Method and device for extracting moon maneuver observation data, medium and product

Abstract

The invention discloses a lunar maneuver observation data extraction method and device, a medium and a product, and relates to the technical field of data processing.The method comprises the steps that a lunar maneuver observation image of any channel is read, and the lunar maneuver observation image comprises a lunar area, a cold sky background area and a mixed background area; determining a cold air background value of the cold air background area; determining the mixed background region, and assigning the gray value of the mixed background region as the cold sky background value to obtain a moon observation image; and determining the moon region from the moon observation image, thereby extracting moon maneuver observation data. According to the method, the precision of extracting the moon maneuver observation data is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a method, device, medium and product for extracting lunar mobile observation data. Background Art

[0002] The lunar target is a radiation-stable target source other than the stable targets on Earth (such as deserts, glaciers, oceans, deep convective clouds, etc.), and is suitable as a radiation reference benchmark for the visible and near-infrared channels of remote sensing instruments (or space-borne imagers).

[0003] At present, the medium-resolution spectral imager (FY-3 / MERSI) carried by Fengyun-3 has the ability to observe the moon in the cold sky field of view on orbit and maneuver the moon in the ground field of view. Among them, maneuver the moon in the ground field of view can be understood as: for an instrument used to observe the earth, by adjusting the earth observation timing of the instrument, the purpose of observing the moon is achieved. At present, most of the methods for extracting lunar observation data are basically used to extract maneuvering lunar observation data from the cold sky field of view lunar observation images on orbit (the images include a large amount of cold sky background values ​​and lunar observation data), such as orbit prediction method, statistical characteristic method and instrument observation parameter identification method. However, when performing maneuvering lunar observation operations, more information will be included, such as ground observation images, stray light from the edge of the earth, instrument baffles, cold sky background values, lunar observations and many other observation information. Therefore, the existing cold sky field lunar observation extraction methods are not suitable for maneuvering lunar observation data extraction. Summary of the invention

[0004] The purpose of this application is to provide a method, device, medium and product for extracting lunar mobile observation data to solve the problems described in the background technology.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a method for extracting lunar mobile observation data, comprising:

[0007] Reading a lunar mobile observation image of any channel, wherein the lunar mobile observation image includes a lunar area, a cold sky background area, and a mixed background area;

[0008] Acquire the cold sky background value of the cold sky background area;

[0009] Identify the mixed background area from the moon mobile observation image, and assign the gray value of the mixed background area to the cold sky background value to obtain a moon observation image;

[0010] The lunar region is determined from the lunar observation image, and the lunar region is determined from the lunar observation image, so as to extract the required mobile lunar observation data from the lunar region.

[0011] Optionally, the mixed background area is determined by the following method:

[0012] Converting the lunar mobile observation image into an RGB grayscale image;

[0013] The RGB grayscale image is fuzzy processed to obtain a fuzzy grayscale image;

[0014] The positions of pixels in the blurred grayscale image whose grayscale values ​​are greater than a predetermined value are recorded to obtain the mixed background area, and the predetermined value is greater than the cold air background value.

[0015] Optionally, the blur processing is image Gaussian blur processing.

[0016] Optionally, converting the moon mobile observation image into an RGB grayscale image includes:

[0017] Reading the calibration coefficients and digital values ​​of each pixel of the moon mobile observation image;

[0018] Sending the digital value and the calibration coefficient to a calibration algorithm, so as to convert the digital value of each pixel of the moon mobile observation image into a range of 1-100 through the calibration algorithm;

[0019] Using a data stretching algorithm, the digital value of each pixel of the moon mobile observation image is stretched to a range of 0-255 to obtain a grayscale value of each pixel;

[0020] The RGB grayscale image is generated according to the grayscale value of each pixel.

[0021] Optionally, the calibration algorithm includes a reflectivity factor calculation method, and the reflectivity factor calculation method can calculate the reflectivity factor of each pixel by the following formula:

[0022] RefFactor=k2*DN*DN+k1*DN+k0

[0023] Among them, RefFactor is the reflectivity factor; k0, k1 and k2 are calibration coefficients; DN is the digital value.

