A remote sensing image data value evaluation method and device

Abstract

The application discloses a kind of remote sensing image data value evaluation method and device, the method includes obtaining remote sensing image data information set and remote sensing image data type information;The remote sensing image data information set includes several remote sensing image data information;The remote sensing image data information set and the remote sensing image data type information are processed, and remote sensing image data content value information, remote sensing image data quality value information and remote sensing image user attention value information are obtained;Remote sensing image data content value information, remote sensing image data quality value information and remote sensing image user attention value information are fused and handled, and remote sensing image data value evaluation information is obtained.The application can quantitatively evaluate remote sensing image data value, while considering the static attribute and dynamic attribute of data comprehensively, dynamically update with time, can objectively reflect the change of data value, is favorable to the accurate value evaluation of remote sensing image data.

Description

Technical Field

[0001] This invention relates to the field of remote sensing image data processing technology, and in particular to a method and apparatus for assessing the value of remote sensing image data. Background Technology

[0002] With the rapid development of remote sensing technology, remote sensing image data from different types of satellites is experiencing explosive growth. To effectively manage this massive amount of remote sensing image data, fully explore and leverage its value, and provide a basis and support for data storage, intelligent recommendation, and other advanced applications, it is necessary to establish a quantitative remote sensing image data value assessment model. Existing remote sensing data value assessment models are set based on the remote sensing image data lifecycle, lacking sufficient detail in the assessment objects. They fail to distinguish between cataloged data and product data, and the parameters included are relatively general, failing to deeply and meticulously consider the content and quality attributes of the data. Therefore, the assessments lack specificity and accuracy. Summary of the Invention

[0003] The technical problem to be solved by this invention is to provide a method and apparatus for evaluating the value of remote sensing image data. By comprehensively considering three dimensions—data content, data quality, and user attention—it can obtain remote sensing image data value evaluation information, which is conducive to accurate value evaluation of remote sensing image data. This provides a reference for applications such as data storage and migration and intelligent recommendation of remote sensing image data.

[0004] To address the aforementioned technical problems, a first aspect of the present invention discloses a method for assessing the value of remote sensing image data, the method comprising:

[0005] S1, acquire remote sensing image data information set and remote sensing image data type information; the remote sensing image data information set includes several remote sensing image data information;

[0006] S2, process the remote sensing image data information set and the remote sensing image data type information to obtain remote sensing image data content value information, remote sensing image data quality value information and remote sensing image user attention value information;

[0007] S3, the content value information, quality value information, and user attention value information of the remote sensing image data are fused to obtain remote sensing image data value assessment information.

[0008] As an optional implementation, in the first aspect of the present invention, the processing of the remote sensing image data information set and the remote sensing image data type information to obtain remote sensing image data content value information, remote sensing image data quality value information, and remote sensing image user attention value information includes:

[0009] S21, preprocess the remote sensing image data information set to obtain a preprocessed remote sensing image data information set; the preprocessed remote sensing image data information set includes several preprocessed remote sensing image data information sets.

[0010] S22, perform data extraction operation on the preprocessed remote sensing image data information set to obtain a remote sensing image data information set to be processed; the remote sensing image data information set to be processed includes several remote sensing image data information sets to be processed.

[0011] S23, process the remote sensing image data information set to be processed and the remote sensing image data type information to obtain remote sensing image data content value information, remote sensing image data quality value information and remote sensing image user attention value information.

[0012] As an optional implementation, in a first aspect of the present invention, the preprocessing of the remote sensing image data information set to obtain a preprocessed remote sensing image data information set includes:

[0013] S211, perform data integrity processing on any of the remote sensing image data information in the remote sensing image data information set to obtain the first remote sensing image data information corresponding to the remote sensing image data information;

[0014] S212, perform metadata checking and processing on the first remote sensing image data information corresponding to the remote sensing image data information to obtain the preprocessed remote sensing image data information corresponding to the remote sensing image data information.

[0015] As an optional implementation, in the first aspect of the present invention, the processing of the remote sensing image data information set to be processed and the remote sensing image data type information to obtain remote sensing image data content value information, remote sensing image data quality value information, and remote sensing image user attention value information includes:

[0016] S231, Perform a first calculation process on the remote sensing image data information set to be processed to obtain remote sensing image data content value information;

[0017] S232, perform a second calculation process on the remote sensing image data information set to be processed and the remote sensing image data type information to obtain remote sensing image data quality value information;

[0018] S233, perform a third calculation on the remote sensing image data information set to be processed to obtain remote sensing image user attention value information.

[0019] As an optional implementation, in a first aspect of the present invention, the first calculation processing of the remote sensing image data information set to be processed to obtain remote sensing image data content value information includes:

[0020] S2311, Using the remote sensing image data freshness calculation model, the remote sensing image data information set to be processed is calculated and processed to obtain remote sensing image data freshness information.

[0021] The remote sensing image data freshness calculation model is as follows:

[0022]

[0023] 1≤i≤N;

[0024] In the formula, XXD represents the freshness information of the remote sensing image data. i Let V1, V2, and V3 be the first, second, and third freshness values, respectively, and T1 and T2 be the first and second time thresholds, respectively. i The generation time value of the i-th remote sensing image data information in the set of remote sensing image data information to be processed is denoted as N, and N is the number of remote sensing image data information to be processed in the set of remote sensing image data information to be processed.

[0025] S2312, Using the remote sensing image data content value calculation model, the remote sensing image data information set to be processed and the remote sensing image data freshness information are calculated and processed to obtain the remote sensing image data content value information.

[0026] The calculation model for the content value of the remote sensing image data is as follows:

[0027]

[0028] GZM = max{GZ i |i=1,2,…,N};

[0029] FBMA = max{FB i |i=1,2,…,N};

[0030] FBMI = min{FB i |i=1,2,…,N};

[0031] 1≤i≤N;

[0032] δ1+δ2+δ3+δ4=1;

[0033] 0≤δ1,δ2,δ3,δ4≤1;

[0034] In the formula, NR represents the content value information of the remote sensing image data. i MS is the value of the i-th remote sensing image data content in the remote sensing image data content value information. i GZ i and FB i These represent the imaging mode value, number of points of interest, and image resolution of the i-th remote sensing image data in the set of remote sensing image data to be processed, respectively, XXD. i Let δ1, δ2, δ3, and δ4 be the first weighting parameter, the second weighting parameter, the third weighting parameter, and the fourth weighting parameter, respectively, in the freshness information of the remote sensing image data.

