Night light remote sensing image saturation correction method and device

Abstract

The present invention provides a method and device for saturation correction of nighttime light remote sensing images, the method comprising: obtaining the power consumption corresponding to the first area when the first area is determined to be in a saturated state according to the statistical characteristics of the actual values ​​of all pixels in the first area in the first nighttime light remote sensing image; obtaining the sum of the correction values ​​of all pixels in the first area according to the power consumption corresponding to the first area and an inversion model; wherein the inversion model is pre-established according to the sum of the actual values ​​of pixels in the second area in the second nighttime light remote sensing image and the power consumption corresponding to the second area, and the second area is in an unsaturated state; calculating the correction value of each pixel in the first area according to the vegetation index of each pixel in the first area and the sum of the correction values, and replacing the actual value of each pixel in the first area with the correction value. The present invention improves the saturation correction effect of nighttime light remote sensing images, and the method is simple and easy to implement.

Description

Technical Field

[0001] The present invention relates to the technical field of remote sensing image processing, and in particular to a method and device for nighttime light remote sensing image saturation correction. Background Art

[0002] Nighttime lights are directly related to human activities, and the density and intensity of lighting facilities reflect the intensity of human activities and economic prosperity in the region to a certain extent. The pixel value of lights in nighttime light remote sensing images is an indicator of the degree of urban development, and can also reflect the negative impact of various disasters on human activities.

[0003] DMSP-OLS (Defense Meteorological Satellite Program Operational Linescan System) is a space-borne night light imager used to obtain night light remote sensing images of the Earth's surface. Since the quantization level of the DMSP-OLS night light imager is only 6 bits, the pixel value range of the night light remote sensing image recorded by the sensor is limited to 0 to 63.

[0004] Cities are the centers of human activities, and urban agglomerations are a reflection of the level of economic development. In urban areas with a high level of economic development, especially in core urban areas, the pixel value of lights usually exceeds the 6-bit recording limit of the sensor, resulting in the phenomenon of saturation of the pixel value of lights in night light remote sensing images in urban areas, thereby reducing the accuracy and reliability of the analysis results of night light remote sensing images, such as using the remote sensing images for quantitative analysis of economic, population and other related parameters. Therefore, saturation correction of light pixels in night light remote sensing images is of great significance for promoting the application of night light remote sensing images in the field of urban and urban agglomeration monitoring.

[0005] At present, there are two main ways to perform saturation correction on light pixel values ​​in night light remote sensing images. One is to use remote sensing images calibrated with night light radiation to correct night light remote sensing images; the other is to use remote sensing vegetation index to qualitatively increase the dynamic range of light pixel values ​​in night light remote sensing images. Among them, remote sensing data calibrated with night light radiation is not easy to obtain, and it is difficult to meet the needs of saturation correction of light pixel values ​​in large-scale and long-time series. Qualitatively increasing the dynamic range of light pixel values ​​in night light remote sensing images has unclear physical meaning and poor saturation correction effect. Summary of the invention

[0006] The present invention provides a method and device for saturation correction of night light remote sensing images, which are used to solve the problems in the prior art that saturation correction of night light remote sensing images is difficult to implement and the correction effect is poor, thereby improving the effect of saturation correction of night light remote sensing images and being easy to implement.

[0007] The present invention provides a nighttime light remote sensing image saturation correction method, comprising:

[0008] When it is determined that the first area is in a saturated state according to statistical characteristics of actual values ​​of all pixels in the first area in the first nighttime light remote sensing image, obtaining power consumption corresponding to the first area;

[0009] According to the power consumption corresponding to the first area and the inversion model, obtaining the sum of correction values ​​of all pixels in the first area;

[0010] The inversion model is pre-established based on the sum of actual values ​​of pixels in the second area in the second nighttime light remote sensing image and the power consumption corresponding to the second area, and the second area is in an unsaturated state;

[0011] The correction value of each pixel in the first area is calculated according to the vegetation index of each pixel in the first area and the sum of the correction values, and the actual value of each pixel in the first area is replaced by the correction value of each pixel in the first area.