[0024] Optionally, the using a data stretching algorithm to stretch the digital value of each pixel of the moon mobile observation image to a range of 0-255 to obtain the grayscale value of each pixel includes:

[0025] Calculating the minimum reflectivity of the moon mobile observation image;

[0026] Calculating the maximum reflectivity of the moon mobile observation image;

[0027] Based on the minimum reflectivity and the maximum reflectivity, the digital value of each pixel of the moon mobile observation image is stretched to a range of 0-255 through a predetermined algorithm.

[0028] Optionally, determining the lunar region from the lunar observation image includes:

[0029] Calculating the sum of the grayscale values ​​of each column of pixels of the lunar observation image, and recording it as a first grayscale value sum;

[0030] Determine a maximum first grayscale value and a pixel column corresponding to the maximum first grayscale value and a pixel column corresponding to the maximum first grayscale value as a center position column for moon observation;

[0031] Calculating the sum of the grayscale values ​​of pixels in each row of the lunar observation image, and recording it as a second grayscale value sum;

[0032] For each row of pixels of the lunar observation image, when the sum of the second grayscale value is greater than a predetermined grayscale value, the pixel row is determined as the region where the moon is located;

[0033] In the area where the moon is located, a predetermined number of columns centered on the central position column are determined as the moon area.

[0034] In a second aspect, the present application provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of any one of the methods described in the first aspect.

[0035] In a third aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any one of the methods described in the first aspect.

[0036] In a fourth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0037] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0038] The method for extracting lunar mobile observation data provided in the embodiment of the present application assigns the mixed background in the lunar mobile observation image to the cold sky background value, that is, converting the lunar mobile observation image into the lunar region and the cold sky background region. Since the cold sky background region is darker and the lunar region is brighter, it is easy to extract the lunar region from the lunar mobile observation image. As for the prior art, the lunar mobile observation data can only be extracted from a single background environment by extracting lunar mobile observation data through orbit prediction method, statistical characteristic method and instrument observation parameter identification method. However, the lunar mobile observation image contains a lot of observation information such as ground observation images, stray light at the edge of the earth, instrument baffle, cold sky background value, etc. The existing extraction method is not suitable for extracting lunar mobile observation data from the lunar mobile observation image, and the extraction accuracy is low. Therefore, compared with the prior art, the present application improves the accuracy of extracting lunar mobile observation data from the lunar mobile observation image. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0040] Figure 1 This is an application environment diagram of a method for extracting lunar mobile observation data in one embodiment of the present application;

[0041] Figure 2 A flowchart of a method for extracting lunar mobile observation data provided in one embodiment of the present application;

[0042] Figure 3 A schematic diagram of a flow chart of a method for acquiring a mixed background area provided in one embodiment of the present application;

[0043] Figure 4 A flowchart of a method for generating an RGB grayscale image from lunar mobile observation data provided by another embodiment of the present application;

[0044] Figure 5 A flowchart of a method for stretching a lunar mobile observation image to a range of 0-255 provided in one embodiment of the present application;

[0045] Figure 6 A schematic flow chart of a method for determining a lunar region provided in another embodiment of the present application;

[0046] Figure 7 A schematic diagram of a determined lunar region provided in another embodiment of the present application;

[0047] Figure 8 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0048] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0049] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0050] The method for extracting lunar mobile observation data provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown, the application environment includes a terminal and a server. Among them, the terminal communicates with the server through a network. The data storage system can store the data that the server needs to process. The data storage system can be set up separately, integrated on the server, or placed on the cloud or other servers. The terminal can send the mobile observation image of the moon to be processed to the server. After the server receives the mobile observation image of the moon to be processed, it can first store the mobile observation image of the moon, and then retrieve it from the storage location when it needs to be processed, or it can perform processing tasks while storing to determine the lunar area from the mobile observation image of the moon, that is, to realize the mobile observation data extraction of the moon. The server can feed back the mobile observation image of the moon with the lunar area determined to the terminal. In addition, in some embodiments, the mobile observation data extraction method of the moon can also be implemented by the server or the terminal alone, such as the terminal can directly execute the mobile observation image of the moon to extract the mobile observation data of the moon, or the server can obtain the mobile observation image of the moon to be processed from the data storage system, and extract the mobile observation data of the moon for the mobile observation image of the moon to be processed.

