerial remote sensing images based on data processing.

[0004] The technical solution of the present invention is: a method for classifying aerial remote sensing images based on data processing comprises the following steps:

[0005] S1. Collect real-time aerial remote sensing images using sensors;

[0006] S2, using the shadow detection set to construct a remote sensing digital template, process the pixels of the real-time aerial remote sensing image, and obtain the remote sensing fitness of each pixel;

[0007] S3. Use the isolation forest to process the remote sensing fitness of all pixels to obtain the classification results of real-time aerial remote sensing images.

[0008] Furthermore, S2 includes the following sub-steps:

[0009] S21, constructing a shadow detection set for the real-time aerial remote sensing image according to the solar sky angle and the solar azimuth when the real-time aerial remote sensing image is collected;

[0010] S22, construct remote sensing digital template using shadow detection set;

[0011] S23. Use the remote sensing digital template to process the real-time aerial remote sensing image to obtain the remote sensing fitness of each pixel.

[0012] The beneficial effect of the above further scheme is: in the present invention, the solar earth angle and the solar azimuth are the key factors affecting the formation and distribution of shadows in remote sensing images. Different solar earth angles and solar azimuths will cause the shape, size and position of shadows in remote sensing images to change. Constructing a shadow detection set according to the specific conditions when collecting images in real time can enhance the adaptability of the shadow detection algorithm to different conditions, thereby more accurately identifying the shadow area in the remote sensing image. An accurate shadow detection set provides a reliable basis for subsequent processing such as target recognition using shadow information. In addition, the construction of the remote sensing digital template takes into account a variety of factors, which can enhance the robustness of the algorithm, enabling it to cope with more complex surface environments and more diverse surface objects. The remote sensing fitness generated by the remote sensing digital template for each pixel can be used as important input information for subsequent classification and recognition processing.

[0013] Furthermore, in S21, the shadow detection set of real-time aerial remote sensing images The expression is:

[0014] ;

[0015] In the formula, It represents the sun angle when collecting real-time aerial remote sensing images. Indicates the solar azimuth when collecting real-time aerial remote sensing images. Represents the radiance of real-time aerial remote sensing images, Represents the radiant emittance of the sensor.

[0016] The beneficial effect of the above further scheme is: in the present invention, the solar zenith angle refers to the angle between the incident sunlight at the pixel position at that moment and the normal of the ground plane of the pixel. The solar azimuth angle refers to the angle of the ray (the vertex is the surface pixel point) that starts from the north direction and turns clockwise to the projection of the incident sunlight at the pixel at a certain moment on the ground surface. The radiation brightness of a remote sensing image refers to the radiation energy received by the remote sensing sensor from the earth's surface or other target objects, and the brightness value formed on the sensor. Using this angle information, the reflectivity and radiation characteristics of the surface can be estimated more accurately, thereby improving the quality and availability of remote sensing images. The radiation emittance of the sensor is used to describe the radiation flux radiated to the hemispherical space per unit area of ​​the image sensor surface.

[0017] Further, S22 includes the following sub-steps:

[0018] S221, generating a detection matrix according to the shadow detection set;

[0019] S222, filling the F-norm value of the detection matrix into the center position of the remote sensing digital template to generate a remote sensing digital template.

[0020] The beneficial effect of the above further scheme is that in the present invention, the F-norm value of the detection matrix is ​​filled into the center position of the remote sensing digital template, which can effectively represent the shadow features. The detection matrix provides important input information for subsequent processing. By analyzing and processing the detection matrix, shadow features can be further extracted to provide support for tasks such as classification and recognition of remote sensing images. Remote sensing digital template The expression is , after filling the F norm value into the center position of the remote sensing digital template, fill the elements of the shadow detection set into the first row in sequence, fill the elements of the shadow detection set into the last row in reverse order, and then fill the elements of the first column of the second row and the third column of the second row, where, represents the F-norm of the detection matrix.

