CN114399438A - Haze distinguishing cloth range identification method and device based on color remote sensing image - Google Patents
Haze distinguishing cloth range identification method and device based on color remote sensing image Download PDFInfo
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Abstract
The invention provides a haze distinguishing cloth range identification method and device based on a color remote sensing image. The method comprises the following steps: acquiring an RGB (red, green and blue) color remote sensing image of a target area; converting the RGB color remote sensing image into an HSV color space to obtain a saturation remote sensing image and a brightness remote sensing image; enhancing the intensity of haze pixel signals in the saturation remote sensing image to obtain a saturation haze pixel signal enhancement image, and enhancing the intensity of haze pixel signals in the brightness remote sensing image to obtain a brightness haze pixel signal enhancement image; and obtaining a haze pixel identification result distribution graph of the target area according to the saturation haze pixel signal enhancement graph and the brightness haze pixel signal enhancement graph. The method can identify the influence range of the haze weather in the map only by depending on the information provided by the satellite color remote sensing image, and has the advantages of high accuracy, wide coverage area and strong practicability.
Description
Technical Field
The invention relates to the technical field of atmospheric remote sensing pollution detection, in particular to a haze distinguishing cloth range identification method and device based on a color remote sensing image.
Background
Haze, also known as dust haze, refers to a turbid phenomenon caused by the suspension of a large amount of particles such as smoke and dust. Some fine particles gather near the ground, and they absorb, refract or reflect sunlight to reduce visibility, and the weather phenomenon caused when the horizontal visibility is less than 10km is haze. The core material of haze is particulate matter suspended in the air, known meteorologically as atmospheric aerosol. At present, large-range and continuous heavy polluted weather still happens occasionally under adverse meteorological conditions, so haze monitoring real-time services need to be developed.
In the prior art, there are two main types of methods for monitoring haze weather, and one type is haze monitoring through a ground monitoring station. However, due to the reasons of uneven site coverage, more cities, less suburbs, more plains, less mountains and the like, the method cannot acquire accurate spatial distribution of the haze weather, and lacks quantitative monitoring capability for large-scale haze distribution. The other type is a method for remote sensing monitoring by using satellites, and an aerosol identification method and a multiband threshold method are mainly adopted. The aerosol identification method generally comprises the steps of inverting the optical thickness of the aerosol, then calculating the concentration of PM2.5 and the like, and dividing the range of the haze area according to the inversion result. The defect is that the method depends on cloud detection products, generally, when haze is severe, the cloud detection products often judge the pixels as thin clouds, so that the optical thickness of the aerosol of the pixels cannot be acquired, and the pixels are wrongly divided into non-haze areas. And the other haze judging and identifying method utilizes multiband remote sensing data and comprehensive threshold values. The method has the disadvantage of using data in a plurality of bands from visible light to near infrared to short wave infrared and thermal infrared. Depending on a plurality of wave bands, a general earth observation satellite cannot completely have the observation capability of the wave bands.
Disclosure of Invention
The invention provides a haze distinguishing cloth range identification method and device based on a color remote sensing image, which are used for solving the problem that the spatial distribution condition of haze weather cannot be accurately and efficiently acquired in the prior art and realizing accurate monitoring of a large-scale haze distinguishing cloth range.
The invention provides a haze distinguishing cloth range identification method based on a color remote sensing image, which comprises the following steps:
acquiring an RGB (red, green and blue) color remote sensing image of a target area;
converting the RGB color remote sensing image into an HSV color space to obtain a saturation remote sensing image and a brightness remote sensing image;
enhancing the intensity of the haze pixel signal in the saturation remote sensing image to obtain a saturation haze pixel signal enhancement image, and enhancing the intensity of the haze pixel signal in the brightness remote sensing image to obtain a brightness haze pixel signal enhancement image;
and obtaining a haze pixel identification result distribution diagram of the target area according to the saturation haze pixel signal enhancement diagram and the brightness haze pixel signal enhancement diagram.
Optionally, converting the RGB color remote sensing image into an HSV color space to obtain a saturation remote sensing image and a brightness remote sensing image, including:
converting the RGB color remote sensing image according to the following formula:
V＝max(R,G,B)
where H is hue data, S is saturation data, V is brightness data, R is a red channel, G is a green channel, and B is a blue channel.
Optionally, the intensity of the haze pixel signal in the saturation remote sensing image is enhanced to obtain a saturation haze pixel signal enhancement map, including:
and enhancing the low saturation signal in the saturation remote sensing image, reducing the high saturation signal in the saturation remote sensing image, and obtaining a saturation haze pixel signal enhancement map.