[0051] The terminals may be, but are not limited to, various desktop computers, laptops, smart phones, tablet computers, IoT devices and portable wearable devices. IoT devices may be smart speakers, smart TVs, smart air conditioners, smart car-mounted devices, etc. Portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server may be implemented as an independent server or a server cluster consisting of multiple servers, or a cloud server.

[0052] In an exemplary embodiment, Figure 2As shown, a method for extracting lunar mobile observation data is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server in the example is used to illustrate, including the following steps 201 to 204:

[0053] Step 201, reading a mobile lunar observation image, wherein the mobile lunar observation image includes a lunar area, a cold sky background area, and a mixed background area.

[0054] Among them, the mobile lunar observation images are taken by satellite-borne imagers (also called remote sensing instruments) located in space. Therefore, the mobile lunar observation images include not only the lunar area, but also the cold sky background area and other mixed background areas.

[0055] Furthermore, the mixed background area is the area in the lunar mobile observation image except the lunar area and the cold sky background area. For example, the mixed background area may include various information such as the earth, stray light at the edge of the earth, and instrument baffles.

[0056] Among them, the description that "the mobile lunar observation image includes the lunar region, the cold sky background region, and the mixed background region" only describes a feature of the mobile lunar observation image, and does not mean that the server has identified these three regions, and certainly does not mean that the server has marked these three regions. Further, in step 201, when the server reads the mobile lunar observation image, the server does not identify the lunar region, the cold sky background region, and the mixed region in the mobile lunar observation image, and of course does not mark the three regions. The server reads an original mobile lunar observation image.

[0057] In order to extract lunar maneuver observation data, the lunar region must be determined from the lunar observation images.

[0058] Among them, the mobile observation images of the moon taken by the satellite-borne imager can include images corresponding to multiple bands, and each band includes multiple wavelengths. For example, for the visible near-infrared band, this band contains 19 different wavelengths, that is, it contains 19 channels.

[0059] In addition, it should be noted that steps 201 to 204 are for the lunar maneuver observation images of any channel of the satellite-borne imager. The lunar maneuver observation images are not grayscale images in the RGB space, that is, the grayscale values ​​of each pixel of the image are not 0-255, but an observation digital (English full name: Digital Number; English abbreviation: DN) image. Therefore, the grayscale images mentioned in steps 201 to 204 are actually observation DN images, and the grayscale values ​​are also the grayscale values ​​of the observation DN images. In the process of converting to RGB, the observation DN image needs to be converted into physical quantities (such as reflectivity, radiance, brightness temperature, etc.) through calibration processing and then linearly stretched. This application takes reflectivity as an example for description.

[0060] Step 202: Acquire the cold sky background value of the cold sky background area.

[0061] For the three areas of the moon area, the cold sky background area and the mixed background area in the moon observation image, the grayscale value of the moon area is larger, so the moon area is a relatively bright area, but because the moon is very small in space and the satellite-borne imager is far away from the moon, the area of ​​the moon area is relatively small; the grayscale value of the cold sky background area is smaller, and it is a relatively dark area, and the mixed background area is also a relatively bright area.

[0062] When the moon is observed in a maneuverable manner, since the moon is already in the maneuverable observation field of view, the cold sky field of view around the moon can be regarded as the observation background during the maneuverable observation of the moon, and the observation background is large and uniform, so the cold sky field of view can be regarded as a uniform observation background. Further, when calculating the cold sky background value of each channel's maneuverable observation image of the moon, the average value of the cold sky field observation value (full name in English: Space View Dark Noise; abbreviated in English: SVDN) of the corresponding channel can be calculated, and the average value is recorded as the cold sky background value of the channel's maneuverable observation image of the moon. Further, the cold sky background observation value is the DN value of the cold sky background.

[0063] Furthermore, when calculating the cold sky background value, the cold sky background area can be first determined from the moon mobile observation image, and then the average value of the DN value in the area is calculated to obtain the cold sky background value.

[0064] Furthermore, since the cold sky background area is darker, the cold sky background area can be determined by a grayscale threshold, and an area with a grayscale value less than the grayscale threshold is determined as the cold sky background area.