[0012] According to a nighttime light remote sensing image saturation correction method and device provided by the present invention, the correction value of each pixel in the first area is calculated according to the vegetation index of each pixel in the first area and the sum of the correction values, including:

[0013] Obtaining a vegetation index for each pixel in the first area;

[0014] The correction value of each pixel in the first area is obtained by multiplying the difference between 1 and the vegetation index of each pixel in the first area by the sum of the correction values ​​and dividing the result by the sum of the difference values ​​corresponding to all pixels in the first area.

[0015] According to a nighttime light remote sensing image saturation correction method provided by the present invention, the correction value of each pixel in the first area is obtained by multiplying the difference between 1 and the vegetation index of each pixel in the first area by the sum of the correction values, and then dividing the result by the sum of the difference corresponding to all pixels in the first area, and the correction value of each pixel in the first area is obtained, which also includes:

[0016] According to the vegetation index of each pixel in the first area, identifying a water area in the first area, and setting the actual value of the pixel in the water area to 0;

[0017] The water area is removed from the first area.

[0018] According to a nighttime light remote sensing image saturation correction method provided by the present invention, the statistical features include one or more of the sum, mean, median, mode, maximum value and minimum value.

[0019] According to a nighttime light remote sensing image saturation correction method provided by the present invention, the statistical features include a maximum value and a mode;

[0020] Accordingly, when it is determined that the first area is in a saturated state according to the statistical characteristics of the actual values ​​of all pixels in the first area in the first nighttime light remote sensing image, obtaining the power consumption corresponding to the first area also includes:

[0021] When the maximum value is a preset constant and the mode is greater than a first preset threshold and less than or equal to the preset constant, it is determined that the first region is in a saturation state; wherein the preset constant is greater than the first preset threshold.

[0022] According to a nighttime light remote sensing image saturation correction method provided by the present invention, the correction value of each pixel in the first area is calculated according to the vegetation index of each pixel in the first area and the sum of the correction values, and the method also includes:

[0023] Counting the maximum value and the mode of the actual values ​​of all pixels in the second area;

[0024] When the maximum value corresponding to the second area is smaller than the preset constant and the mode corresponding to the second area is smaller than a second preset threshold, it is determined that the second area is in an unsaturated state; wherein the second preset threshold is smaller than the first preset threshold.

[0025] The present invention also provides a nighttime light remote sensing image saturation correction device, comprising:

[0026] an acquisition module, configured to acquire the power consumption corresponding to the first area when it is determined that the first area is in a saturated state according to the statistical characteristics of the actual values ​​of all pixels in the first area in the first nighttime light remote sensing image;

[0027] an inversion module, configured to obtain a sum of correction values ​​of all pixels in the first area according to the power consumption corresponding to the first area and an inversion model;

[0028] The inversion model is pre-established based on the sum of actual values ​​of pixels in the second area in the second nighttime light remote sensing image and the power consumption corresponding to the second area, and the second area is in an unsaturated state;

[0029] A calculation module is used to calculate the correction value of each pixel in the first area according to the vegetation index of each pixel in the first area and the sum of the correction values, and use the correction value of each pixel in the first area to replace the actual value of each pixel in the first area.

[0030] According to a nighttime light remote sensing image saturation correction device provided by the present invention, the calculation module is used for:

[0031] Obtaining a vegetation index for each pixel in the first area;

[0032] The correction value of each pixel in the first area is obtained by multiplying the difference between 1 and the vegetation index of each pixel in the first area by the sum of the correction values ​​and dividing the result by the sum of the difference values ​​corresponding to all pixels in the first area.

[0033] The present invention also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps of any of the above-mentioned methods for saturation correction of nighttime light remote sensing images are implemented.

[0034] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the steps of any of the above-mentioned methods for nighttime light remote sensing image saturation correction are implemented.