Optionally, the method for enhancing the intensity of the haze pixel signal in the luminance remote sensing image to obtain a luminance haze pixel signal enhancement map includes:
enhancing a high-low brightness target mixed pixel signal in the brightness remote sensing image to obtain a mixed pixel signal enhancement image, wherein the high-low brightness target mixed pixel signal is a cloud edge pixel signal and a broken cloud pixel signal in the brightness remote sensing image;
and enhancing the medium-brightness signal in the mixed pixel signal enhancement image, and reducing the high-brightness signal and the low-brightness signal in the mixed pixel signal enhancement image to obtain a brightness haze pixel signal enhancement image.
Optionally, obtaining a haze pixel identification result distribution map of the target region according to the saturation haze pixel signal enhancement map and the brightness haze pixel signal enhancement map, including:
calculating the pixel index of each pixel according to the pixel signal intensity value in the saturation haze pixel signal enhancement image and the pixel signal intensity value in the brightness haze pixel signal enhancement image;
and determining the pixels with the pixel indexes larger than a preset threshold value as haze pixels, and generating a haze pixel identification result distribution graph of the target area.
Optionally, calculating a pixel index of each pixel according to the pixel signal intensity value in the saturation haze pixel signal enhancement map and the pixel signal intensity value in the brightness haze pixel signal enhancement map includes:
calculating a pixel index I of each pixel according to the following formulaHazThe formula is as follows:
IHaz＝SE·VE
wherein SE is the pixel signal intensity value in the saturation haze pixel signal enhancement map, and VE is the pixel signal intensity value in the brightness haze pixel signal enhancement map.
Optionally, the RGB remote-color sensing image is a true-color remote-sensing image after atmospheric molecular scattering correction and image brightness enhancement.
The invention also provides a haze distinguishing cloth range identification device based on the color remote sensing image, which comprises the following components:
the acquisition module is used for acquiring RGB (red, green and blue) color remote sensing images of the target area;
the conversion module is used for converting the RGB color remote sensing image into an HSV color space to obtain a saturation remote sensing image and a brightness remote sensing image;
the enhancement module is used for enhancing the intensity of the haze pixel signals in the saturation remote sensing image to obtain a saturation haze pixel signal enhancement image, and enhancing the intensity of the haze pixel signals in the brightness remote sensing image to obtain a brightness haze pixel signal enhancement image;
and the judging and identifying module is used for obtaining a haze pixel judging and identifying result distribution graph of the target area according to the saturation haze pixel signal enhancement graph and the brightness haze pixel signal enhancement graph.
The invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the steps of the haze distinguishing cloth range judging method based on the color remote sensing image.
The present invention also provides a non-transitory computer readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the haze distinguishing distribution range identification method based on a color remote sensing map as described in any one of the above.
The invention provides a haze distinguishing cloth range judging and identifying method and device based on a color remote sensing image. Further, strength enhancement is carried out on the haze pixel signals in the saturation remote sensing image to obtain a saturation haze pixel signal enhancement image, strength enhancement is carried out on the haze pixel signals in the brightness remote sensing image to obtain a brightness haze pixel signal enhancement image, and finally a haze pixel identification result distribution image of the target area is obtained according to the saturation haze pixel signal enhancement image and the brightness haze pixel signal enhancement image. According to the invention, through the technical route of converting RGB color remote sensing images collected by a satellite into HSV, enhancing saturation haze pixel signals and enhancing brightness haze pixel signals, and utilizing the enhancement results of the saturation haze pixel signals and the enhancement results of the brightness haze pixel signals to judge haze pixels, the judgment of haze weather influence ranges based on true color remote sensing images is realized, the haze weather influence ranges in the images can be identified only by depending on information provided by the satellite color remote sensing images, the accuracy is high, the coverage area is wide, and the practicability is strong.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is one of the flow diagrams of the haze distinguishing cloth range judging method based on the color remote sensing image provided by the invention;
FIG. 2 is a second schematic flow chart of the haze distinguishing cloth range identification method based on the color remote sensing image provided by the invention;
FIG. 3 is a schematic diagram of an RGB color remote sensing image of a target area provided by the present invention;
FIG. 4 is a schematic diagram of a saturation remote sensing image of a target area provided by the present invention;
FIG. 5 is a schematic view of a remote sensing image of the brightness of a target area provided by the present invention;
FIG. 6 is a schematic diagram of the saturation haze pixel signal enhancement of a target region provided by the present invention;
FIG. 7 is a schematic diagram of the blended pixel signal enhancement of a target region provided by the present invention;
FIG. 8 is a schematic diagram of the enhancement of luminance haze pixel signals in a target region according to the present invention;
FIG. 9 is a schematic pixel index of a target region provided by the present invention;
FIG. 10 is a schematic diagram illustrating a distribution of haze pixel identification results of a target area according to the present invention;
FIG. 11 is a schematic diagram illustrating comparison between the haze pixel identification result of the target area and the ground meteorological site monitoring result according to the present invention;
FIG. 12 is a schematic structural diagram of the haze distinguishing cloth range identification device based on the color remote sensing image provided by the invention;
fig. 13 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. 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.