[0065] Of course, the cold sky background value may also be determined by other methods without identifying the cold sky background area, for example, it may be an empirical value or a historical average value of the value.

[0066] Step 203, identifying the mixed background area from the mobile lunar observation image, and assigning the gray value of the mixed background area to the cold sky background value, so as to obtain a lunar observation image.

[0067] After the grayscale value of the mixed background area is reassigned to the cold sky background value, the mixed background area also becomes a darker area. At this time, the lunar area is the only brighter area in the mobile lunar observation image, so the lunar area can be determined from the mobile lunar observation image.

[0068] Step 204, determining the lunar region from the lunar observation image, thereby extracting the lunar mobile observation data.

[0069] The determination of the lunar region may be performed by recording the positions of pixels of the lunar region.

[0070] The lunar maneuver observation data includes the lunar region determined from the lunar observation image. Furthermore, the lunar region is also used for subsequent data analysis.

[0071] After the lunar region is determined, various data of the lunar region, such as the DN value, can be extracted according to the regional location.

[0072] The method for extracting lunar mobile observation data provided in the embodiment of the present application converts the lunar mobile observation image into a lunar region and a cold sky background region by assigning the mixed background in the lunar mobile observation image to a cold sky background value. Since the cold sky background region is darker and the lunar region is brighter, it is easy to extract the lunar region from the lunar mobile observation image. As for the prior art, lunar observation data can only be extracted from a single background environment by extracting lunar observation data through orbit prediction method, statistical characteristic method and instrument observation parameter identification method. However, the lunar mobile observation image contains a lot of observation information such as ground observation images, stray light at the edge of the earth, instrument baffles, cold sky background values, etc. The existing extraction method is not suitable for extracting lunar mobile observation data from the lunar mobile observation image, and the extraction accuracy is low. Therefore, compared with the prior art, the present application improves the accuracy of extracting lunar mobile observation data from the lunar mobile observation image.

[0073] Alternatively, see Figure 3 In another exemplary embodiment of the present application, the mixed background area in step 203 is determined by the following steps 2031 to 2033:

[0074] Step 2031, converting the moon mobile observation image into an RGB grayscale image.

[0075] Since the moon mobile observation image in step 201 is not an RGB grayscale image, the moon mobile observation image is first converted into an RGB grayscale image.

[0076] Step 2032: blur the RGB grayscale image to obtain a blurred grayscale image.

[0077] Optionally, the blur processing is Gaussian blur processing of the image.

[0078] Image Gaussian blur processing, using GaussianBlur function to smooth the RGB grayscale image and reduce image details. Since the moon is a small target and the moon is surrounded by a cold sky background, the moon will be blurred by the cold sky background, thus becoming a darker area. Since the satellite is far away from the moon, the resolution of each channel is inconsistent. When setting the size of the function kernel of the Gaussian blur function, you can refer to the imaging size of the moon on the channel. For example, for a channel with a resolution of 1KM, the function kernel size of the Gaussian blur function is set to (51, 51).

[0079] Step 2033, recording the positions of pixels in the blurred grayscale image whose grayscale values ​​are greater than a predetermined value to obtain the mixed background area, wherein the predetermined value is greater than the cold air background value.

[0080] Since the moon area is blurred by the surrounding cold sky background in the blurred grayscale image, the moon brightness value is greatly reduced, but the mixed area still exists and its brightness is greater than that of the surrounding cold sky background area. Therefore, a bright target rejection threshold Th, that is, the above-mentioned predetermined value, can be set to identify pixels greater than the predetermined value and save the locations of these pixels, thereby obtaining the locations of the pixels in the mixed background area.

[0081] In addition, it should be noted that, in the process of executing the above steps 2031 to 2033, a mobile lunar observation image can be copied, and the copied image can be used to execute steps 2031 to 2033 to avoid damaging the original mobile lunar observation image.

[0082] Alternatively, see Figure 4 In another exemplary embodiment of the present application, step 2031 includes the following steps 301 to 304:

[0083] Step 301, reading the calibration coefficients and digital values ​​of each pixel of the moon mobile observation image.

[0084] The calibration coefficients and the digital values ​​of each pixel are the relevant data of the lunar mobile observation image and are known data.