[0035] As an optional implementation, in a first aspect of the present invention, the second calculation processing of the remote sensing image data information set to be processed and the remote sensing image data type information to obtain remote sensing image data quality value information includes:

[0036] S2321, determine whether the remote sensing image data type information is cataloging data information, and obtain the first determination result;

[0037] When the first judgment result is yes, execute S2322;

[0038] If the first judgment result is negative, execute S2323;

[0039] S2322, Using the first remote sensing image data quality value model, the remote sensing image data information set to be processed is calculated and processed to obtain remote sensing image data quality value information;

[0040] The first remote sensing image data quality value model is as follows:

[0041] ZL j =θ1·(1-YL) j )+θ2·(1-HK j )1≤j≤M;

[0042] θ1 + θ2 = 1;

[0043] 0≤θ1,θ2≤1;

[0044] In the formula, ZL represents the quality value information of the remote sensing image data. j YL represents the j-th remote sensing image data quality value in the aforementioned remote sensing image data quality value information. j and HK jθ1 and θ2 are the cloud cover detection result value and black block detection result value in the j-th remote sensing image data information in the remote sensing image data information set to be processed, respectively; M is the number of remote sensing image data information in the remote sensing image data information set to be processed.

[0045] S2323, using the second remote sensing image data quality value model, the remote sensing image data information set to be processed is calculated and processed to obtain remote sensing image data quality value information;

[0046] The second remote sensing image data quality value model is as follows:

[0047] ZL j =θ3·(1-YL) j )+θ4·(1-HK j )+θ5·JD j +θ6·XD j +θ7·JF j +θ8

[0048] ·XF j ;

[0049] 1≤j≤M;

[0050] θ3+θ4+θ5+θ6+θ7+θ8=1;

[0051] 0≤θ3,θ4,θ5,θ6,θ7,θ8≤1;

[0052] In the formula, ZL represents the quality value information of the remote sensing image data. j YL is the j-th value of the remote sensing image data quality value information in the remote sensing image data quality value information. j HK j JD j XD j JF j and XF j θ1, θ2, θ3, θ4, θ5, θ6, θ7, and θ8 are the cloud cover detection result value, black block detection result value, absolute positioning accuracy value, relative positioning accuracy value, absolute radiometric accuracy value, and relative radiometric accuracy value in the j-th remote sensing image data information in the remote sensing image data information set to be processed, respectively. θ3, θ4, θ5, θ6, θ7, and θ8 are the third, fourth, fifth, sixth, seventh, and eighth weighting coefficients, respectively. M is the number of remote sensing image data information to be processed in the remote sensing image data information set to be processed.

[0053] As an optional implementation, in the first aspect of the present invention, the fusion processing of the remote sensing image data content value information, the remote sensing image data quality value information, and the remote sensing image user attention value information to obtain remote sensing image data value assessment information includes:

[0054] S31, normalize the content value information, quality value information, and user attention value information of the remote sensing image data respectively to obtain normalized content value information, normalized quality value information, and normalized user attention value information.

[0055] S32, using the remote sensing image data value calculation model, calculate and process the normalized data content value information, the normalized data quality value information, and the normalized user attention value information to obtain remote sensing image data value assessment information;

[0056] The remote sensing image data value calculation model is as follows:

[0057] SJJZ z =GZL z ·(δ5·GNR z +δ6·GGZD z 1≤z≤L;

[0058] In the formula, SJJZ represents the value assessment information of the remote sensing image data. z GZL represents the z-th remote sensing image data value assessment value in the remote sensing image data value assessment information. z GNR z and GGZD z These are the z-th normalized data quality value information value in the normalized data quality value information, the z-th normalized data content value information value in the normalized data content value information, and the z-th normalized user attention value information value in the normalized user attention value information, respectively. δ5 and δ6 are the fifth and sixth weight parameters, respectively, and L is the number of normalized data quality value information values ​​in the normalized data quality value information.

[0059] A second aspect of this invention discloses a remote sensing image data value assessment device, the device comprising:

[0060] The acquisition module is used to acquire remote sensing image data information set and remote sensing image data type information; the remote sensing image data information set includes several remote sensing image data information sets.

[0061] The first calculation module is used to process the remote sensing image data information set and the remote sensing image data type information to obtain remote sensing image data content value information, remote sensing image data quality value information and remote sensing image user attention value information.

[0062] The second calculation module is used to fuse the content value information, quality value information, and user attention value information of the remote sensing image data to obtain remote sensing image data value assessment information.

[0063] A third aspect of this invention discloses another remote sensing image data value assessment device, the device comprising:

[0064] processor;

[0065] A memory coupled to the processor stores executable program code;

[0066] The processor calls the executable program code stored in the memory to execute some or all of the steps of the remote sensing image data value assessment method disclosed in the first aspect of the present invention.

[0067] The fourth aspect of this invention discloses a computer-readable storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps of the remote sensing image data value assessment method disclosed in the first aspect of this invention.

[0068] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0069] In this embodiment of the invention, a remote sensing image data information set and remote sensing image data type information are acquired. The remote sensing image data information set includes several remote sensing image data information sets. The remote sensing image data information set and the remote sensing image data type information are processed to obtain remote sensing image data content value information, remote sensing image data quality value information, and remote sensing image user attention value information. The remote sensing image data content value information, remote sensing image data quality value information, and remote sensing image user attention value information are then fused to obtain remote sensing image data value assessment information. It is evident that this embodiment comprehensively considers data content, data quality, and user attention from three dimensions, enabling the acquisition of remote sensing image data value assessment information. Simultaneously, it comprehensively considers both static and dynamic attributes of the data, dynamically updating over time to objectively reflect changes in data value. This facilitates accurate value assessment of remote sensing image data, thereby providing a reference for applications such as remote sensing image data storage migration and intelligent recommendation. Attached Figure Description

[0070] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0071] Figure 1 This is a flowchart illustrating a method for assessing the value of remote sensing image data disclosed in an embodiment of the present invention.