[0035] The nighttime light remote sensing image saturation correction method and device provided by the present invention construct an inversion model based on the high correlation between the power consumption in an unsaturated light area and the sum of the pixel values ​​of the light remote sensing image, input the power consumption in the saturated area into the inversion model to calculate the sum of the pixel values ​​of the saturated area corrected to an unsaturated state, and combine the vegetation index of each pixel to calculate the correction value of each pixel, thereby improving the saturation correction effect, and the method is simple and easy to implement. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 This is one of the flow charts of the nighttime light remote sensing image saturation correction method provided by the present invention;

[0038] Figure 2 This is the second flow chart of the nighttime light remote sensing image saturation correction method provided by the present invention;

[0039] Figure 3 It is a structural schematic diagram of the nighttime light remote sensing image saturation correction device provided by the present invention;

[0040] Figure 4 is a schematic diagram of the structure of an electronic device provided by the present invention; DETAILED DESCRIPTION

[0041] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Combine the following Figure 1 The nighttime light remote sensing image saturation correction method of the present invention is described, and the method comprises: step 101, when it is determined that the first area is in a saturated state according to the statistical characteristics of the actual values ​​of all pixels in the first area of ​​the first nighttime light remote sensing image, obtaining the power consumption corresponding to the first area;

[0043] Optionally, the first nighttime light remote sensing image is a DMSP-OLS nighttime light remote sensing image.

[0044] DMSP-OLS nighttime light remote sensing images can be either stable light data products or raw average light data products.

[0045] Optionally, the first area is a lighting area that requires saturation correction, such as an administrative area or an urban area.

[0046] One or more first regions that need to be corrected are extracted from the first nighttime light remote sensing image according to a predetermined region division vector diagram.

[0047] The regional division vector map may be a city-level regional division map, or a regional division map at other levels such as a county or district level.

[0048] Optionally, the actual value of the pixel is the actual grayscale value of the pixel.

[0049] The actual values ​​of the pixels are counted to obtain statistical features. This embodiment is not limited to the type of statistical features.

[0050] According to the statistical characteristics of the actual values ​​of the pixels in the first region, it is determined whether the first region is in a saturated state or an unsaturated state. The first region in a saturated state is corrected. This embodiment does not limit the determination method of the saturated state and the unsaturated state.

[0051] When the first region is in a saturated state, actual values ​​of pixels in the first region are corrected.

[0052] The saturation state of the first area is caused by the light in the first area, and the light is related to the power consumption.

[0053] The power consumption is the total power consumption of the first region corresponding to the geographical location, or the power consumption directly related to the nighttime lighting.

[0054] Step 102, obtaining the sum of correction values ​​of all pixels in the first area according to the power consumption corresponding to the first area and the inversion model;

[0055] The inversion model is pre-established based on the sum of actual values ​​of pixels in the second area in the second nighttime light remote sensing image and the power consumption corresponding to the second area, and the second area is in an unsaturated state;

[0056] Optionally, the inversion model is a power function. The second nighttime light remote sensing image is a DMSP-OLS nighttime light remote sensing image. The first nighttime light remote sensing image and the second nighttime light remote sensing image are the same image or different images.

[0057] It should be noted that for the DMSP-OLS nighttime light remote sensing image, the light value saturation correction requires that the first nighttime light remote sensing image and the second nighttime light remote sensing image are from the same year. For other nighttime light remote sensing images, such as NPP-VIIRS, it is not necessary to require that the two images are from the same year.

[0058] According to the actual values ​​of the pixels in the second area in an unsaturated state and the power consumption of the second area, an inversion model is established by using a statistical analysis method.

[0059] Before establishing the inversion model, the actual values ​​of the pixels in the second region in the unsaturated state are counted to obtain the statistical features of the pixels in the second region. The correlation coefficient between each statistical feature and the power consumption of the second region is calculated, as shown in Table 1. It can be seen that the correlation coefficient between the power consumption and the sum of the pixel values ​​is the largest, so the sum of the actual values ​​of the pixels in the second region is selected to establish the inversion model.