As shown in fig. 1, the haze distinguishing cloth range identification method based on the color remote sensing image provided by the invention comprises the following steps:
step 101: acquiring an RGB (red, green and blue) color remote sensing image of a target area;
in this step, optionally, an RGB color remote sensing image of the target area is obtained based on a visible light band remote sensor on the satellite, the image includes data of three bands of red, green and blue, before the RGB color remote sensing image is input into a computer for processing, non-linear processing steps such as atmospheric molecular scattering correction processing (image turned to blue after processing) and image enhancement processing (landmark target brightness improvement after processing) need to be performed on the RGB color remote sensing image, and the effect of the processed RGB color remote sensing image is similar to that of human visual observation, as shown in fig. 3.
Step 102: converting the RGB color remote sensing image into an HSV color space to obtain a saturation remote sensing image and a brightness remote sensing image;
in this step, it should be noted that, in order to be able to identify that a pixel of the image is a haze weather from a true color image, the image data of the RGB color remote sensing image needs to be converted from the RGB model to the HSV model, and the specific conversion method is not limited in the present invention.
Step 103: enhancing the intensity of the haze pixel signal in the saturation remote sensing image to obtain a saturation haze pixel signal enhancement image, and enhancing the intensity of the haze pixel signal in the brightness remote sensing image to obtain a brightness haze pixel signal enhancement image;
in this step, as shown in fig. 4, in the saturation remote sensing image, the saturation corresponds to the signal intensity, the cloud saturation is close to 0, the saturation of the land and ocean in clear sky is higher, generally greater than 0.2, and the saturation of the haze area is lower, and is between the clear space area and the cloud area. Therefore, the signal intensity of the haze pixels in the original saturation remote sensing image is between the clear area and the cloud area, and the haze pixels are distinguished from other pixels well due to the fact that the signal intensity of the haze pixels is highlighted. Therefore, the signal intensity of the haze pixel is enhanced by converting the saturation data in the saturation remote sensing image into a dimensionless numerical value, specifically, the low saturation signal in the saturation remote sensing image is enhanced to be close to 1, the high saturation signal in the saturation remote sensing image is weakened to be close to 0, so that the cloud area and the haze area with the original low signal intensity are enhanced, the clear sky land and the clear sky sea with the original high signal intensity are inhibited, and the final saturation haze pixel signal enhancement diagram is obtained, as shown in fig. 6.
In this step, it should be noted that, there are various calculation manners for converting the saturation data in the saturation remote sensing image into a dimensionless number, including but not limited to the following formula:
SE＝e-4S
SE＝1-S
SE＝1-S2
in this step, it should be noted that, as the edges and the broken clouds in the cloud area and the ground surface of the lower ground generate a mixing effect, the saturation and the brightness of these areas change significantly, and similar to the appearance of the haze, the areas are not easily distinguished from the haze. Therefore, the signal intensity of the cloud edge and the cloud-broken pixels is enhanced by adopting the method of enhancing the mixed pixel signal, as shown in fig. 7, the mixed pixel signal enhances the edge pixels of the cloud and the large-patch cloud in the image, and the enhancement factors are stronger, so that the purpose of enhancing the areas is achieved.
In this step, after the mixed pixel signal enhancement map is obtained, since the cloud region (including the ragged clouds and the edge clouds) and the clear sky region in the mixed pixel signal enhancement map have strong signals and the luminance signal intensity of the ocean is weak, in order to suppress signals in these non-haze regions, the intermediate value of the signal intensity is enhanced (close to the value 1), and the vicinity of the end point value of the signal intensity (the vicinity of the original value 0 and the vicinity of the original value 1) is weakened (close to the value 0), so as to achieve the purpose of enhancing the signal intensity of the haze pixel, and the obtained luminance haze pixel signal enhancement map is as shown in fig. 8.
Step 104: and obtaining a haze pixel identification result distribution diagram of the target area according to the saturation haze pixel signal enhancement diagram and the brightness haze pixel signal enhancement diagram.
In this step, the pixel signal intensities in the saturation haze pixel signal enhancement image and the brightness haze pixel signal enhancement image are multiplied respectively to obtain the pixel index corresponding to each pixel. As shown in fig. 9, the pixel signal intensity is higher in the graph, which is a haze pixel. Comparing the pixel index of each pixel with a preset threshold, judging the pixel as a haze pixel when the pixel index is greater than or equal to the preset threshold, judging the pixel as a non-haze pixel when the pixel index is less than the preset threshold, and finally obtaining a haze pixel judgment result distribution graph of the target area, as shown in fig. 10.