[0072] Figure 2 This is a schematic diagram of the structure of a remote sensing image data value assessment device disclosed in an embodiment of the present invention;

[0073] Figure 3 This is a schematic diagram of another remote sensing image data value assessment device disclosed in an embodiment of the present invention. Detailed Implementation

[0074] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0075] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0076] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0077] This invention discloses a method and apparatus for evaluating the value of remote sensing image data. It comprehensively considers three dimensions: data content, data quality, and user attention, enabling the acquisition of remote sensing image data value assessment information. Simultaneously, it considers both static and dynamic attributes of the data, updating dynamically over time to objectively reflect changes in data value. This facilitates accurate value assessment of remote sensing image data, providing a reference for applications such as data storage and migration, and intelligent recommendation. Detailed descriptions follow.

[0078] Example 1

[0079] Please see Figure 1 , Figure 1 This is a flowchart illustrating a method for assessing the value of remote sensing image data disclosed in an embodiment of the present invention. Figure 1 The described remote sensing image data value assessment method is applied in a remote sensing image data value assessment device, such as a local server or cloud server used for optimized management of remote sensing image data value assessment, etc., and the embodiments of the present invention are not limited thereto. Figure 1 As shown, the method for assessing the value of remote sensing image data may include the following operations:

[0080] S1, acquire the remote sensing image data information set and remote sensing image data type information; the remote sensing image data information set includes several remote sensing image data information;

[0081] It should be noted that the remote sensing image data information set consists of remote sensing image data corresponding to several remote sensing images, and one of the remote sensing image data information is the remote sensing image data corresponding to one remote sensing image. Through the embodiments of the present invention, the value assessment of the remote sensing image corresponding to the remote sensing image data information can be performed through the remote sensing image data information.

[0082] In this embodiment of the invention, the remote sensing image data type information is divided into two types: cataloging data information and product data information. Therefore, the invention can separately evaluate the value of the remote sensing images corresponding to the cataloging data information and the product data information. When the remote sensing image data type information is cataloging data information, all remote sensing image data information in the remote sensing image data information set is cataloging data of the remote sensing images; when the remote sensing image data type information is product data information, all remote sensing image data information in the remote sensing image data information set is product data of the remote sensing images.

[0083] The cataloging data consists of descriptive information about remote sensing images, including formatted data, image thumbnail information, and metadata information. It is mainly used for data entry and archiving. When necessary, cataloging data can be extracted for product production. In this embodiment, the value assessment is mainly based on information extracted from thumbnail information and metadata information. The product data consists of image information after radiometric and geometric correction, used to provide users with relevant applications. It includes raster file information, thumbnail information, and metadata information. In this embodiment, the value assessment is mainly based on information extracted from the above-mentioned raster file information, thumbnail information, and metadata information.

[0084] S2, processes the remote sensing image data information set and the remote sensing image data type information to obtain remote sensing image data content value information, remote sensing image data quality value information, and remote sensing image user attention value information;

[0085] It should be noted that the content value information of remote sensing image data is an important indicator for evaluating the value of remote sensing images. It is used to quantify the richness and importance of the information contained in the image in practical applications. Its main purpose is to measure the application potential and practical value of the data based on the content characteristics of the remote sensing image. The quality value information of remote sensing image data is an important indicator for evaluating the reliability, accuracy, and consistency of remote sensing image data at the technical level. It reflects whether the remote sensing image meets the quality standards required for a specific application scenario and plays a decisive role in the value assessment of the remote sensing image. The user attention value information of remote sensing image data is the degree to which the remote sensing image meets user needs. It comprehensively evaluates the value of the remote sensing image through user behavior data (such as the number of queries and orders). By quantifying user behavior and needs, it clarifies the importance of the remote sensing image in practical applications.

[0086] S3 integrates the content value information, quality value information, and user attention value information of remote sensing image data to obtain remote sensing image data value assessment information.

[0087] It is evident that implementing the remote sensing image data value assessment method described in the embodiments of the present invention is beneficial for accurately assessing the value of remote sensing image data, thereby providing a reference for applications such as data storage and migration and intelligent recommendation of remote sensing image data.

[0088] In an optional embodiment, the remote sensing image data information set and remote sensing image data type information are processed to obtain remote sensing image data content value information, remote sensing image data quality value information, and remote sensing image user attention value information, including:

[0089] S21, preprocess the remote sensing image data information set to obtain a preprocessed remote sensing image data information set; the preprocessed remote sensing image data information set includes several preprocessed remote sensing image data information sets.

[0090] S22, perform data extraction operation on the preprocessed remote sensing image data information set to obtain the remote sensing image data information set to be processed; the remote sensing image data information set to be processed includes several remote sensing image data information to be processed.

[0091] It should be noted that the above extraction operation can be performed using tools such as pandas, ArcGIS, and ArcPy, or it can be performed by a user-defined tool. In particular, the embodiments of the present invention do not limit the specific extraction.

[0092] It should be noted that when the remote sensing image data is cataloged data, the information extracted from the remote sensing image data includes: the generation time value corresponding to the remote sensing image, indicating the time when the remote sensing image was first generated; the imaging mode value, used to indicate the imaging mode of the remote sensing image. When the remote sensing image is in stereo imaging mode, the imaging mode value is 1, and when the remote sensing image is in non-stereo imaging mode, the imaging mode value is 0; the number of points of interest, used to indicate the number of specific target objects in the remote sensing image that have research or analytical value; the image resolution, used to indicate the specific image resolution of the remote sensing image; the cloud cover detection result value, the quantitative result of the cloud coverage area in the remote sensing image, reflecting the proportion of the image area that is obscured by clouds or contains clouds to the total image area, with a value range of (0,1); the black patch detection result value, usually used to indicate the ratio of the black patch area to the entire image area, with a value range of (0,1); the number of times the user placed an order for production, used to indicate the number of times the user placed an order for production of the remote sensing image; and the number of times the user queried, used to indicate the number of times the remote sensing image was queried.