[0060] Table 1 Correlation coefficients between various statistical characteristics and electricity consumption

[0061]

[0062] The inversion model is a relationship model between the power consumption in the unsaturated area and the sum of the actual values ​​of the pixels in the area.

[0063] The power consumption of the first region is input into the inversion model to obtain the sum of actual values ​​of pixels in the first region in an unsaturated state, and the sum of actual values ​​of pixels in the first region in an unsaturated state is used as the sum of correction values ​​for saturation correction of the first region.

[0064] Step 103, calculating the correction value of each pixel in the first area according to the vegetation index of each pixel in the first area and the sum of the correction values, and using the correction value of each pixel in the first area to replace the actual value of each pixel in the first area.

[0065] The correction value of each pixel is a value after correction is performed on each pixel.

[0066] The vegetation index of each pixel in the first area can have two sources: one is to calculate the vegetation index using remote sensing images, and the other is to use existing remote sensing vegetation index products, that is, the vegetation index has been calculated using remote sensing images according to the remote sensing vegetation index calculation formula.

[0067] The proportion of the correction value of each pixel in the total correction value is determined according to the vegetation index of each pixel in the first area. The correction value of each pixel is determined according to the total correction value and the proportion of the correction value of each pixel, so as to spatialize the total correction value. The complete saturation correction process is as follows: Figure 2 shown.

[0068] This embodiment constructs an inversion model based on the high correlation between the power consumption in the unsaturated area of ​​the night light remote sensing image and the sum of the pixel values ​​of the night light remote sensing image. The power consumption in the saturated area is input into the inversion model to calculate the sum of the pixel values ​​of the saturated area corrected to the unsaturated state. Combined with the vegetation index of each pixel, the correction value of each pixel is calculated, thereby improving the saturation correction effect. The method is simple and easy to implement.

[0069] Based on the above embodiment, the method of calculating the correction value of each pixel in the first area according to the vegetation index of each pixel in the first area and the sum of the correction values ​​in this embodiment includes: obtaining the vegetation index of each pixel in the first area;

[0070] Optionally, the vegetation index of each pixel is calculated based on the vegetation index calculation formula according to the actual value of each pixel in the remote sensing image of the first area acquired by the satellite. In addition, the remote sensing vegetation index product of the first area can also be directly acquired.

[0071] According to the definition of remote sensing vegetation index, the vegetation index of each pixel is normalized to between 0 and 1.

[0072] The correction value of each pixel in the first area is obtained by multiplying the difference between 1 and the vegetation index of each pixel in the first area by the sum of the correction values ​​and dividing the result by the sum of the difference values ​​corresponding to all pixels in the first area.

[0073] The vegetation index of the vegetation area is larger, the difference is smaller, and the proportion of the difference in the total correction value is smaller. Therefore, the correction value calculation formula for each pixel value in the first area is as follows:

[0074]

[0075] Among them, y i represents the correction value of the i-th pixel in the first area, Z i represents the vegetation index of the i-th pixel in the first region, Z j represents the vegetation index of the jth pixel in the first area, N represents the total number of pixels in the first area, and S represents the sum of correction values.

[0076] In this embodiment, by determining the proportion of the correction value of each pixel according to the vegetation index of each pixel, the obtained correction value of each pixel is closer to the real scene, thereby improving the correction effect.

[0077] Based on the above embodiment, the present embodiment multiplies the difference of 1 minus the vegetation index of each pixel in the first area by the sum of the correction values, and then divides the result by the sum of the differences corresponding to all pixels in the first area to obtain the correction value of each pixel in the first area. The method also includes: identifying the water area in the first area according to the vegetation index of each pixel in the first area, setting the actual value of the pixel in the water area to 0; and eliminating the water area from the first area.

[0078] Specifically, according to the physical meaning of the remote sensing vegetation index, the vegetation index of the water body area is less than 0, and the area composed of pixels with a vegetation index less than 0 is regarded as the water body area.