[0085] Step 302: Send the digital value and calibration coefficient to a calibration algorithm, so as to convert the digital value of each pixel of the moon mobile observation image into a range of 1-100 through the calibration algorithm.

[0086] Optionally, the calibration algorithm includes a reflectivity factor calculation method, and the reflectivity factor calculation method can calculate the reflectivity factor of each pixel by the following formula (1):

[0087] RefFactor=k2*DN*DN+k1*DN+k0 (1)

[0088] Among them, RefFactor is the reflectivity factor; k0, k1 and k2 are calibration coefficients; DN is the digital value.

[0089] Among them, the calibration coefficients k0, k1 and k2 are fixed systems. The DN value can also be obtained from the mobile observation image of the moon. The DN value can be converted into the range of 1-100 by the above formula (1).

[0090] Step 303: Use a data stretching algorithm to stretch the digital value of each pixel of the moon mobile observation image to a range of 0-255 to obtain a grayscale value of each pixel.

[0091] Step 304: Generate the RGB grayscale image according to the grayscale value of each pixel.

[0092] Alternatively, see Figure 5 , step 303 includes the following steps 3031-3033:

[0093] Step 3031, calculating the minimum reflectivity of the moon mobile observation image.

[0094] Step 3032, calculating the maximum reflectivity of the moon mobile observation image.

[0095] Step 3033: Based on the minimum reflectivity and the maximum reflectivity, the digital value of each pixel of the moon mobile observation image is stretched to a range of 0-255 through a predetermined algorithm.

[0096] Optionally, the predetermined algorithm is the following formula (2):

[0097] ImageData=(RefFactor-min)*255 / (max-min) (2)

[0098] Among them, ImageData represents the gray value of the stretched lunar mobile observation image, RefFactor is the reflectivity factor, min is the minimum reflectivity, and max is the maximum reflectivity.

[0099] Alternatively, see Figure 6 , and combined with Figure 7 In another exemplary embodiment of the present application, step 204 includes the following steps 2041 to 2045:

[0100] Step 2041, calculate the sum of the grayscale values ​​of each column of pixels in the lunar observation image, and record it as the first grayscale value sum.

[0101] like Figure 7 As shown, Figure 7 For a moon observation image, there are 10 rows and 12 columns, so there are a total of 12 first grayscale values.

[0102] Step 2042, determine the maximum first grayscale value and, determine the pixel column corresponding to the maximum first grayscale value and as the center position column of the moon observation.

[0103] Still taking the above example, the 7th column is the column with the first grayscale value and the largest value, so the 7th column is the center position column of the moon observation.

[0104] Step 2043, calculating the sum of the grayscale values ​​of pixels in each row of the lunar observation image, and recording it as the second grayscale value sum.

[0105] like Figure 7 As shown, there are a total of 10 second grayscale values ​​and.

[0106] Step 2044: for each row of pixels of the lunar observation image, when the sum of the second grayscale value is greater than a predetermined grayscale value, the pixel row is determined as the region where the moon is located.

[0107] The predetermined grayscale value is generally between the grayscale value of the cold sky background area and the grayscale value of the moon area, and may be an empirical value.

[0108] Since the gray value of the cold sky background is significantly smaller than the gray value of the moon region, the starting and ending rows of the moon region are the rows with the second gray value and the mutation.

[0109] like Figure 7 As shown, the area where the moon is located is from the 4th to the 8th row.

[0110] Step 2045, in the area where the moon is located, a predetermined number of columns centered on the center position column are determined as the moon area.

[0111] The predetermined number of columns may be an empirical value, which is determined according to the pixel size of the mobile lunar observation image. When the pixel is larger, the predetermined number of columns is larger, and when the pixel is smaller, the predetermined number of columns is smaller.

[0112] like Figure 7 As shown, the predetermined number is 2, that is, in the area where the moon is located, the 7th column is the central column, and the two columns on the left and right including the 7th column are determined as the moon area.

[0113] As can be seen from step 201, steps 201 to 204 are operations performed for any channel. Moreover, any band includes multiple wavelengths, and one wavelength corresponds to one channel. Therefore, for any band, the moon area of ​​each channel can be determined through steps 201 to 204, and then the digital values ​​of the pixels of each moon area are averaged to obtain the final moon area, so as to improve the accuracy of the moon area.