[0093] When remote sensing image data is product data, the information extracted from the remote sensing image data includes not only the data information that needs to be extracted from the cataloging data mentioned above, but also absolute positioning accuracy value, relative positioning accuracy value, absolute radiometric accuracy value, and relative radiometric accuracy value, which are used to measure the accuracy of positioning and radiometric information in the remote sensing image.

[0094] S23, process the remote sensing image data information set and remote sensing image data type information to obtain remote sensing image data content value information, remote sensing image data quality value information, and remote sensing image user attention value information.

[0095] It is evident that implementing the remote sensing image data value assessment method described in the embodiments of the present invention is beneficial for accurately assessing the value of remote sensing image data, thereby providing a reference for applications such as data storage and migration and intelligent recommendation of remote sensing image data.

[0096] In another optional embodiment, the remote sensing image data set is preprocessed to obtain a preprocessed remote sensing image data set, including:

[0097] S211, Perform data integrity processing on any remote sensing image data information in the remote sensing image data information set to obtain the first remote sensing image data information corresponding to the remote sensing image data information;

[0098] It should be noted that the above data integrity processing involves checking whether the remote sensing image data information records are complete and whether the fields are complete. If they are incomplete, relevant filling is performed to ensure the integrity of the remote sensing image data information. Specific data integrity processing can be performed using tools such as ENVI and GDAL. However, the specific implementation of this invention does not limit the scope of the process.

[0099] S212, perform metadata inspection and processing on the first remote sensing image data information corresponding to the remote sensing image data information to obtain the preprocessed remote sensing image data information corresponding to the remote sensing image data information.

[0100] It should be noted that the above metadata inspection and processing can be performed using tools such as ENVI and Pandas. The main purpose is to check whether there are outliers in the metadata information of the first remote sensing image data. If so, the data is corrected by means such as mean substitution, nearest neighbor interpolation, or time series-based interpolation correction to eliminate abnormal data in the first remote sensing image data information, so as to obtain more accurate remote sensing image data value assessment information.

[0101] It is evident that implementing the remote sensing image data value assessment method described in the embodiments of the present invention is beneficial for accurately assessing the value of remote sensing image data, thereby providing a reference for applications such as data storage and migration and intelligent recommendation of remote sensing image data.

[0102] In another optional embodiment, the remote sensing image data information set and remote sensing image data type information to be processed are processed to obtain remote sensing image data content value information, remote sensing image data quality value information, and remote sensing image user attention value information, including:

[0103] S231, Perform the first calculation and processing on the remote sensing image data information set to be processed to obtain the value information of the remote sensing image data content;

[0104] S232, perform a second calculation process on the remote sensing image data information set and remote sensing image data type information to be processed, and obtain remote sensing image data quality value information;

[0105] S233, performs a third calculation on the remote sensing image data set to be processed, and obtains the remote sensing image user attention value information.

[0106] It is evident that implementing the remote sensing image data value assessment method described in the embodiments of the present invention is beneficial for accurately assessing the value of remote sensing image data, thereby providing a reference for applications such as data storage and migration and intelligent recommendation of remote sensing image data.

[0107] In an optional embodiment, a first computational process is performed on the remote sensing image data information set to be processed to obtain remote sensing image data content value information, including:

[0108] S2311, using the remote sensing image data freshness calculation model, calculates and processes the remote sensing image data information set to be processed to obtain remote sensing image data freshness information;

[0109] The model for calculating the freshness of remote sensing image data is as follows:

[0110]

[0111] 1≤i≤N;

[0112] In the formula, XXD represents the freshness information of the remote sensing image data. i Let V1, V2, and V3 be the first, second, and third freshness values, respectively, and T1 and T2 be the first and second time thresholds, respectively. i The generation time value of the i-th remote sensing image data in the remote sensing image data set to be processed is denoted as N, and the number of remote sensing image data in the remote sensing image data set to be processed is denoted as N.

[0113] It should be noted that the specific values ​​of T1, T2, V1, V2 and V3 can be set by the user or obtained from historical data, and this embodiment of the invention does not limit them.

[0114] For example, T1 can be 36, representing 36 months, or 3 years, and T2 can be 12, representing 12 months, or 1 year. When T1 is 36 and T2 is 12, if the generation time of the remote sensing image data to be processed (i.e., the time when the remote sensing image corresponding to the remote sensing image data to be processed was generated) is greater than 3 years, it indicates that the data is too old and has poor timeliness; if the generation time is more than 1 year but less than 3 years, it indicates that the data is relatively old and has poor timeliness; if the generation time is less than 1 year, it indicates that the data is relatively new and has the highest timeliness.

[0115] It should be noted that when T1 is 36 and T2 is 12, it means that the data is older than 3 years, and the freshness is a fixed value V1. A low value (such as 0.1) needs to be set to indicate that the data is too old and has poor timeliness. For data between 3 years and 1 year: the freshness increases linearly from V1 to V2 (V2>V1, such as V2=0.7), that is, the closer the data is to 1 year, the higher the freshness. For data within 1 year: the freshness increases linearly from V2 to V3 (usually 1, indicating that the latest data has the highest timeliness), that is, the closer the data is to the current time, the higher the freshness.

[0116] For example, the values ​​of V1, V2 and V3 are 0.1, 0.7 and 1.0, respectively.

[0117] By using the freshness information of remote sensing image data, we can obtain the freshness value of the remote sensing image data to be processed as it changes dynamically over time. This dynamically changing freshness value can objectively reflect the changes in the value assessment of remote sensing image data, thereby enabling us to obtain a more accurate value assessment of remote sensing image data.

[0118] S2312, using the remote sensing image data content value calculation model, calculates and processes the remote sensing image data information set and the remote sensing image data freshness information to obtain the remote sensing image data content value information.

