A battlefield situation assessment method based on night satellite light data

Abstract

The application discloses a battlefield situation assessment method based on night satellite light data, which comprises the following steps: acquiring night micro-light data, wherein the night micro-light data comprises pixel observation data, quality condition data and cloud condition data; performing calculation preprocessing on the quality condition data and the cloud condition data, and screening the night micro-light data sample according to the preprocessing result; according to the pixel gray value of the pixel observation data, presetting a typical light threshold value, marking the city light, and acquiring the relative gray scale diagram of the city light; according to the marking of the city light, calculating the number of light pixels and the average brightness of the light pixels; calculating the proportion of the poor quality data and the proportion of the cloud pollution data in the target region, calculating the relative light ratio of the two parameters of the number of light pixels and the average brightness of the light pixels of the target city and the control city, and obtaining the battlefield situation assessment result.

Description

Technical Field

[0001] This invention belongs to the field of remote sensing technology analysis, specifically relating to a battlefield situation assessment method based on nighttime satellite light data. Background Technology

[0002] Visible / infrared radiation imagers (VIIRS) and linear scanning operational system sensors (OLS) can detect the radiance of lights on the Earth's surface at night, and then generate light data that is transmitted back to the ground via satellite. In the past two decades, low-light detection sensors have been put into orbit one after another, and research and application technologies related to this type of light data are also developing rapidly.

[0003] The application of light data has been on the rise in recent years. Satellite data from the earlier DMSP-OLS satellite cannot fully meet the needs of ground calibration, while the NPP-VIIRS satellite launched in recent years has more advanced technology with higher spatial resolution, radiometric resolution and temporal resolution, which provides the possibility for more in-depth research.

[0004] In the past decade, this type of light data has been applied to post-earthquake disaster area status assessment and nighttime light environment studies in typical countries. WU Han et al. analyzed the recovery status of disaster areas based on the light index of severely affected areas, assessing the macro-recovery status of various cities and counties within intensity VII and above. Non-patent literature: Wu Han, Wang Ming, Liu Kai et al., based on nighttime light remote sensing data, assessed the recovery status of severely affected areas in the Wenchuan earthquake, demonstrating the application of light data in assessing post-disaster reconstruction effectiveness. Through intuitive comparison and analysis, nighttime light data can play a significant role in post-war assessment of post-war regions. The VNP46A2 product, developed based on NPP / VIIRS (National Polar-orbiting Partnership / Visible Infrared Imaging Radiometer) observation data, provides daily nighttime satellite light data. This product is publicly released by NASA, with the Mandatory_Quality_Flag subset containing satellite light data quality information and the QF_Cloud_Mask subset containing cloud mask information for satellite light data.

[0005] In the existing technology, there is no relevant research linking this light data with the war situation and making short-term assessments of the war zone and the development of the war. Therefore, this application proposes a battlefield situation assessment method based on nighttime satellite light data. Summary of the Invention

[0006] The purpose of this invention is to fill the gap in the existing technology for utilizing satellite low-light data, and to provide a battlefield situation assessment method based on nighttime satellite light data. This method can objectively assess the battlefield situation based on nighttime low-light data and provide analysis results of the war.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] A battlefield situation assessment method based on nighttime satellite light data includes the following steps:

[0009] Step 1: Acquire nighttime low-light data, which includes pixel observation data, quality status data, and cloud status data;

[0010] Step 2: Perform calculation and preprocessing on the quality status data and cloud status data, and filter the nighttime low light data samples according to the preprocessing results, deleting nighttime low light data with poor quality and cloud pollution in the target area.

[0011] Step 3: Based on the pixel grayscale values ​​of the pixel observation data, preset typical light thresholds, mark urban lights, and obtain a relative grayscale map of urban lights;

[0012] Step 4: Calculate the number of light pixels and the average brightness of the light pixels based on the city light markings.

[0013] Step 5: Calculate the proportion of poor-quality data and cloud-polluted data within the target area, and calculate the relative light ratio of two parameters: the number of light pixels and the average brightness of light pixels in the target city and the control city, to obtain the battlefield situation assessment results.

[0014] Specifically, the steps for calculating and preprocessing the quality status data and cloud status data are as follows:

[0015] Quality status data is obtained by using a subset of Mandatory_Quality_Flag data. A value of 02 indicates a poor main algorithm, and a value of 255 indicates no search fill value. The number of pixels with values ​​of 02 and 255 is counted to obtain poor quality nighttime low light data in the target area.

[0016] The cloud mask for each pixel is obtained by using a subset of the QF_Cloud_Mask data. The 6-7 bits are 00 for very clear, 01 for possibly clear, 10 for possibly cloudy, and 11 for definitely cloudy. The number of pixels with values ​​of 10 and 11 is counted to obtain the nighttime low light data of cloud pollution in the target area.