The haze distinguishing cloth range judging and identifying method based on the color remote sensing image comprises the steps of firstly obtaining an RGB color remote sensing image of a target area, and then converting the RGB color remote sensing image into an HSV color space to obtain a saturation remote sensing image and a brightness remote sensing image. Further, strength enhancement is carried out on the haze pixel signals in the saturation remote sensing image to obtain a saturation haze pixel signal enhancement image, strength enhancement is carried out on the haze pixel signals in the brightness remote sensing image to obtain a brightness haze pixel signal enhancement image, and finally a haze pixel identification result distribution image of the target area is obtained according to the saturation haze pixel signal enhancement image and the brightness haze pixel signal enhancement image. According to the invention, through the technical route of converting RGB color remote sensing images collected by a satellite into HSV, enhancing saturation haze pixel signals and enhancing brightness haze pixel signals, and utilizing the enhancement results of the saturation haze pixel signals and the enhancement results of the brightness haze pixel signals to judge haze pixels, the judgment of haze weather influence ranges based on true color remote sensing images is realized, the haze weather influence ranges in the images can be identified only by depending on information provided by the satellite color remote sensing images, the accuracy is high, the coverage area is wide, and the practicability is strong.
Based on the content of the above embodiment, in this embodiment, converting the RGB color remote sensing image into an HSV color space to obtain a saturation remote sensing image and a brightness remote sensing image includes:
converting the RGB color remote sensing image according to the following formula:
V＝max(R,G,B)
where H is hue data, S is saturation data, V is brightness data, R is a red channel, G is a green channel, and B is a blue channel.
In this embodiment, the RGB color remote sensing image may also be converted into the HSV color space by other manners, which are not limited herein.
Based on the content of the foregoing embodiment, in this embodiment, enhancing the intensity of the haze pixel signal in the saturation remote sensing image to obtain a saturation haze pixel signal enhancement map includes:
and enhancing the low saturation signal in the saturation remote sensing image, reducing the high saturation signal in the saturation remote sensing image, and obtaining a saturation haze pixel signal enhancement map.
Based on the content of the foregoing embodiment, in this embodiment, enhancing the intensity of the haze pixel signal in the remote luminance sensing image to obtain a luminance haze pixel signal enhancement map includes:
enhancing a high-low brightness target mixed pixel signal in the brightness remote sensing image to obtain a mixed pixel signal enhancement image, wherein the high-low brightness target mixed pixel signal is a cloud edge pixel signal and a broken cloud pixel signal in the brightness remote sensing image;
and enhancing the medium-brightness signal in the mixed pixel signal enhancement image, and reducing the high-brightness signal and the low-brightness signal in the mixed pixel signal enhancement image to obtain a brightness haze pixel signal enhancement image.
In this embodiment, optionally, the mixed pixel signal of the high and low brightness targets in the brightness remote sensing image is enhanced based on the following formula to obtain a mixed pixel signal enhancement map, where the formula is:
where Local indicates that Local neighborhood data is used, 5 × 5 may be used in this embodiment.
In this embodiment, the low luminance signal in the enhancement map of the mixed pixel signal is enhanced, and the calculation manner for reducing the high luminance signal in the enhancement map of the mixed pixel signal can be various, including but not limited to the following formula:
VE＝-4(V·RVH)2+4·V·RV
based on the content of the foregoing embodiment, in this embodiment, obtaining a distribution diagram of a haze pixel identification result of a target region according to the saturation haze pixel signal enhancement diagram and the brightness haze pixel signal enhancement diagram includes:
calculating the pixel index of each pixel according to the pixel signal intensity value in the saturation haze pixel signal enhancement image and the pixel signal intensity value in the brightness haze pixel signal enhancement image;
and determining the pixels with the pixel indexes larger than a preset threshold value as haze pixels, and generating a haze pixel identification result distribution graph of the target area.
Based on the content of the foregoing embodiment, in this embodiment, calculating the pixel index of each pixel according to the pixel signal intensity value in the saturation haze pixel signal enhancement map and the pixel signal intensity value in the brightness haze pixel signal enhancement map includes:
calculating a pixel index I of each pixel according to the following formulaHazThe formula is as follows:
IHaz＝SE·VE
wherein SE is the pixel signal intensity value in the saturation haze pixel signal enhancement map, and VE is the pixel signal intensity value in the brightness haze pixel signal enhancement map.
Based on the content of the above embodiment, in this embodiment, the RGB color remote sensing image is a true color remote sensing image after atmospheric molecular scattering correction and image brightness enhancement.