[0114] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 8 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. Among them, the processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data related to the extraction of mobile observation data of the moon. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for extracting mobile observation data of the moon is implemented.

[0115] Those skilled in the art will understand that Figure 8 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0116] In an exemplary embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0117] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0118] In an exemplary embodiment, a computer program product is provided, including a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0119] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0120] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0121] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0122] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0123] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application. At the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A method for extracting lunar mobile observation data, characterized in that: include: Reading a mobile lunar observation image, wherein the mobile lunar observation image includes a lunar region, a cold sky background region, and a mixed background region, wherein the mixed background region is a region of the mobile lunar observation image excluding the lunar region and the cold sky background region; Acquire the cold sky background value of the cold sky background area; Identify the mixed background area from the moon mobile observation image, and assign the gray value of the mixed background area to the cold sky background value to obtain a moon observation image; The lunar region is determined from the lunar observation image, thereby extracting the required lunar mobile observation data from the lunar region.

2. The method for extracting lunar mobile observation data according to claim 1, characterized in that: The mixed background area is determined by the following method: Converting the lunar mobile observation image into an RGB grayscale image; The RGB grayscale image is fuzzy processed to obtain a fuzzy grayscale image; The positions of pixels in the blurred grayscale image whose grayscale values ​​are greater than a predetermined value are recorded to obtain the mixed background area, and the predetermined value is greater than the cold air background value.

3. The method for extracting lunar mobile observation data according to claim 2, characterized in that: The blur processing is Gaussian blur processing of the image.

4. The method for extracting lunar mobile observation data according to claim 2, characterized in that: The step of converting the moon mobile observation image into an RGB grayscale image comprises: Reading the calibration coefficient of the moon mobile observation image and the digital value DN value of each pixel; Sending the digital value and the calibration coefficient to a calibration algorithm, so as to convert the digital value of each pixel of the moon mobile observation image into a range of 1-100 through the calibration algorithm; Using a data stretching algorithm, the digital value of each pixel of the moon mobile observation image is stretched to a range of 0-255 to obtain a grayscale value of each pixel; The RGB grayscale image is generated according to the grayscale value of each pixel.

5. The method for extracting lunar mobile observation data according to claim 4, characterized in that: The calibration algorithm includes a reflectivity factor calculation method, and the reflectivity factor calculation method is as follows: RefFactor=k2*DN*DN+k1*DN+k0 Among them, RefFactor is the reflectivity factor; k0, k1 and k2 are calibration coefficients; DN is the digital value.

6. The method for extracting lunar mobile observation data according to claim 4, characterized in that: The method of using a data stretching algorithm to stretch the digital value of each pixel of the moon mobile observation image to a range of 0-255 to obtain a grayscale value of each pixel includes: Calculating the minimum reflectivity of the moon mobile observation image; Calculating the maximum reflectivity of the moon mobile observation image; Based on the minimum reflectivity and the maximum reflectivity, the digital value of each pixel of the moon mobile observation image is stretched to a range of 0-255 through a predetermined algorithm.

7. The method for extracting lunar mobile observation data according to any one of claims 1 to 6, characterized in that: Determining the lunar region from the lunar observation image comprises: Calculating the sum of the grayscale values ​​of each column of pixels of the lunar observation image, and recording it as a first grayscale value sum; Determine a maximum first grayscale value and a pixel column corresponding to the maximum first grayscale value and a pixel column corresponding to the maximum first grayscale value as a center position column for moon observation; Calculating the sum of the grayscale values ​​of pixels in each row of the lunar observation image, and recording it as a second grayscale value sum; For each row of pixels of the lunar observation image, when the sum of the second grayscale value is greater than a predetermined grayscale value, the pixel row is determined as the region where the moon is located; In the area where the moon is located, a predetermined number of columns centered on the central position column are determined as the moon area.

8. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method for extracting lunar mobile observation data as described in any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method for extracting lunar mobile observation data described in any one of claims 1 to 7 is implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the method for extracting lunar mobile observation data described in any one of claims 1 to 7 is implemented.

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