[0017] Furthermore, the marking of city lights described in step 3 includes the following steps:

[0018] With a preset typical light threshold of T, determine whether a light source is urban lighting based on the following formula:

[0019]

[0020] Where i and j are the row and column numbers of a pixel in the light data, respectively, and L i,j M represents the luminance value of the pixel located in the i-th row and j-th column. i,j This represents the light marker value of the pixel located in the i-th row and j-th column. 1 indicates that it is a city light marker, and 0 indicates that it is not a city light marker.

[0021] Specifically, the formulas for calculating the number of light pixels and the average brightness of the light pixels are as follows:

[0022]

[0023]

[0024] Among them, i max j is the maximum row number of the pixels within the target area. max Where L is the maximum column number of the pixels in the target area, N is the number of light pixels, and L is the maximum column number of the target area. ave This represents the average brightness of the light pixel.

[0025] Specifically, the formula for calculating the relative light ratio of the number of light pixels and the average brightness of light pixels in the target city and the control city is as follows:

[0026]

[0027]

[0028] Where, N tg N represents the number of light pixels in the target city. ref To compare the number of city light pixels, L ave,tg For the average brightness of light pixels in the target city, L ave,ref To compare with the average brightness of urban light pixels, R L R is the relative light ratio of the average brightness of light pixels. N This represents the relative light ratio of the number of light pixels.

[0029] The aforementioned low-light nighttime data was obtained using a visible light / infrared radiation imager.

[0030] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0031] This invention uses a different data type than before, with a time resolution ranging from monthly to daily. It can analyze the changes in two parameters—the number of light pixels and the average brightness of light pixels in the target area—over the course of the day. It also proposes a method for calculating the relative light ratio. By selecting a benchmark city at a suitable distance for comparison, the relative light ratio of the number of light pixels and the relative light ratio of the average brightness of light pixels are calculated for both the target city and the benchmark city. This reduces the error in the final result and provides a more objective, accurate, and usable battlefield situation. Attached Figure Description

[0032] Figure 1 A flowchart illustrating an embodiment of the present invention;

[0033] Figure 2 This is a schematic diagram illustrating the monthly changes in lighting parameters according to an embodiment of the present invention;

[0034] Figure 3 This is a schematic diagram illustrating the daily changes in lighting parameters according to an embodiment of the present invention. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are merely some, not all, of the embodiments of this invention. Based on the embodiments of this invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this invention. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0036] like Figure 1 As shown, a battlefield situation assessment method based on nighttime satellite light data includes the following steps:

[0037] Step 1: Acquire nighttime low-light data, which includes pixel observation data, quality status data, and cloud status data;

[0038] Step 2: Perform calculation and preprocessing on the quality status data and cloud status data. Based on the preprocessing results, filter the nighttime low-light data samples and delete nighttime low-light data with poor quality and cloud pollution in the target area. During this process, manual image review can be added to speed up the screening of obvious abnormal data.

[0039] Step 3: Based on the pixel grayscale values ​​of the pixel observation data, preset typical light thresholds, mark urban lights, and obtain a relative grayscale map of urban lights;

[0040] Step 4: Calculate the number of light pixels and the average brightness of the light pixels based on the city light markings.

[0041] Step 5: Calculate the proportion of poor-quality data and cloud-polluted data within the target area, and calculate the relative light ratio of two parameters: the number of light pixels and the average brightness of light pixels in the target city and the control city, to obtain the battlefield situation assessment results.

[0042] The monthly changes in lighting parameters in this embodiment are illustrated in the diagram below. Figure 2 As shown in the diagram, the daily changes in lighting parameters are illustrated below. Figure 3 As shown, it can be seen that in February 2022, due to the impact of the war, the monthly and daily light parameters in this area decreased significantly.

[0043] Specifically, the steps for calculating and preprocessing the quality status data and cloud status data are as follows:

[0044] Quality status data is obtained by using a subset of Mandatory_Quality_Flag data. A value of 02 indicates a poor main algorithm, and a value of 255 indicates no search fill value. The number of pixels with values ​​of 02 and 255 is counted to obtain poor quality nighttime low light data in the target area.

[0045] The cloud mask for each pixel is obtained by using a subset of the QF_Cloud_Mask data. The 6-7 bits are 00 for very clear, 01 for possibly clear, 10 for possibly cloudy, and 11 for definitely cloudy. The number of pixels with values ​​of 10 and 11 is counted to obtain the nighttime low light data of cloud pollution in the target area.