[0119] The calculation model for the content value of remote sensing image data is as follows:

[0120]

[0121] GZM = max{GZ i |i=1,2,…,N};

[0122] FBMA = max{FB i |i=1,2,…,N};

[0123] FBMI = min{FB i |i=1,2,…,N};

[0124] 1≤i≤N;

[0125] δ1+δ2+δ3+δ4=1;

[0126] 0≤δ1,δ2,δ3,δ4≤1;

[0127] In the formula, NR represents the content value information of the remote sensing image data. i MS represents the value of the i-th remote sensing image data content in the remote sensing image data content value information. i GZ i and FBi These represent the imaging mode value, number of points of interest, and image resolution of the i-th remote sensing image data in the dataset to be processed, respectively, XXD. i Let δ1, δ2, δ3, and δ4 be the first weight parameter, the second weight parameter, the third weight parameter, and the fourth weight parameter, respectively, in the freshness information of remote sensing image data.

[0128] It should be noted that the first weight parameter, the second weight parameter, the third weight parameter, and the fourth weight parameter can be set by the user or obtained from historical data. Specifically, this embodiment of the invention does not limit the specific weight parameters.

[0129] It should be noted that dynamically weighting the freshness value, imaging mode value, number of points of interest, and image resolution of remote sensing image data helps to obtain the comprehensive value of the data content. The freshness value, imaging mode value, number of points of interest, and image resolution of remote sensing image data are all closely related to the spatial quality, information content, and usability of the remote sensing image data content. By weighting these data for evaluation, we can help verify the value of the data content from multiple dimensions, ensuring that accurate information on the value of remote sensing image data content can be obtained from multiple dimensions. This provides precise information on the value of remote sensing image data content from the perspective of data content. Furthermore, the freshness value (a dynamic attribute of remote sensing images that changes over time), imaging mode value, number of points of interest, and image resolution (static attributes of remote sensing images) can accurately and objectively reflect the changes in the value information of remote sensing image data content.

[0130] It should be noted that the content value of remote sensing image data mainly reflects the informational value of the image. This value is primarily manifested in the satellite's own observational attributes (such as imaging mode and resolution), the area covered (such as the number of points of interest included), and the freshness of the data. Generally speaking, the higher the resolution, the higher the informational value of the image; the more points of interest included, the higher the informational value; and the more recently the image was captured, the higher its informational value.

[0131] It is evident that implementing the remote sensing image data value assessment method described in the embodiments of the present invention is beneficial for accurately assessing the value of remote sensing image data, thereby providing a reference for applications such as data storage and migration and intelligent recommendation of remote sensing image data.

[0132] In an optional embodiment, a second computational process is performed on the remote sensing image data information set and the remote sensing image data type information to be processed to obtain remote sensing image data quality value information, including:

[0133] S2321, Determine whether the remote sensing image data type information is cataloging data information, and obtain the first judgment result;

[0134] If the first judgment result is yes, execute S2322;

[0135] If the result of the first judgment is negative, execute S2323;

[0136] It should be noted that when the first judgment result is yes, the remote sensing image data information to be processed is catalog data; when the first judgment result is no, the remote sensing image data information to be processed is product data. This allows for the simultaneous classification and processing of both catalog data and product data, thereby obtaining the corresponding remote sensing image data quality value information.

[0137] S2322, Using the first remote sensing image data quality value model, the remote sensing image data information set to be processed is calculated and processed to obtain the remote sensing image data quality value information;

[0138] The first remote sensing image data quality value model is as follows:

[0139] ZL j =θ1·(1-YL) j )+θ2·(1-HK j )1≤j≤M;

[0140] θ1 + θ2 = 1;

[0141] 0≤θ1,θ2≤1;

[0142] In the formula, ZL represents the quality value information of remote sensing image data. j YL represents the j-th remote sensing image data quality value in the remote sensing image data quality value information. j and HK j θ1 and θ2 are the cloud cover detection result and black block detection result in the j-th remote sensing image data information in the remote sensing image data information set to be processed, respectively; M is the number of remote sensing image data information to be processed in the remote sensing image data information set to be processed.

[0143] It should be noted that the first weighting coefficient and the second weighting coefficient can be set by the user or obtained from historical data. Specifically, this embodiment of the invention does not limit the specific weighting coefficient.

[0144] It should be noted that cloud cover detection results and black patch detection results can reflect common quality problems in remote sensing imagery, affecting the image's usability and accuracy. When dealing with cataloged data, using cloud cover detection results and black patch detection results to assess the quality value of remote sensing imagery data can reliably and quickly obtain this information.

[0145] S2323, using the second remote sensing image data quality value model, calculates and processes the remote sensing image data information set to be processed to obtain remote sensing image data quality value information;

[0146] The second remote sensing image data quality value model is as follows:

[0147] ZL j =θ3·(1-YL) j )+θ4·(1-HK j )+θ5·JD j +θ6·XD j +θ7·JF j +θ8

[0148] ·XF j ;

[0149] 1≤j≤M;

[0150] θ3+θ4+θ5+θ6+θ7+θ8=1;

[0151] 0≤θ3,θ4,θ5,θ6,θ7,θ8≤1;

[0152] In the formula, ZL represents the quality value information of remote sensing image data. j YL represents the j-th remote sensing image data quality value in the remote sensing image data quality value information. j HK j JD j XD j JF j and XF j θ1, θ2, θ3, θ4, θ5, θ6, θ7, and θ8 are the cloud cover detection result, black block detection result, absolute positioning accuracy, relative positioning accuracy, absolute radiometric accuracy, and relative radiometric accuracy values ​​in the j-th remote sensing image data information in the remote sensing image data information set to be processed, respectively. θ4, θ5, θ6, θ7, and θ8 are the third, fourth, fifth, sixth, seventh, and eighth weighting coefficients, respectively. M is the number of remote sensing image data information to be processed in the remote sensing image data information set to be processed.

[0153] It should be noted that the third, fourth, fifth, sixth, seventh, and eighth weighting coefficients can be set by the user or obtained from historical data. Specifically, this embodiment of the invention does not limit the specific weighting coefficients.