The following is illustrated by specific examples:
the first embodiment is as follows:
in the present embodiment, as shown in fig. 2, first, a true color image (RGB color remote sensing image) is acquired. Specifically, collecting a true color remote sensing image, and requiring the true color image to be corrected through atmospheric molecular scattering to remove the blue bias of the image caused by molecular scattering; and then, the display degree of low-brightness earth surface details is improved through image brightness enhancement. The module reads the eligible images into the computer. Taking the true color image of the sunflower satellite imager as an example, the target area is the north China area, as shown in fig. 3. In the picture, a white area is cloud, the color of seawater is dark green, bare soil is tawny, vegetation is dark green, particularly, haze areas are like tawny and are mainly distributed in western parts of North China, plain and other places.
In this embodiment, for the image RGB to HSV in fig. 2, specifically, after the true color remote sensing image is read into the computer, the true color remote sensing image exists in a form of red, green and blue (RGB) three-layer data, and the image data is converted from an RGB model to an HSV model. After transformation, the Saturation (Saturation) and brightness (Value) images are shown in fig. 4 and 5. In fig. 4, the cloud saturation is close to 0, and the saturations of the clear sky land and the clear sky sea are higher, generally greater than 0.2; the haze zone is low in saturation degree and is between the clear zone and the cloud zone. In fig. 5, the cloud area has high brightness, the sea and clear land vegetation area is low, and the brightness of the haze area and the bare soil area is between the two.
In this embodiment, for the enhancement of the haze signal of the saturation data in fig. 2, specifically, the saturation data is converted into a dimensionless number, and the signal intensity of the haze pixel is enhanced. The calculation includes, but is not limited to, the following formula:
SE＝e-4S
SE＝1-S
SE＝1-S2
the calculation result is shown in fig. 6, in which the signal intensity of the cloud area and the haze area is enhanced, and the signals of other areas are suppressed.
In this embodiment, for the luminance data mixed pixel enhancement in fig. 2, specifically, since the cloud region edge and the cloudy are mixed with the ground surface of the lower ground, so that the saturation and luminance of these regions change significantly and behave similar to the haze, the signal intensity of the cloud edge and the cloudy pixel is enhanced by using the mixed pixel signal enhancement method based on the following formula:
the calculation result is shown in fig. 7, the enhancement factors of the edge pixels of the broken cloud and the large cloud in the graph are stronger, and the purpose of enhancing the areas is achieved.
In this embodiment, for the enhancement of the haze information of the luminance data in fig. 2, the low luminance signal in the enhancement map of the mixed pixel signal is enhanced, and the calculation manner for reducing the high luminance signal in the enhancement map of the mixed pixel signal can be various, including but not limited to the following formula:
VE＝-4(V·RVH)2+4·V·RV
the calculation result is shown in fig. 8, signals of a clear sky area and a cloud area in the graph are suppressed, and signals of a haze area are enhanced.
In this embodiment, for identifying the haze pixels in fig. 2, a haze pixel index is obtained by multiplying a saturation haze enhancement result and a brightness haze information enhancement result, and the haze pixel index is used for identifying whether the pixels are haze. In this embodiment, a pixel higher than 0.25 is identified as a haze pixel by using 0.25 as a threshold (the threshold is selected from 0.1 to 0.3 according to a specific scene, which is not specifically limited herein). The haze pixel index calculation result is shown in fig. 9. In the haze pixel index diagram, it can be seen that the signal of the haze area pixel is significantly higher than that of the non-haze area pixel.
In this embodiment, fig. 10 is a distribution diagram of haze region identification results, fig. 11 is a schematic diagram of monitoring a ground meteorological site of a target region (where an ∞ symbol in the diagram indicates that a monitoring result of the ground meteorological site is a position of haze weather), and comparing fig. 10 with fig. 11 can obtain an overlapping region in the diagram. Therefore, the haze distinguishing cloth range judging method based on the color remote sensing image has the following beneficial effects:
1) the dependence data is less, and the influence range of the haze weather in the graph can be identified only by relying on information provided by the satellite remote sensing true color image.
2) The precision is higher, compares with ground meteorological station monitoring result, and the overlap degree is higher, and the judgement precision is higher.
3) Monitoring range is wide, can supply ground website to arrange the regional haze monitoring of sparsity.
The haze distinguishing cloth range judging device based on the color remote sensing image provided by the invention is described below, and the haze distinguishing cloth range judging device based on the color remote sensing image described below and the haze distinguishing cloth range judging method based on the color remote sensing image described above can be referred to correspondingly.
As shown in fig. 12, the haze distinguishing cloth range judging device based on the color remote sensing image according to the present invention includes:
the acquisition module 1 is used for acquiring RGB (red, green and blue) color remote sensing images of a target area;
the conversion module 2 is used for converting the RGB color remote sensing image into an HSV color space to obtain a saturation remote sensing image and a brightness remote sensing image;
the enhancing module 3 is used for enhancing the intensity of the haze pixel signal in the saturation remote sensing image to obtain a saturation haze pixel signal enhancing image, and enhancing the intensity of the haze pixel signal in the brightness remote sensing image to obtain a brightness haze pixel signal enhancing image;
and the judging and identifying module 4 is used for obtaining a haze pixel judging and identifying result distribution map of the target area according to the saturation haze pixel signal enhancement map and the brightness haze pixel signal enhancement map.
