A multi-dimensional remote sensing data product value evaluation method

Abstract

This invention discloses a multi-dimensional remote sensing data product value assessment method, belonging to the field of remote sensing data processing and value assessment technology. The method includes: collecting basic information on remote sensing data products and establishing a basic assessment database; constructing a multi-dimensional assessment index system covering five aspects: data quality, spatiotemporal characteristics, application effectiveness, cost input, and market demand; using the analytic hierarchy process (AHP) combined with the entropy weight method to determine the weights of indicators under each dimension; quantifying and scoring each indicator, and calculating the comprehensive value assessment score through weighted summation; classifying value levels and forming assessment conclusions; verifying the assessment results and dynamically updating the assessment system. This invention effectively solves the problems of existing assessment methods being single-dimensional, highly subjective, and lacking specificity, achieving a scientific, objective, and efficient assessment of the comprehensive value of remote sensing data products. It provides a reliable basis for pricing, trading, and application selection of remote sensing data products, and powerfully promotes the circulation and industrialization of remote sensing data products.

Description

Technical Field

[0001] This invention relates to the field of remote sensing data processing and data value assessment technology, specifically disclosing a multi-dimensional remote sensing data product value assessment method. This method is applicable to various remote sensing data products, including optical, synthetic aperture radar (SAR), hyperspectral, and infrared data, and can be widely used in scenarios such as remote sensing data asset management, remote sensing data product services, remote sensing data procurement and pricing, land use surveys and supervision, ecological environment monitoring, and commercial remote sensing data applications. It provides a scientific and reliable assessment basis for the pricing, trading, and application selection of remote sensing data products. Background Technology

[0002] With the rapid development of the aerospace industry, remote sensing data has become a core component of space big data assetization, encompassing multiple fields such as ground-based observation, UAV observation, airborne remote sensing, and satellite remote sensing. This has resulted in massive amounts of remote sensing data products across various sensor types and resolutions, widely applied in key areas such as ecological environment monitoring, land spatial planning, agriculture, forestry and water conservancy, emergency disaster relief, and national defense. The value assessment of remote sensing data products is a fundamental prerequisite for the circulation of data elements and a crucial support for improving data quality and optimizing application efficiency. It is of significant practical importance for constructing a space-air remote sensing data product service system encompassing "data owners - data processors - data service providers - data exchanges - data users."

[0003] Currently, existing methods for assessing the value of remote sensing data products still have several prominent shortcomings: First, the assessment dimensions are relatively singular, mostly focusing only on data quality or a single application scenario, failing to comprehensively cover the core characteristics of remote sensing data products and making it difficult to objectively reflect the comprehensive value of the products; second, the assessment indicators lack unified standards, and a standardized assessment system has not been formed. Different assessment entities adopt different indicator systems and weight allocations, resulting in highly subjective and incomparable assessment results, failing to effectively address the uncertainty in value valuation caused by the diverse sources and differentiated application scenarios of remote sensing data; third, the assessment methods lack specificity, failing to design dedicated assessment logic based on the unique attributes of remote sensing data products (such as timeliness, spatial resolution, radiometric accuracy, etc.), making it difficult for general assessment methods to adapt to the value differences of different types of remote sensing data products; fourth, the assessment process is cumbersome and complex, relying excessively on manual scoring and experience-based judgment, with low levels of intelligence and low assessment efficiency, failing to meet the rapid assessment needs of massive remote sensing data products.

[0004] In existing technologies, some evaluation methods focus only on a single dimension such as data quality or application benefits, failing to fully consider core dimensions of remote sensing data products, such as timeliness, resolution, production cost, and application benefits. This results in biased and unreliable evaluation results, unable to provide reliable support for practical needs such as data trading and application selection. Therefore, there is an urgent need to develop a multi-dimensional, standardized, intelligent, and targeted method for evaluating the value of remote sensing data products. This method should comprehensively cover the core characteristics of remote sensing data products, achieve standardization and efficiency in the evaluation process, and significantly improve the objectivity and accuracy of the evaluation results. Summary of the Invention