[0154] It should be noted that when the remote sensing image data to be processed is product data, the absolute positioning accuracy, relative positioning accuracy, absolute radiometric accuracy, and relative radiometric accuracy of the remote sensing image can be obtained. By combining the absolute positioning accuracy, relative positioning accuracy, absolute radiometric accuracy, and relative radiometric accuracy of the remote sensing image, along with cloud cover detection results and black spot detection results, the quality value information of the remote sensing image data can be calculated. This allows for a comprehensive evaluation of the multi-dimensional quality of the remote sensing image data to be processed, improving the efficiency and accuracy of data selection, and obtaining more precise quality value information of the remote sensing image data.

[0155] It is evident that implementing the remote sensing image data value assessment method described in the embodiments of the present invention is beneficial for accurately assessing the value of remote sensing image data, thereby providing a reference for applications such as data storage and migration and intelligent recommendation of remote sensing image data.

[0156] In an optional embodiment, a third computational process is performed on the remote sensing image data set to be processed to obtain remote sensing image user attention value information, including:

[0157] Using a remote sensing image data user attention calculation model, a third calculation process is performed on the remote sensing image data information set to be processed to obtain remote sensing image user attention value information.

[0158] The model for calculating user attention to remote sensing image data is as follows:

[0159]

[0160] 1≤r≤U;

[0161] δ7+δ8=1;

[0162] 0≤δ7,δ8≤1;

[0163] In the formula, GZD represents the user attention value information of remote sensing imagery. r Let XD be the r-th user attention value information value in the remote sensing image user attention value information. r and CX rδ7 and δ8 are the number of user orders generated and the number of user queries for the r-th remote sensing image data in the set of remote sensing image data to be processed, respectively. δ7 and δ8 are the seventh and eighth weight parameters, respectively. U is the number of remote sensing image data in the set of remote sensing image data to be processed.

[0164] It should be noted that the seventh and eighth weight parameters can be set by the user or obtained from historical data, and this embodiment of the invention does not limit them.

[0165] It should be noted that the number of user orders generated and the number of user queries are important indicators for measuring user demand and interest. By combining these two data indicators, we can better understand the level of user attention to remote sensing images within a specific time period. The user attention value information of remote sensing images can reflect their application attributes. If the same remote sensing image is accessed multiple times, it indicates that the data contains greater application value.

[0166] It is evident that implementing the remote sensing image data value assessment method described in the embodiments of the present invention is beneficial for accurately assessing the value of remote sensing image data, thereby providing a reference for applications such as data storage and migration and intelligent recommendation of remote sensing image data.

[0167] In an optional embodiment, the content value information, quality value information, and user attention value information of remote sensing image data are fused to obtain remote sensing image data value assessment information, including:

[0168] S31, normalize the content value information, quality value information, and user attention value information of remote sensing image data respectively to obtain normalized data content value information, normalized data quality value information, and normalized user attention value information.

[0169] S32. Using the remote sensing image data value calculation model, the value information of normalized data content, the value information of normalized data quality, and the value information of normalized user attention are calculated and processed to obtain the value assessment information of remote sensing image data.

[0170] The value calculation model for remote sensing image data is as follows:

[0171] SJJZ z =GZL z ·(δ5·GNR z +δ6·GGZD z 1≤z≤L;

[0172] In the formula, SJJZ represents the value assessment information of remote sensing image data. zGZL represents the value assessment value of the z-th remote sensing image data in the remote sensing image data value assessment information. z GNR z and GGZD z δ5 and δ6 are the z-th normalized data quality value information value, the z-th normalized data content value information value, and the z-th normalized user attention value information value, respectively. δ5 and δ6 are the fifth and sixth weight parameters, respectively, and L is the number of normalized data quality value information values ​​in the normalized data quality value information.

[0173] It should be noted that the number of normalized data quality value information values ​​in normalized data quality value information, the number of normalized data content value information values ​​in normalized data content value information, and the number of normalized user attention value information values ​​in normalized user attention value information are consistent.

[0174] It should be noted that the fifth and sixth weight parameters can be set by the user or obtained from historical data, and this embodiment of the invention does not limit them.

[0175] It should be noted that δ5 + δ6 = 1; 0 ≤ δ5, δ6 ≤ 1.

[0176] It should be noted that the above calculations enable a comprehensive assessment of the value of remote sensing image data. By fully combining the data characteristics of three dimensions—data content, data quality, and user attention—highly accurate and comprehensive remote sensing image data value assessment information can be obtained, providing a reference for applications such as data storage and migration and intelligent recommendation of remote sensing image data.

[0177] It is evident that implementing the remote sensing image data value assessment method described in the embodiments of the present invention is beneficial for accurately assessing the value of remote sensing image data, thereby providing a reference for applications such as data storage and migration and intelligent recommendation of remote sensing image data.

[0178] In an optional embodiment, the content value information, quality value information, and user attention value information of remote sensing image data are normalized respectively to obtain normalized data content value information, normalized data quality value information, and normalized user attention value information, including:

[0179] S311, Normalize the content value information of remote sensing image data to obtain normalized data content value information;

[0180] S312, Normalize the quality value information of remote sensing image data to obtain normalized data quality value information;

[0181] It should be noted that the normalization of remote sensing image data quality value information to obtain normalized data quality value information is achieved through a minimum-maximum normalization algorithm. Specific details are not limited in the embodiments of this invention.

[0182] S313, normalize the user attention value information of remote sensing images to obtain normalized user attention value information.

[0183] It is evident that implementing the remote sensing image data value assessment method described in the embodiments of the present invention is beneficial for accurately assessing the value of remote sensing image data, thereby providing a reference for applications such as data storage and migration and intelligent recommendation of remote sensing image data.

[0184] In an optional embodiment, the content value information of remote sensing image data is normalized to obtain normalized data content value information, including:

[0185] The first normalization calculation module is used to normalize the content value information of remote sensing image data to obtain normalized data content value information.

[0186] The first normalization calculation module is as follows:

[0187]

[0188] In the formula, GNR represents the normalized data content value information. i1 NR represents the i-th normalized data content value information value in the normalized data content value information. i1 Let be the value of the i1th remote sensing image data content in the remote sensing image data content value information. NRI and NRM are the minimum and maximum values ​​of the remote sensing image data content value information among all the values ​​of the remote sensing image data content value information, respectively. ∈ is the importance factor, and ∈ is a positive integer.