[0079] Since the water area does not generate light, the pixel value of the water area in the corrected image should be 0. Therefore, the pixel value of the water area is set to 0. The reason why it is not 0 before correction is that the water body reflects the light of other pixels, resulting in the water pixel being not 0.

[0080] By setting the pixel values ​​of the water area to 0, the pixel value correction of the water area has been completed, and there is no need to complete the pixel value correction through the light value inversion model. Therefore, the water area is removed from the first area, and the saturation correction is performed on the first area after the water area is removed.

[0081] This embodiment eliminates the water area that does not require saturation correction, thereby reducing the amount of calculation; and reduces the influence of the water area on the saturation correction, thereby improving the saturation correction effect.

[0082] On the basis of the above embodiments, the statistical features described in this embodiment include one or more of the sum, mean, median, mode, maximum value and minimum value.

[0083] The sum is the sum of the actual values ​​of all pixels. The mean is the average of the actual values ​​of all pixels. The median is the value in the middle of the actual values ​​of all pixels. The mode is the value that appears most frequently among the actual values ​​of all pixels.

[0084] In this embodiment, the statistical characteristics of the actual values ​​of the pixels are not limited to the above six types.

[0085] On the basis of the above embodiment, the statistical features in this embodiment include a maximum value and a mode; accordingly, when it is determined that the first area is in a saturated state according to the statistical features of the actual values ​​of all pixels in the first area in the first nighttime light remote sensing image, the power consumption corresponding to the first area is obtained, which also includes:

[0086] When the maximum value is a preset constant and the mode is greater than a first preset threshold and less than or equal to the preset constant, it is determined that the first region is in a saturation state; wherein the preset constant is greater than the first preset threshold.

[0087] Specifically, when the first nighttime light remote sensing image is a DMSP-OLS nighttime light remote sensing image, the actual value of the pixel in the first area ranges from 0 to 63.

[0088] In this case, the preset constant is 63, and the first preset threshold is close to 63. When the maximum value of the actual values ​​of all pixels in the first region is 63, and the mode is close to the maximum value, the first region is determined to be in a saturated state. Saturation correction is performed on the saturated first region.

[0089] On the basis of the above embodiments, the correction value of each pixel in the first area is calculated according to the vegetation index of each pixel in the first area and the sum of the correction values ​​in this embodiment, and it also includes: counting the maximum value and mode of the actual values ​​of all pixels in the second area; when the maximum value corresponding to the second area is less than the preset constant and the mode corresponding to the second area is less than the second preset threshold, determining that the second area is in an unsaturated state; wherein the second preset threshold is less than the first preset threshold.

[0090] Specifically, when the second nighttime light remote sensing image and the first nighttime light remote sensing image are the same image, other regions in the first nighttime light remote sensing image except the first region in a saturated state may be regarded as the second region in an unsaturated state.

[0091] When the second nighttime light remote sensing image and the first nighttime light remote sensing image are the same image or different images, it can be determined whether the second area is in an unsaturated state based on the value range and statistical characteristics of the actual values ​​of all pixels in the second area.

[0092] In the case of two different images, they need to be acquired by the same satellite sensor. Different sensors have different quantization levels, resulting in different meanings for the same pixel value. Since the inversion model establishes the relationship between pixel value and power consumption, the pixel values ​​in the two images need to represent the same meaning.

[0093] When the two images are obtained by different sensors and have different quantization levels, they can also be converted to the same quantization level through image preprocessing. This embodiment is applicable to saturation correction of night light remote sensing images of various quantization levels, such as 6 bits, 8 bits and 12 bits.

[0094] In the case of determining whether the second region is in an unsaturated state based on the value range and statistical characteristics of the actual values ​​of all pixels in the second region, if the second nighttime light remote sensing image is a DMSP-OLS nighttime light remote sensing image, the preset constant is 63, and the second preset threshold is close to 0. When the maximum value of the actual values ​​of all pixels in the second region is less than 63, and the mode is close to 0, the second region is determined to be in an unsaturated state. The inversion model is constructed using the sum of the actual values ​​of the pixels in the unsaturated second region and the power consumption of the region.