[0021] Further, in S221, the detection matrix The expression is:

[0022] ;

[0023] In the formula, It represents the sun angle when collecting real-time aerial remote sensing images. Indicates the solar azimuth when collecting real-time aerial remote sensing images. Represents a set of shadow detections for real-time aerial remote sensing images The standard deviation of all elements in , Represents the maximum value function.

[0024] Further, in S23, each pixel of the real-time aerial remote sensing image is input into the remote sensing digital template, and the shadow fitness of each pixel is output using the maximum eigenvalue of the remote sensing digital template;

[0025] The location in the real-time aerial remote sensing image is The shadow fitness of the pixel The calculation formula is:

[0026] ;

[0027] In the formula, represents the horizontal coordinate of the pixel, represents the vertical coordinate of the pixel, Indicates the hue of the pixel, It indicates the mean value of all tones of the four neighboring pixels corresponding to the pixel. Indicates the saturation of the pixel. It represents the mean of all saturation values ​​of the four neighboring pixels corresponding to the pixel. represents the brightness of the pixel, It indicates the mean value of all brightness of the four neighboring pixels corresponding to the pixel. It represents the mean value of all hues of the pixel’s D neighboring pixels. It represents the mean of all saturation values ​​of the D neighboring pixels corresponding to the pixel. It represents the mean brightness of all pixels in the D neighboring pixels corresponding to the pixel. Represents the maximum eigenvalue of the remote sensing digital template, It represents the sun angle when collecting real-time aerial remote sensing images. Indicates the solar azimuth when collecting real-time aerial remote sensing images.

[0028] The beneficial effects of the above further scheme are: in the present invention, in the HSV model, hue is expressed as an angle, usually ranging from 0° to 360°; saturation is expressed as the ratio between the purity of the selected color and the maximum purity of the color, usually expressed as a percentage or a normalized value; lightness represents the brightness of the color, usually expressed as a percentage or a normalized value. The present invention extracts the elements of the corresponding position in the remote sensing digital template according to the neighboring pixels of each pixel, and participates in the calculation.

[0029] Furthermore, S3 includes the following sub-steps:

[0030] S31, randomly selecting a number of remote sensing fitnesses from the remote sensing fitnesses of all pixels as the root nodes of each isolated tree to obtain an isolated forest;

[0031] S32, traversing each isolated tree using remote sensing fitness to obtain the height of the remote sensing fitness in each isolated tree;

[0032] S33, calculating the average height of all isolated trees for each remote sensing fitness;

[0033] S34, judging whether there is a remote sensing fitness whose average height is greater than or equal to the threshold, if so, there is a detection target in the real-time aerial remote sensing image, otherwise there is no detection target in the real-time aerial remote sensing image, and the scene classification is completed.

[0034] The beneficial effect of the above further scheme is that in the present invention, in the classification of aerial remote sensing image scenes, abnormal data points may represent potential target areas. By calculating the height of the remote sensing fitness at each isolated tree and judging whether there is a remote sensing fitness with an average height greater than or equal to a threshold, the abnormal data points, i.e., potential targets or areas of interest, can be accurately detected. The threshold can be set manually.

[0035] Furthermore, in S32, the remote sensing fitness is at the height of the isolated tree The calculation formula is:

[0036] ;

[0037] In the formula, represents the standard deviation of all remote sensing fitness in the root node of the isolated tree, represents the split value of the isolated tree, represents the maximum depth of an isolated tree, represents a natural constant, Represents the number of remote sensing fitness in the root node of the isolated tree.

[0038] The split value refers to the threshold used at each node to split the data into the left subtree or the right subtree.