In this embodiment, optionally, an RGB color remote sensing image of a target area is obtained based on a visible light band remote sensor on a satellite, where the image includes data of three bands of red, green and blue, and before the RGB color remote sensing image is input into a computer for processing, it needs to be subjected to nonlinear processing steps such as atmospheric molecular scattering correction processing (processed image is bluish) and image enhancement processing (processed landmark target brightness is increased), and the processed RGB color remote sensing image has an effect similar to a visual human observation effect, as shown in fig. 3.
In this embodiment, it should be noted that, in order to be able to recognize that a pixel is a haze weather from a true color image, the image data of an RGB color remote sensing image needs to be converted from an RGB model to an HSV model, and a specific conversion method of the present invention is not limited in particular.
In this embodiment, as shown in fig. 4, in the saturation remote sensing image, the saturation corresponds to the signal intensity, the cloud saturation is close to 0, the saturations of the clear sky land and the clear sky sea are high and generally greater than 0.2, and the saturation of the haze area is low and is between the clear space area and the cloud area. Therefore, the signal intensity of the haze pixels in the original saturation remote sensing image is between the clear area and the cloud area, and the haze pixels are distinguished from other pixels well due to the fact that the signal intensity of the haze pixels is highlighted. Therefore, the signal intensity of the haze pixel is enhanced by converting the saturation data in the saturation remote sensing image into a dimensionless numerical value, specifically, the low saturation signal in the saturation remote sensing image is enhanced to be close to 1, the high saturation signal in the saturation remote sensing image is weakened to be close to 0, so that the cloud area and the haze area with the original low signal intensity are enhanced, the clear sky land and the clear sky sea with the original high signal intensity are inhibited, and the final saturation haze pixel signal enhancement diagram is obtained, as shown in fig. 6.
In this embodiment, it should be noted that, there are various calculation manners for converting the saturation data in the saturation remote sensing image into a dimensionless number, including but not limited to the following formula:
SE＝e-4S
SE＝1-S
SE＝1-S2
in this embodiment, it should be noted that, because the edge of the cloud area and the broken clouds produce a mixing effect with the ground surface of the lower ground, the saturation and brightness of these areas change significantly, and similar to the appearance of the haze, it is not easy to distinguish the haze. Therefore, the signal intensity of the cloud edge and the cloud-broken pixels is enhanced by adopting the method of enhancing the mixed pixel signal, as shown in fig. 7, the mixed pixel signal enhances the edge pixels of the cloud and the large-patch cloud in the image, and the enhancement factors are stronger, so that the purpose of enhancing the areas is achieved.
In this embodiment, after obtaining the mixed pixel signal enhancement map, since the cloud region (including the ragged clouds and the edge clouds) and the clear sky region in the mixed pixel signal enhancement map have strong signals and the intensity of the luminance signal of the ocean is weak, in order to suppress the signals in these non-haze regions, the intermediate value of the signal intensity is enhanced (close to the value 1), and the vicinity of the end point value of the signal intensity (the vicinity of the original value 0 and the vicinity of the original value 1) is weakened (close to the value 0), so as to achieve the purpose of enhancing the signal intensity of the haze pixel, and the obtained luminance haze pixel signal enhancement map is as shown in fig. 8.
In this embodiment, the pixel signal intensities in the saturation haze pixel signal enhancement map and the brightness haze pixel signal enhancement map are multiplied respectively to obtain the pixel index corresponding to each pixel, as shown in fig. 9, the pixel signal intensity in the map is higher than that of the haze pixel. Comparing the pixel index of each pixel with a preset threshold, judging the pixel as a haze pixel when the pixel index is greater than or equal to the preset threshold, judging the pixel as a non-haze pixel when the pixel index is less than the preset threshold, and finally obtaining a haze pixel judgment result distribution graph of the target area, as shown in fig. 10.