[0095] The night light remote sensing image saturation correction device provided by the present invention is described below. The night light remote sensing image saturation correction device described below and the night light remote sensing image saturation correction method described above can be referenced to each other.

[0096] like Figure 3 As shown, the device includes an acquisition module 301, an inversion module 302 and a calculation module 303, wherein:

[0097] The acquisition module 301 is used to acquire the power consumption corresponding to the first area when it is determined that the first area is in a saturated state according to the statistical characteristics of the actual values ​​of all pixels in the first area in the first nighttime light remote sensing image;

[0098] The actual values ​​of the pixels are counted to obtain statistical features. This embodiment is not limited to the type of statistical features.

[0099] According to the statistical characteristics of the actual values ​​of the pixels in the first region, it is determined whether the first region is in a saturated state or an unsaturated state. The first region in a saturated state is corrected. This embodiment does not limit the determination method of the saturated state and the unsaturated state.

[0100] When the first region is in a saturated state, actual values ​​of pixels in the first region are corrected.

[0101] Because night light remote sensing images record light data, and light is related to electricity consumption.

[0102] The power consumption is the total power consumption of the first region corresponding to the geographical location, or the power consumption directly related to the nighttime lighting.

[0103] The inversion module 302 is used to obtain the sum of correction values ​​of all pixels in the first area according to the power consumption corresponding to the first area and the inversion model;

[0104] The inversion model is pre-established based on the sum of actual values ​​of pixels in the second area in the second nighttime light remote sensing image and the power consumption corresponding to the second area, and the second area is in an unsaturated state;

[0105] Optionally, the inversion model is a power function. The second nighttime light remote sensing image is a DMSP-OLS nighttime light remote sensing image. The first nighttime light remote sensing image and the second nighttime light remote sensing image are the same image or different images.

[0106] It should be noted that for the DMSP-OLS nighttime light remote sensing image, the light value saturation correction requires that the first nighttime light remote sensing image and the second nighttime light remote sensing image are from the same year. For other nighttime light remote sensing images, such as NPP-VIIRS, it is not necessary to require that the two images are from the same year.

[0107] According to the actual values ​​of the pixels in the second area in an unsaturated state and the power consumption of the second area, an inversion model is established by using a statistical analysis method.

[0108] The power consumption of the first region is input into the inversion model to obtain the sum of actual values ​​of pixels in the first region in an unsaturated state, and the sum of actual values ​​of pixels in the first region in an unsaturated state is used as the sum of correction values ​​for saturation correction of the first region.

[0109] The calculation module 303 is used to calculate the correction value of each pixel in the first area according to the vegetation index of each pixel in the first area and the sum of the correction values, and use the correction value of each pixel in the first area to replace the actual value of each pixel in the first area.

[0110] The correction value of each pixel is a value after correction is performed on each pixel.

[0111] The proportion of the correction value of each pixel in the total correction value is determined according to the vegetation index of each pixel in the first area. The correction value of each pixel is determined according to the total correction value and the proportion of the correction value of each pixel, so as to spatialize the total correction value.

[0112] This embodiment constructs an inversion model based on the high correlation between the power consumption in the unsaturated area of ​​the night light remote sensing image and the sum of the pixel values ​​of the night light remote sensing image. The power consumption in the saturated area is input into the inversion model to calculate the sum of the pixel values ​​of the saturated area corrected to the unsaturated state. Combined with the vegetation index of each pixel, the correction value of each pixel is calculated, thereby improving the saturation correction effect. The method is simple and easy to implement.

[0113] Based on the above embodiments, the calculation module in this embodiment is used to: obtain the vegetation index of each pixel in the first area; multiply the difference between 1 and the vegetation index of each pixel in the first area by the sum of the correction values, and divide it by the sum of the differences corresponding to all pixels in the first area to obtain the correction value of each pixel in the first area.