[0039] The beneficial effects of the present invention are:

[0040] (1) The present invention uses the characteristic parameters of real-time aerial remote sensing images to extract shadow detection sets, providing important clues for subsequent target detection; by constructing remote sensing digital templates, the pixels of real-time aerial remote sensing images can be standardized to improve the efficiency and accuracy of image processing; at the same time, the remote sensing digital templates can also be adjusted and optimized as needed to adapt to different surface features and classification requirements;

[0041] (2) The present invention uses remote sensing digital templates to calculate the remote sensing fitness of pixels, which can quantify the characteristics of pixels and provide a reliable basis for subsequent target classification. The isolation forest algorithm can process large-scale data sets and is suitable for real-time aerial remote sensing images that contain a large number of pixels. It can also effectively identify outliers, thereby determining whether a target exists and completing scene classification. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 The flowchart of the aerial remote sensing image scene classification method based on data processing. DETAILED DESCRIPTION

[0043] The embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0044] like Figure 1 As shown, the present invention provides an aerial remote sensing image scene classification method based on data processing, comprising the following steps:

[0045] S1. Collect real-time aerial remote sensing images using sensors;

[0046] S2, using the shadow detection set to construct a remote sensing digital template, process the pixels of the real-time aerial remote sensing image, and obtain the remote sensing fitness of each pixel;

[0047] S3. Use the isolation forest to process the remote sensing fitness of all pixels to obtain the classification results of real-time aerial remote sensing images.

[0048] In this embodiment of the present invention, S2 includes the following sub-steps:

[0049] S21, constructing a shadow detection set for the real-time aerial remote sensing image according to the solar sky angle and the solar azimuth when the real-time aerial remote sensing image is collected;

[0050] S22, construct remote sensing digital template using shadow detection set;

[0051] S23. Use the remote sensing digital template to process the real-time aerial remote sensing image to obtain the remote sensing fitness of each pixel.

[0052] In the present invention, the solar sky angle and the solar azimuth are key factors affecting the formation and distribution of shadows in remote sensing images. Different solar sky angles and solar azimuths will cause the shape, size and position of shadows in remote sensing images to change. Constructing a shadow detection set according to the specific conditions when collecting images in real time can enhance the adaptability of the shadow detection algorithm to different conditions, thereby more accurately identifying the shadow area in the remote sensing image. An accurate shadow detection set provides a reliable basis for subsequent processing such as target recognition using shadow information. In addition, the construction of the remote sensing digital template takes into account a variety of factors, which can enhance the robustness of the algorithm, enabling it to cope with more complex surface environments and more diverse surface objects. The remote sensing fitness generated by the remote sensing digital template for each pixel can be used as important input information for subsequent classification and recognition processing.

[0053] In the embodiment of the present invention, in S21, the shadow detection set of the real-time aerial remote sensing image The expression is:

[0054] ;

[0055] In the formula, It represents the sun angle when collecting real-time aerial remote sensing images. Indicates the solar azimuth when collecting real-time aerial remote sensing images. Represents the radiance of real-time aerial remote sensing images, Represents the radiant emittance of the sensor.

[0056] In the present invention, the solar zenith angle refers to the angle between the incident sunlight at the pixel position at that moment and the normal of the ground plane of the pixel. The solar azimuth angle refers to the angle of the ray (the vertex is the surface pixel point) from the north direction to the projection of the incident sunlight at the pixel at a certain moment on the ground surface. The radiant brightness of a remote sensing image refers to the radiant energy received by the remote sensing sensor from the earth's surface or other target objects, and the brightness value formed on the sensor. Using these angle information, the reflectivity and radiation characteristics of the ground surface can be estimated more accurately, thereby improving the quality and availability of remote sensing images.

[0057] In this embodiment of the present invention, S22 includes the following sub-steps:

[0058] S221, generating a detection matrix according to the shadow detection set;

[0059] S222, filling the F-norm value of the detection matrix into the center position of the remote sensing digital template to generate a remote sensing digital template.

[0060] In the present invention, the F-norm value of the detection matrix is ​​filled into the center position of the remote sensing digital template, which can effectively represent the shadow features. The detection matrix provides important input information for subsequent processing. By analyzing and processing the detection matrix, shadow features can be further extracted to provide support for tasks such as classification and recognition of remote sensing images. Remote sensing digital template The expression is , after filling the F norm value into the center position of the remote sensing digital template, fill the elements of the shadow detection set into the first row in sequence, fill the elements of the shadow detection set into the last row in reverse order, and then fill the elements of the first column of the second row and the third column of the second row, where, Represents the F-norm of the detection matrix.