The haze distinguishing cloth range judging and identifying device based on the color remote sensing image firstly obtains an RGB color remote sensing image of a target area, and then converts the RGB color remote sensing image into an HSV color space to obtain a saturation remote sensing image and a brightness remote sensing image. Further, strength enhancement is carried out on the haze pixel signals in the saturation remote sensing image to obtain a saturation haze pixel signal enhancement image, strength enhancement is carried out on the haze pixel signals in the brightness remote sensing image to obtain a brightness haze pixel signal enhancement image, and finally a haze pixel identification result distribution image of the target area is obtained according to the saturation haze pixel signal enhancement image and the brightness haze pixel signal enhancement image. According to the invention, through the technical route of converting RGB color remote sensing images collected by a satellite into HSV, enhancing saturation haze pixel signals and enhancing brightness haze pixel signals, and utilizing the enhancement results of the saturation haze pixel signals and the enhancement results of the brightness haze pixel signals to judge haze pixels, the judgment of haze weather influence ranges based on true color remote sensing images is realized, the haze weather influence ranges in the images can be identified only by depending on information provided by the satellite color remote sensing images, the accuracy is high, the coverage area is wide, and the practicability is strong.
Fig. 13 illustrates a physical structure diagram of an electronic device, and as shown in fig. 13, the electronic device may include: a processor (processor)1310, a communication Interface (Communications Interface)1320, a memory (memory)1330 and a communication bus 1340, wherein the processor 1310, the communication Interface 1320 and the memory 1330 communicate with each other via the communication bus 1340. The processor 1310 may call logic instructions in the memory 1330 to execute a haze distinguishing range judging method based on the color remote sensing image, the method comprising: acquiring an RGB (red, green and blue) color remote sensing image of a target area; converting the RGB color remote sensing image into an HSV color space to obtain a saturation remote sensing image and a brightness remote sensing image; enhancing the intensity of the haze pixel signal in the saturation remote sensing image to obtain a saturation haze pixel signal enhancement image, and enhancing the intensity of the haze pixel signal in the brightness remote sensing image to obtain a brightness haze pixel signal enhancement image; and obtaining a haze pixel identification result distribution diagram of the target area according to the saturation haze pixel signal enhancement diagram and the brightness haze pixel signal enhancement diagram.
In addition, the logic instructions in the memory 1330 may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In another aspect, the present invention further provides a computer program product, where the computer program product includes a computer program, the computer program can be stored on a non-transitory computer-readable storage medium, and when the computer program is executed by a processor, a computer can execute the method for determining a haze distribution range based on a color remote sensing image, where the method includes: acquiring an RGB (red, green and blue) color remote sensing image of a target area; converting the RGB color remote sensing image into an HSV color space to obtain a saturation remote sensing image and a brightness remote sensing image; enhancing the intensity of the haze pixel signal in the saturation remote sensing image to obtain a saturation haze pixel signal enhancement image, and enhancing the intensity of the haze pixel signal in the brightness remote sensing image to obtain a brightness haze pixel signal enhancement image; and obtaining a haze pixel identification result distribution diagram of the target area according to the saturation haze pixel signal enhancement diagram and the brightness haze pixel signal enhancement diagram.
In still another aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for determining a haze distribution range based on a color remote sensing image according to the foregoing methods, the method including: acquiring an RGB (red, green and blue) color remote sensing image of a target area; converting the RGB color remote sensing image into an HSV color space to obtain a saturation remote sensing image and a brightness remote sensing image; enhancing the intensity of the haze pixel signal in the saturation remote sensing image to obtain a saturation haze pixel signal enhancement image, and enhancing the intensity of the haze pixel signal in the brightness remote sensing image to obtain a brightness haze pixel signal enhancement image; and obtaining a haze pixel identification result distribution diagram of the target area according to the saturation haze pixel signal enhancement diagram and the brightness haze pixel signal enhancement diagram.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. With this understanding in mind, the above-described technical solutions may be embodied in the form of a software product, which can be stored in a computer-readable storage medium such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will 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; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A haze distinguishing cloth range identification method based on a color remote sensing image is characterized by comprising the following steps:
acquiring an RGB (red, green and blue) color remote sensing image of a target area;
converting the RGB color remote sensing image into an HSV color space to obtain a saturation remote sensing image and a brightness remote sensing image;
enhancing the intensity of the haze pixel signal in the saturation remote sensing image to obtain a saturation haze pixel signal enhancement image, and enhancing the intensity of the haze pixel signal in the brightness remote sensing image to obtain a brightness haze pixel signal enhancement image;
and obtaining a haze pixel identification result distribution diagram of the target area according to the saturation haze pixel signal enhancement diagram and the brightness haze pixel signal enhancement diagram.
2. The haze distinguishing cloth range judging method based on the color remote sensing image according to claim 1, wherein the RGB color remote sensing image is converted into HSV color space to obtain a saturation remote sensing image and a brightness remote sensing image, and the method comprises the following steps:
converting the RGB color remote sensing image according to the following formula:
V＝max(R,G,B)
where H is hue data, S is saturation data, V is brightness data, R is a red channel, G is a green channel, and B is a blue channel.