[0114] Based on the above embodiment, this embodiment also includes a removal module, which is used to identify the water area in the first area according to the vegetation index of each pixel in the first area, set the actual value of the pixel in the water area to 0; and remove the water area from the first area.

[0115] On the basis of the above embodiments, the statistical features described in this embodiment include one or more of the sum, mean, median, mode, maximum value and minimum value.

[0116] Based on the above embodiments, the statistical features in this embodiment include a maximum value and a mode; accordingly, it also includes a determination module for: when the maximum value is a preset constant and the mode is greater than a first preset threshold and less than or equal to the preset constant, determining that the first region is in a saturation state; wherein the preset constant is greater than the first preset threshold.

[0117] On the basis of the above embodiments, the determination module in this embodiment is also used to: count the maximum value and mode of the actual values ​​of all pixels in the second area; when the maximum value corresponding to the second area is less than the preset constant and the mode corresponding to the second area is less than a second preset threshold, determine that the second area is in an unsaturated state; wherein the second preset threshold is less than the first preset threshold.

[0118] Figure 4An example of a physical structure diagram of an electronic device is shown in FIG. Figure 4 As shown, the electronic device may include: a processor 410, a communication interface 420, a memory 430 and a communication bus 440, wherein the processor 410, the communication interface 420 and the memory 430 communicate with each other through the communication bus 440. The processor 410 may call the logic instructions in the memory 430 to execute the night light remote sensing image saturation correction method, the method comprising: obtaining the power consumption corresponding to the first area in the first night light remote sensing image when the first area is determined to be in a saturated state according to the statistical characteristics of the actual values ​​of all pixels in the first area; obtaining the sum of the correction values ​​of all pixels in the first area according to the power consumption corresponding to the first area and the inversion model; wherein the inversion model is pre-established according to the sum of the actual values ​​of the pixels in the second area in the second night light remote sensing image and the power consumption corresponding to the second area, and the second area is in an unsaturated state; calculating the correction value of each pixel in the first area according to the vegetation index of each pixel in the first area and the sum of the correction values, and replacing the actual value of each pixel in the first area with the correction value of each pixel in the first area.

[0119] In addition, the logic instructions in the above-mentioned memory 430 can be implemented in the form of a software functional unit and can be stored in a computer-readable storage medium when it is sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0120] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the night light remote sensing image saturation correction method provided by the above methods, the method comprising: when it is determined that the first area is in a saturated state based on the statistical characteristics of the actual values ​​of all pixels in the first area in the first night light remote sensing image, obtaining the power consumption corresponding to the first area; obtaining the sum of the correction values ​​of all pixels in the first area based on the power consumption corresponding to the first area and an inversion model; wherein the inversion model is pre-established based on the sum of the actual values ​​of pixels in the second area in the second night light remote sensing image, and the power consumption corresponding to the second area, and the second area is in an unsaturated state; calculating the correction value of each pixel in the first area based on the vegetation index of each pixel in the first area and the sum of the correction values, and replacing the actual value of each pixel in the first area with the correction value of each pixel in the first area.

[0121] On the other hand, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned method for saturation correction of nighttime light remote sensing images, the method comprising: obtaining the power consumption corresponding to the first area in a first nighttime light remote sensing image when it is determined that the first area is in a saturated state based on the statistical characteristics of the actual values ​​of all pixels in the first area; obtaining the sum of the correction values ​​of all pixels in the first area based on the power consumption corresponding to the first area and an inversion model; wherein the inversion model is pre-established based on the sum of the actual values ​​of pixels in a second area in a second nighttime light remote sensing image and the power consumption corresponding to the second area, and the second area is in an unsaturated state; calculating the correction value of each pixel in the first area based on the vegetation index of each pixel in the first area and the sum of the correction values, and replacing the actual value of each pixel in the first area with the correction value of each pixel in the first area.