[0061] In the embodiment of the present invention, in S221, the detection matrix The expression is:

[0062] ;

[0063] In the formula, It represents the sun angle when collecting real-time aerial remote sensing images. Indicates the solar azimuth when collecting real-time aerial remote sensing images. Represents a set of shadow detections for real-time aerial remote sensing images The standard deviation of all elements in , Represents the maximum value function.

[0064] In the embodiment of the present invention, in S23, each pixel of the real-time aerial remote sensing image is input into the remote sensing digital template, and the shadow fitness of each pixel is output using the maximum eigenvalue of the remote sensing digital template;

[0065] The location in the real-time aerial remote sensing image is The shadow fitness of the pixel The calculation formula is:

[0066] ;

[0067] In the formula, represents the horizontal coordinate of the pixel, represents the vertical coordinate of the pixel, Indicates the hue of the pixel, It indicates the mean value of all tones of the four neighboring pixels corresponding to the pixel. Indicates the saturation of the pixel. It represents the mean of all saturation values ​​of the four neighboring pixels corresponding to the pixel. represents the brightness of the pixel, It indicates the mean value of all brightness of the four neighboring pixels corresponding to the pixel. It represents the mean value of all hues of the pixel’s D neighboring pixels. It represents the mean of all saturation values ​​of the D neighboring pixels corresponding to the pixel. It represents the mean brightness of all pixels in the D neighboring pixels corresponding to the pixel. Represents the maximum eigenvalue of the remote sensing digital template, It represents the sun angle when collecting real-time aerial remote sensing images. Indicates the solar azimuth when collecting real-time aerial remote sensing images.

[0068] In the present invention, in the HSV model, hue is expressed as an angle, usually ranging from 0° to 360°; saturation is expressed as the ratio between the purity of the selected color and the maximum purity of the color, usually expressed as a percentage or a normalized value; lightness represents the brightness of the color, usually expressed as a percentage or a normalized value. The present invention extracts the elements of the corresponding position in the remote sensing digital template according to the neighboring pixels of each pixel, and participates in the calculation.

[0069] In this embodiment of the present invention, S3 includes the following sub-steps:

[0070] S31, randomly selecting a number of remote sensing fitnesses from the remote sensing fitnesses of all pixels as the root nodes of each isolated tree to obtain an isolated forest;

[0071] S32, traversing each isolated tree using remote sensing fitness to obtain the height of the remote sensing fitness in each isolated tree;

[0072] S33, calculating the average height of all isolated trees for each remote sensing fitness;

[0073] S34, judging whether there is a remote sensing fitness whose average height is greater than or equal to the threshold, if so, there is a detection target in the real-time aerial remote sensing image, otherwise there is no detection target in the real-time aerial remote sensing image, and the scene classification is completed.

[0074] In the present invention, in the classification of aerial remote sensing image scenes, abnormal data points may represent potential target areas. By calculating the height of the remote sensing fitness at each isolated tree and judging whether there is a remote sensing fitness with an average height greater than or equal to a threshold, abnormal data points, i.e., potential targets or areas of interest, can be accurately detected. The threshold can be set manually.

[0075] In the embodiment of the present invention, in S32, the remote sensing fitness is at the height of the isolated tree. The calculation formula is:

[0076] ;

[0077] In the formula, represents the standard deviation of all remote sensing fitness in the root node of the isolated tree, represents the split value of the isolated tree, represents the maximum depth of an isolated tree, represents a natural constant, Represents the number of remote sensing fitness in the root node of the isolated tree.

[0078] The split value refers to the threshold used at each node to split the data into the left subtree or the right subtree.

[0079] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the present invention.

Claims

1. A method for scene classification of aerial remote sensing images based on data processing, characterized in that: The following steps are involved: S1. Collect real-time aerial remote sensing images using sensors; S2, using the shadow detection set to construct a remote sensing digital template, process the pixels of the real-time aerial remote sensing image, and obtain the remote sensing fitness of each pixel; S3. Use the isolation forest to process the remote sensing fitness of all pixels to obtain the classification results of real-time aerial remote sensing images.