3. The haze distinguishing cloth range judging method based on the color remote sensing image according to claim 1, wherein the haze pixel signal intensity in the saturation remote sensing image is enhanced to obtain a saturation haze pixel signal enhancement map, and the method comprises the following steps:
and enhancing the low saturation signal in the saturation remote sensing image, reducing the high saturation signal in the saturation remote sensing image, and obtaining a saturation haze pixel signal enhancement map.
4. The method for judging the haze distinguishing cloth range based on the color remote sensing image according to claim 1, wherein the method for enhancing the haze pixel signal intensity in the luminance remote sensing image to obtain a luminance haze pixel signal enhancement map comprises the following steps:
enhancing a high-low brightness target mixed pixel signal in the brightness remote sensing image to obtain a mixed pixel signal enhancement image, wherein the high-low brightness target mixed pixel signal is a cloud edge pixel signal and a broken cloud pixel signal in the brightness remote sensing image;
and enhancing the medium-brightness signal in the mixed pixel signal enhancement image, and reducing the high-brightness signal and the low-brightness signal in the mixed pixel signal enhancement image to obtain a brightness haze pixel signal enhancement image.
5. The haze distinguishing cloth range distinguishing method based on the color remote sensing image according to claim 1, wherein a haze pixel distinguishing result distribution map of a target region is obtained according to the saturation haze pixel signal enhancement map and the brightness haze pixel signal enhancement map, and the method comprises the following steps:
calculating the pixel index of each pixel according to the pixel signal intensity value in the saturation haze pixel signal enhancement image and the pixel signal intensity value in the brightness haze pixel signal enhancement image;
and determining the pixels with the pixel indexes larger than a preset threshold value as haze pixels, and generating a haze pixel identification result distribution graph of the target area.
6. The method for judging the haze distinguishing cloth range based on the color remote sensing image according to claim 5, wherein the step of calculating the pixel index of each pixel according to the pixel signal intensity value in the saturation haze pixel signal enhancement map and the pixel signal intensity value in the brightness haze pixel signal enhancement map comprises the following steps:
calculating a pixel index I of each pixel according to the following formulaHazThe formula is as follows:
IHaz＝SE·VE
wherein SE is the pixel signal intensity value in the saturation haze pixel signal enhancement map, and VE is the pixel signal intensity value in the brightness haze pixel signal enhancement map.
7. The haze distinguishing cloth range judging method based on the color remote sensing image according to claim 1, wherein the RGB color remote sensing image is a true color remote sensing image which is subjected to atmospheric molecular scattering correction and image brightness enhancement.
8. The utility model provides a cloth range is distinguished to haze based on colored remote sensing picture judges and knows device which characterized in that includes:
the acquisition module is used for acquiring RGB (red, green and blue) color remote sensing images of the target area;
the conversion module is used for converting the RGB color remote sensing image into an HSV color space to obtain a saturation remote sensing image and a brightness remote sensing image;
the enhancement module is used for enhancing the intensity of the haze pixel signals in the saturation remote sensing image to obtain a saturation haze pixel signal enhancement image, and enhancing the intensity of the haze pixel signals in the brightness remote sensing image to obtain a brightness haze pixel signal enhancement image;
and the judging and identifying module is used for obtaining a haze pixel judging and identifying result distribution graph of the target area according to the saturation haze pixel signal enhancement graph and the brightness haze pixel signal enhancement graph.
9. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method for determining the haze distribution range based on the color remote sensing image according to any one of claims 1 to 7.
10. A non-transitory computer readable storage medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, implements the steps of the method for judging haze distinguishing distribution range according to any one of claims 1 to 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115290526A (en) * | 2022-09-29 | 2022-11-04 | 南通炜秀环境技术服务有限公司 | Air pollutant concentration detection method based on data analysis |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112435184A (en) * | 2020-11-18 | 2021-03-02 | 西安理工大学 | Haze sky image identification method based on Retinex and quaternion |
| CN113822816A (en) * | 2021-09-25 | 2021-12-21 | 李蕊男 | Haze removing method for single remote sensing image optimized by aerial fog scattering model |
-
2022
- 2022-01-06 CN CN202210009905.XA patent/CN114399438A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112435184A (en) * | 2020-11-18 | 2021-03-02 | 西安理工大学 | Haze sky image identification method based on Retinex and quaternion |
| CN113822816A (en) * | 2021-09-25 | 2021-12-21 | 李蕊男 | Haze removing method for single remote sensing image optimized by aerial fog scattering model |
Non-Patent Citations (1)
| Title |
|---|
| 曹绪民;刘春晓;张金栋;林宇航;赵锦威;: "基于亮度对比度增强与饱和度补偿的快速图像去雾算法", 计算机辅助设计与图形学学报, no. 10, 15 October 2018 (2018-10-15) * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115290526A (en) * | 2022-09-29 | 2022-11-04 | 南通炜秀环境技术服务有限公司 | Air pollutant concentration detection method based on data analysis |
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