[0189] It should be noted that the importance factor can be set by the user or obtained from historical data, and this embodiment of the invention does not limit it.

[0190] It should be noted that normalized data content value information can reflect the data content value of remote sensing images. Among the three aspects of data content, data quality, and user attention, content value has the greatest impact on the overall value assessment of remote sensing images. When using the remote sensing image data value calculation model to calculate and process normalized data content value information, normalized data quality value information, and normalized user attention value information, the importance factor can reflect the degree of importance of normalized data content value information in these three aspects. The higher the importance, the larger the value of the importance factor. For example, the value of the importance factor is 2.

[0191] It is evident that implementing the remote sensing image data value assessment method described in the embodiments of the present invention is beneficial for accurately assessing the value of remote sensing image data, thereby providing a reference for applications such as data storage and migration and intelligent recommendation of remote sensing image data.

[0192] In an optional embodiment, the user attention value information of remote sensing imagery is normalized to obtain normalized user attention value information, including:

[0193] Using the second normalization calculation model, the user attention value information of remote sensing images is normalized to obtain normalized user attention value information;

[0194] The second normalization calculation model is as follows:

[0195]

[0196] In the formula, GGZD represents the normalized user attention value information. i2 GZD represents the i-th normalized user attention value in the normalized user attention value information. i2 Let GZDM be the i-th user attention value information value in the remote sensing image user attention value information, and GZDI be the largest and smallest user attention value information value among all user attention value information values ​​in the remote sensing image user attention value information. N2 is the scaling factor, and N2 is the number of remote sensing image user attention value information values ​​in the remote sensing image user attention value information.

[0197] It should be noted that the scaling factor can be set by the user or obtained from historical data, and this embodiment of the invention does not limit it.

[0198] For example, the scaling factor is 2.

[0199] It should be noted that in practical applications, the range of user attention value information values ​​for various remote sensing images is very large. When normalizing by scaling factors, the scale of the data is adjusted to reduce the impact of large-range data on the analysis results, making the data more comparable and stable.

[0200] It is evident that implementing the remote sensing image data value assessment method described in the embodiments of the present invention is beneficial for accurately assessing the value of remote sensing image data, thereby providing a reference for applications such as data storage and migration and intelligent recommendation of remote sensing image data.

[0201] Example 2

[0202] Please see Figure 2 , Figure 2 This is a schematic diagram of the structure of a remote sensing image data value assessment device disclosed in an embodiment of the present invention. Figure 2 The described remote sensing image data value assessment device is applied to a sampling optimization system for remote sensing image data value assessment, such as a local server or cloud server used for remote sensing image data value assessment, etc., and the embodiments of the present invention are not limited thereto. Figure 2 As shown, the remote sensing image data value assessment device includes:

[0203] The acquisition module 201 is used to acquire remote sensing image data information set and remote sensing image data type information; the remote sensing image data information set includes several remote sensing image data information;

[0204] The first calculation module 202 is used to process the remote sensing image data information set and the remote sensing image data type information to obtain remote sensing image data content value information, remote sensing image data quality value information and remote sensing image user attention value information.

[0205] The second calculation module 203 is used to fuse the content value information, quality value information, and user attention value information of remote sensing image data to obtain remote sensing image data value assessment information.

[0206] It is evident that implementing the remote sensing image data value assessment device described in the embodiments of the present invention is beneficial for accurately assessing the value of remote sensing image data, thereby providing a reference for applications such as data storage and migration and intelligent recommendation of remote sensing image data.

[0207] Example 3

[0208] Please see Figure 3 , Figure 3 This is a schematic diagram of another remote sensing image data value assessment device disclosed in an embodiment of the present invention. Figure 3The described remote sensing image data value assessment device is applied in a remote sensing image data value assessment optimization system, such as a local server or cloud server used for remote sensing image data value assessment, etc., and the embodiments of the present invention are not limited thereto. Figure 3 As shown, the remote sensing image data value assessment device includes:

[0209] Processor 301;

[0210] A memory 302 containing executable program code is coupled to the processor 301;

[0211] The processor 301 calls the executable program code stored in the memory 302 to execute some or all of the steps of the remote sensing image data value assessment method of Embodiment 1.

[0212] It is evident that implementing the remote sensing image data value assessment device described in the embodiments of the present invention is beneficial for accurately assessing the value of remote sensing image data, thereby providing a reference for applications such as data storage and migration and intelligent recommendation of remote sensing image data.

[0213] Example 4

[0214] This invention discloses a computer-readable storage medium storing computer instructions. When the computer instructions are invoked, they are used to execute some or all of the steps of the remote sensing image data value assessment method of Embodiment 1.

[0215] Example 5

[0216] This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform some or all of the steps in the remote sensing image data value assessment method described in Embodiment 1.