[0122] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0123] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0124] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for nighttime light remote sensing image saturation correction, characterized in that: include: When it is determined that the first area is in a saturated state according to the statistical characteristics of the actual values ​​of all pixels in the first area in the first nighttime light remote sensing image, the power consumption corresponding to the first area is obtained; the statistical characteristics include one or more of the sum, mean, median, mode, maximum value and minimum value; According to the power consumption corresponding to the first area and the inversion model, obtaining the sum of correction values ​​of all pixels in the first area; The inversion model is pre-established based on the sum of actual values ​​of pixels in the second area in the second nighttime light remote sensing image and the power consumption corresponding to the second area, and the second area is in an unsaturated state; Obtaining a vegetation index for each pixel in the first region; identifying a water region in the first region according to the vegetation index for each pixel in the first region, and setting actual values ​​of pixels in the water region to 0; and removing the water region from the first region; The correction value of each pixel in the first area is obtained by subtracting 1 from the vegetation index of each pixel in the first area and multiplying the result by the sum of the correction values, and then dividing the result by the sum of the difference values ​​corresponding to all pixels in the first area to obtain the correction value of each pixel in the first area, and the correction value of each pixel in the first area is used to replace the actual value of each pixel in the first area.

2. The nighttime light remote sensing image saturation correction method according to claim 1, characterized in that: The statistical features include maximum value and mode; Accordingly, when it is determined that the first area is in a saturated state according to the statistical characteristics of the actual values ​​of all pixels in the first area in the first nighttime light remote sensing image, obtaining the power consumption corresponding to the first area also includes: When the maximum value is a preset constant and the mode is greater than a first preset threshold and less than or equal to the preset constant, it is determined that the first region is in a saturation state; wherein the preset constant is greater than the first preset threshold.

3. The nighttime light remote sensing image saturation correction method according to claim 2, characterized in that: The step of obtaining a vegetation index for each pixel in the first region; identifying a water region in the first region according to the vegetation index for each pixel in the first region, and setting the actual value of the pixel in the water region to 0; and removing the water region from the first region; The correction value of each pixel in the first area is obtained by multiplying the difference between 1 and the vegetation index of each pixel in the first area by the sum of the correction values, and then dividing the result by the sum of the difference values ​​corresponding to all pixels in the first area, wherein the correction value of each pixel in the first area is obtained, wherein the method further includes: Counting the maximum value and the mode of the actual values ​​of all pixels in the second area; When the maximum value corresponding to the second area is smaller than the preset constant and the mode corresponding to the second area is smaller than a second preset threshold, it is determined that the second area is in an unsaturated state; wherein the second preset threshold is smaller than the first preset threshold.

4. A nighttime light remote sensing image saturation correction device, characterized in that: include: an acquisition module, configured to acquire the power consumption corresponding to the first area when it is determined that the first area is in a saturated state according to the statistical characteristics of the actual values ​​of all pixels in the first area in the first nighttime light remote sensing image; an inversion module, configured to obtain a sum of correction values ​​of all pixels in the first area according to the power consumption corresponding to the first area and an inversion model; The inversion model is pre-established based on the sum of actual values ​​of pixels in the second area in the second nighttime light remote sensing image and the power consumption corresponding to the second area, and the second area is in an unsaturated state; A calculation module is used to obtain the vegetation index of each pixel in the first area; identify the water area in the first area according to the vegetation index of each pixel in the first area, and set the actual value of the pixel in the water area to 0; eliminate the water area from the first area; multiply the difference between 1 and the vegetation index of each pixel in the first area by the sum of the correction values, and divide the result by the sum of the difference values ​​corresponding to all pixels in the first area to obtain the correction value of each pixel in the first area, and use the correction value of each pixel in the first area to replace the actual value of each pixel in the first area.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the nighttime light remote sensing image saturation correction method as described in any one of claims 1 to 3 are implemented.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the nighttime light remote sensing image saturation correction method as claimed in any one of claims 1 to 3 are implemented.

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