2. The method for scene classification of aerial remote sensing images based on data processing according to claim 1, characterized in that: The S2 comprises the following sub-steps: S21, constructing a shadow detection set for the real-time aerial remote sensing image according to the solar sky angle and the solar azimuth when the real-time aerial remote sensing image is collected; S22, construct remote sensing digital template using shadow detection set; S23. Use the remote sensing digital template to process the real-time aerial remote sensing image to obtain the remote sensing fitness of each pixel.

3. The method for scene classification of aerial remote sensing images based on data processing according to claim 2 is characterized in that: In S21, real-time shadow detection of aerial remote sensing images The expression is: ； In the formula, It represents the sun angle when collecting real-time aerial remote sensing images. Indicates the solar azimuth when collecting real-time aerial remote sensing images. Represents the radiance of real-time aerial remote sensing images, Represents the radiant emittance of the sensor.

4. The method for scene classification of aerial remote sensing images based on data processing according to claim 2, characterized in that: The S22 comprises the following sub-steps: S221, generating a detection matrix according to the shadow detection set; S222, filling the F-norm value of the detection matrix into the center position of the remote sensing digital template to generate a remote sensing digital template.

5. The method for scene classification of aerial remote sensing images based on data processing according to claim 4 is characterized in that: In S221, the detection matrix The expression is: ； In the formula, It represents the sun angle when collecting real-time aerial remote sensing images. Indicates the solar azimuth when collecting real-time aerial remote sensing images. Represents a set of shadow detections for real-time aerial remote sensing images The standard deviation of all elements in , Represents the maximum value function.

6. The method for scene classification of aerial remote sensing images based on data processing according to claim 2, characterized in that: In S23, each pixel of the real-time aerial remote sensing image is input into the remote sensing digital template, and the shadow fitness of each pixel is output using the maximum eigenvalue of the remote sensing digital template; The position in the real-time aerial remote sensing image is The shadow fitness of the pixel The calculation formula is: ； In the formula, represents the horizontal coordinate of the pixel, represents the vertical coordinate of the pixel, Indicates the hue of the pixel, It indicates the mean value of all tones of the four neighboring pixels corresponding to the pixel. Indicates the saturation of the pixel. It represents the mean of all saturation values ​​of the four neighboring pixels corresponding to the pixel. represents the brightness of the pixel, It indicates the mean value of all brightness of the four neighboring pixels corresponding to the pixel. It represents the mean value of all hues of the pixel’s D neighboring pixels. It represents the mean of all saturation values ​​of the D neighboring pixels corresponding to the pixel. It represents the mean brightness of all pixels in the D neighboring pixels corresponding to the pixel. Represents the maximum eigenvalue of the remote sensing digital template, It represents the sun angle when collecting real-time aerial remote sensing images. Indicates the solar azimuth when collecting real-time aerial remote sensing images.

7. The method for scene classification of aerial remote sensing images based on data processing according to claim 1, characterized in that: The S3 comprises the following sub-steps: S31, randomly selecting a number of remote sensing fitnesses from the remote sensing fitnesses of all pixels as the root nodes of each isolated tree to obtain an isolated forest; S32, traversing each isolated tree using remote sensing fitness to obtain the height of the remote sensing fitness in each isolated tree; S33, calculating the average height of all isolated trees for each remote sensing fitness; S34, judging whether there is a remote sensing fitness whose average height is greater than or equal to the threshold, if so, there is a detection target in the real-time aerial remote sensing image, otherwise there is no detection target in the real-time aerial remote sensing image, and the scene classification is completed.

8. The method for scene classification of aerial remote sensing images based on data processing according to claim 7 is characterized in that: In S32, the remote sensing fitness is the height of the isolated tree The calculation formula is: ； In the formula, represents the standard deviation of all remote sensing fitness in the root node of the isolated tree, represents the split value of the isolated tree, represents the maximum depth of an isolated tree, represents a natural constant, Represents the number of remote sensing fitness in the root node of the isolated tree.

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