[0217] The system embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0218] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0219] Finally, it should be noted that the remote sensing image data value assessment method and apparatus disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention, and are only used to illustrate the technical solutions of the present invention, not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for assessing the value of remote sensing image data, characterized in that, The method includes: S1, acquire remote sensing image data information set and remote sensing image data type information; the remote sensing image data information set includes several remote sensing image data information; S2, process the remote sensing image data information set and the remote sensing image data type information to obtain remote sensing image data content value information, remote sensing image data quality value information and remote sensing image user attention value information; S3, the content value information, quality value information, and user attention value information of the remote sensing image data are fused and processed to obtain remote sensing image data value assessment information; S2 includes: S21, preprocess the remote sensing image data information set to obtain a preprocessed remote sensing image data information set; the preprocessed remote sensing image data information set includes several preprocessed remote sensing image data information sets. S22, perform data extraction operation on the preprocessed remote sensing image data information set to obtain a remote sensing image data information set to be processed; the remote sensing image data information set to be processed includes several remote sensing image data information sets to be processed. S23, process the remote sensing image data information set to be processed and the remote sensing image data type information to obtain remote sensing image data content value information, remote sensing image data quality value information, and remote sensing image user attention value information, including: S231, Perform a first calculation process on the remote sensing image data information set to be processed to obtain remote sensing image data content value information; S232, perform a second calculation process on the remote sensing image data information set to be processed and the remote sensing image data type information to obtain remote sensing image data quality value information; S233, perform a third calculation process on the remote sensing image data information set to be processed to obtain remote sensing image user attention value information; S232 includes: S2321, determine whether the remote sensing image data type information is cataloging data information, and obtain the first determination result; When the first judgment result is yes, execute S2322; If the first judgment result is negative, execute S2323; S2322, Using the first remote sensing image data quality value model, the remote sensing image data information set to be processed is calculated and processed to obtain remote sensing image data quality value information; The first remote sensing image data quality value model is as follows: ; ; ; In the formula, This refers to the quality value information of the remote sensing image data. The first in the remote sensing image data quality value information The quality value information of remote sensing image data. and The first of the remote sensing image data information sets to be processed is... The cloud cover detection result value and black block detection result value in the remote sensing image data information to be processed. and These are the first weighting coefficient and the second weighting coefficient, respectively, and M is the number of remote sensing image data information to be processed in the remote sensing image data information set. S2323, using the second remote sensing image data quality value model, the remote sensing image data information set to be processed is calculated and processed to obtain remote sensing image data quality value information; The second remote sensing image data quality value model is as follows: ; ; ; ; In the formula, This refers to the quality value information of the remote sensing image data. The first in the remote sensing image data quality value information The aforementioned remote sensing image data quality value information value. , , , , and The first of the remote sensing image data information sets to be processed is... The cloud cover detection result value, black block detection result value, absolute positioning accuracy value, relative positioning accuracy value, absolute radiometric accuracy value, and relative radiometric accuracy value in the remote sensing image data information to be processed. , , , , and These are the third, fourth, fifth, sixth, seventh, and eighth weighting coefficients, respectively, and M is the number of remote sensing image data information to be processed in the remote sensing image data information set. S3 includes: S31, normalize the content value information, quality value information, and user attention value information of the remote sensing image data respectively to obtain normalized content value information, normalized quality value information, and normalized user attention value information. S32, using the remote sensing image data value calculation model, calculate and process the normalized data content value information, the normalized data quality value information, and the normalized user attention value information to obtain remote sensing image data value assessment information; The remote sensing image data value calculation model is as follows: ; In the formula, This is for the value assessment information of the remote sensing image data. The first in the value assessment information of the remote sensing image data Value assessment of remote sensing image data. , and The first of the normalized data quality value information is respectively the second. The first normalized data quality value information value, and the first normalized data content value information value. The value of normalized data content and the value of normalized user attention are both present in the first normalized data content value and the second normalized user attention value. A normalized user attention value information value. and These are the fifth and sixth weight parameters, respectively, and L is the number of normalized data quality value information values ​​in the normalized data quality value information. S31 includes: S311, Normalize the content value information of remote sensing image data to obtain normalized data content value information; S312, Normalize the quality value information of remote sensing image data to obtain normalized data quality value information; S313, using the second normalization calculation model, the user attention value information of the remote sensing image is normalized to obtain normalized user attention value information; The second normalization calculation model is as follows: In the formula, GGZD represents the normalized user attention value information. The value of the i2th normalized user attention value in the normalized user attention value information is... Let GZDM be the i2th user attention value information value in the remote sensing image user attention value information, and GZDI be the largest and smallest user attention value information value among all user attention value information values ​​in the remote sensing image user attention value information, respectively. N2 is the scaling factor, and N2 is the number of remote sensing image user attention value information values ​​in the remote sensing image user attention value information.

2. The method for assessing the value of remote sensing image data according to claim 1, characterized in that, The preprocessing of the remote sensing image data information set to obtain a preprocessed remote sensing image data information set includes: S211, perform data integrity processing on any of the remote sensing image data information in the remote sensing image data information set to obtain the first remote sensing image data information corresponding to the remote sensing image data information; S212, perform metadata checking and processing on the first remote sensing image data information corresponding to the remote sensing image data information to obtain the preprocessed remote sensing image data information corresponding to the remote sensing image data information.

3. The method for assessing the value of remote sensing image data according to claim 1, characterized in that, The first calculation processing of the remote sensing image data information set to be processed to obtain remote sensing image data content value information includes: S2311, Using the remote sensing image data freshness calculation model, the remote sensing image data information set to be processed is calculated and processed to obtain remote sensing image data freshness information. The remote sensing image data freshness calculation model is as follows: ; ; In the formula, This refers to the freshness information of the remote sensing image data. The first in the remote sensing image data freshness information A freshness value for remote sensing image data. , and These are the first freshness value, the second freshness value, and the third freshness value, respectively. and These are the first time threshold and the second time threshold, respectively. The generation time value of the i-th remote sensing image data information in the set of remote sensing image data information to be processed is denoted as N, and N is the number of remote sensing image data information to be processed in the set of remote sensing image data information to be processed. S2312, Using the remote sensing image data content value calculation model, the remote sensing image data information set to be processed and the remote sensing image data freshness information are calculated and processed to obtain the remote sensing image data content value information. The calculation model for the content value of the remote sensing image data is as follows: ; ; ; ; ; ; ; In the formula, The value information of the remote sensing image data content, The first of the value information in the remote sensing image data content The value of information content in remote sensing image data. , and These represent the imaging mode value, number of points of interest, and image resolution of the i-th remote sensing image data in the set of remote sensing image data to be processed, respectively. The first in the remote sensing image data freshness information The aforementioned remote sensing image data freshness value , , and These are the first weight parameter, the second weight parameter, the third weight parameter, and the fourth weight parameter, respectively.

4. A device for assessing the value of remote sensing image data, characterized in that, The device includes: processor; A memory coupled to the processor stores executable program code; The processor calls the executable program code stored in the memory to execute the remote sensing image data value assessment method as described in any one of claims 1-3.

5. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, which, when invoked, are used to execute the remote sensing image data value assessment method as described in any one of claims 1-3.

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