[0046] Furthermore, the marking of city lights described in step 3 includes the following steps:

[0047] With a preset typical light threshold of T, determine whether a light source is urban lighting based on the following formula:

[0048]

[0049] Where i and j are the row and column numbers of a pixel in the light data, respectively, and L i,j M represents the luminance value of the pixel located in the i-th row and j-th column. i,j This represents the light marker value of the pixel located in the i-th row and j-th column. 1 indicates that it is a city light marker, and 0 indicates that it is not a city light marker.

[0050] Specifically, the formulas for calculating the number of light pixels and the average brightness of the light pixels are as follows:

[0051]

[0052]

[0053] Among them, i max j is the maximum row number of the pixels within the target area. max N represents the maximum column number of the pixels within the target area, and N is the number of light pixels.

[0054] Specifically, the formula for calculating the relative light ratio of the number of light pixels and the average brightness of light pixels in the target city and the control city is as follows:

[0055]

[0056]

[0057] Where, N tg N represents the number of light pixels in the target city. ref To compare the number of city light pixels, L ave,tg For the average brightness of light pixels in the target city, L ave,ref To compare with the average brightness of urban light pixels, R L R is the relative light ratio of the average brightness of light pixels. N This represents the relative light ratio of the number of light pixels.

[0058] The aforementioned low-light nighttime data was obtained using a visible light and / or infrared radiation imager.

[0059] As can be seen from the invention content and embodiments, the present invention acquires nighttime low-light data using a visible light / infrared radiation imager to eliminate poor-quality samples; by calculating the number of pixels and the average brightness of pixels in the target area, the relative light ratio between the target area and the reference area is calculated; then the relative light ratio is visualized, and by analyzing the image, a more accurate change in the nighttime low-light data of the target area can be obtained, making up for the current deficiency that it is impossible to conduct battlefield situation assessment using nighttime satellite light data.

Claims

1. A battlefield situation assessment method based on night satellite light data, characterized in that, The method comprises the following steps: Step 1, obtaining night micro-light data, wherein the night micro-light data comprises pixel observation data, quality condition data and cloud condition data; Step 2, performing calculation preprocessing on the quality condition data and the cloud condition data, screening the night micro-light data samples according to the preprocessing results, and deleting the night micro-light data with poor quality and cloud pollution in the target area; Step 3, presetting a typical light threshold according to the pixel gray value of the pixel observation data, marking the city light, and obtaining the relative gray scale diagram of the city light; Step 4, calculating the number of light pixels and the average brightness of the light pixels according to the marking of the city light; Step 5, calculating the proportion of the data with poor quality and the proportion of the data with cloud pollution in the target area, calculating the relative light ratio of the number of light pixels and the average brightness of the light pixels of the target city and the control city, and obtaining the battlefield situation evaluation result; In step 3, the marking of the city light comprises the following steps: The typical light threshold is T, and whether it is city light is determined according to the following formula: where i and j are the row number and column number of a pixel in the light data, L i,j represents the light radiance value of the pixel at the i-th row and j-th column, M i,j represents the light mark value of the pixel at the i-th row and j-th column, 1 represents the mark of urban light, and 0 represents the mark of non-urban light; The calculation formula of the number of light pixels and the average brightness of the light pixels is as follows: wherein, is a maximum row number of the pixels in the target region, is a maximum column number of the pixels in the target region, is a number of the light pixels, is an average brightness of the light pixels; The calculation formula of the relative light ratio of the number of light pixels and the average brightness of the light pixels of the target city and the control city is as follows: wherein, is the number of light pixels of the target city, is the number of light pixels of the control city, is the average brightness of the light pixels of the target city, is the average brightness of the light pixels of the control city, is the relative light ratio of the average brightness of the light pixels, is the relative light ratio of the number of light pixels.

2. The battlefield situation assessment method based on night satellite light data according to claim 1, characterized in that, The calculation preprocessing of the quality condition data and the cloud condition data comprises the following steps: Through the Mandatory_Quality_Flag data subset, the quality condition data is obtained, 02 indicates poor quality main algorithm, 255 indicates no search filling value, the number of pixels with the values of 02 and 255 is counted, and the night micro-light data with poor quality in the target area is obtained; Through the QF_Cloud_Mask data subset, the cloud mask of each pixel is obtained, wherein 00 in the 6-7 byte indicates very clear, 01 indicates possibly clear, 10 indicates possibly cloudy, and 11 indicates definitely cloudy, the number of pixels with the values of 10 and 11 is counted, and the night micro-light data with cloud pollution in the target area is obtained. 3.The battlefield situation assessment method based on night satellite light data according to claim 1, characterized in that, The night micro-light data is obtained by a visible light / infrared radiation imager.

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