[0005] To address the shortcomings of existing remote sensing data product value assessment methods, such as limited dimensions, inconsistent indicators, lack of specificity, and low level of intelligence, this invention provides a multi-dimensional remote sensing data product value assessment method. By designing multiple core evaluation indicators and constructing a standardized evaluation system, this method achieves a scientific, efficient, and objective assessment of the comprehensive value of remote sensing data products. It provides a reliable basis for the pricing, trading, application selection, and asset management of remote sensing data products, and adapts to the actual needs of the circulation of aerospace remote sensing data elements.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows: A method for evaluating the value of multi-dimensional remote sensing data products includes the following steps: S1. Determine the type, purpose, and data source of the remote sensing data product to be evaluated, collect basic information on the remote sensing data product to be evaluated, including basic parameters, observation and production processes, and application cases, and establish an evaluation basic database; S2. Construct a multi-dimensional evaluation index system, including multiple evaluation dimensions, such as data quality, spatiotemporal characteristics, application effectiveness, cost input, and market demand. Each evaluation dimension corresponds to one or more specific evaluation indicators. The multiple evaluation indicators include: data spatial resolution, data temporal resolution, data radiometric accuracy, data geometric accuracy, data integrity, data timeliness, data compatibility, data security, application benefits, observation and production costs, market demand, and feedback evaluation from special users. S3. Using the analytic hierarchy process combined with the entropy weight method, and integrating subjective experience and objective data, the subjective and objective weights of the specific evaluation indicators under each evaluation dimension are determined. The final weight is a weighted fusion of the subjective and objective weights. S4. For each evaluation indicator, develop a quantitative scoring standard, and combine the information in the evaluation database, field verification data, industry standards and application feedback to quantitatively score each evaluation indicator and obtain the individual score for each evaluation indicator. S5. Based on the weight of each evaluation indicator and the corresponding individual score, the weighted summation method is used to calculate the comprehensive value evaluation score of the remote sensing data product. S6. Based on the comprehensive value assessment score, classify different value levels, combine the individual scores of each assessment indicator, analyze the advantages and disadvantages of remote sensing data products, and form a complete assessment conclusion. S7. Compare and verify the evaluation conclusions with actual application effects and market transaction prices, and revise the evaluation indicators and weights accordingly. At the same time, dynamically update the evaluation indicator system, scoring standards and weights based on the development of remote sensing technology, changes in application scenarios and market demand.

[0007] Furthermore, the specific definitions of the evaluation indicators in S2 are as follows: 1) Data spatial resolution: refers to the size of the smallest ground target that remote sensing data products can distinguish, reflecting the spatial detail representation capability of the data. Corresponding evaluation factors include ground sampling distance, image sharpness, and richness of detail and texture. 2) Data temporal resolution: refers to the time interval between repeated observations of the same area by the remote sensing data observation and production system, reflecting the dynamic monitoring capability of the data. The corresponding evaluation factors include revisit cycle, observation frequency and temporal continuity. 3) Data radiometric accuracy: refers to the degree to which remote sensing data products accurately reflect the radiometric characteristics of ground targets, reflecting the radiometric authenticity of the data. The corresponding evaluation factors include radiometric calibration accuracy, radiometric consistency, and noise level. 4) Data geometric accuracy: refers to the degree of agreement between the geometric position of the ground target in the remote sensing data product and its actual position, reflecting the spatial positioning accuracy of the data. The corresponding evaluation factors include positioning error, geometric correction accuracy and stitching accuracy. 5) Data integrity: refers to the completeness of the area, band, time range and data volume covered by remote sensing data products. The corresponding evaluation factors include regional coverage integrity, band coverage integrity, time series integrity and data missing rate. 6) Data timeliness: refers to the time interval from when remote sensing data is observed and received by remote sensing satellites to when it is delivered to users. It reflects the real-time nature of the data. The corresponding evaluation factors include the time from when remote sensing satellites conduct observations to when the data is received, the data processing cycle, and the distribution and delivery time. 7) Data compatibility: refers to the degree of compatibility between remote sensing data products and existing data processing software, analysis tools, storage formats and other remote sensing data products. The corresponding evaluation factors include format compatibility, software adaptability and data interoperability. 8) Data security: refers to the security assurance capabilities of remote sensing data products during storage, transmission and use, reflecting the confidentiality, integrity and availability of data. The corresponding evaluation factors include data encryption capabilities, access control permissions, data backup and recovery capabilities and classified information processing capabilities. 9) Application benefits: refers to the economic, social and ecological benefits generated by remote sensing data products in practical applications. The corresponding evaluation factors include economic benefits, social benefits and ecological benefits. Among them, economic benefits include cost savings and efficiency improvement, social benefits include decision support and emergency response, and ecological benefits include environmental monitoring and ecological protection. 10) Observation production cost: refers to the total cost of remote sensing data products from data demand submission, observation planning and execution, data reception and processing to distribution and delivery. The corresponding evaluation factors include data observation cost, processing cost, labor cost and equipment cost. 11) Market demand: refers to the strength of market demand for the remote sensing data products to be evaluated, reflecting the market value of the products. The corresponding evaluation factors include market demand, market share, user growth rate and product scarcity. 12) Special User Feedback Evaluation: This refers to the evaluation provided by relevant users through the remote sensing data service system when remote sensing data products demonstrate application value to certain special industry users in specific business scenarios. The system conducts a special user weighted evaluation of the remote sensing data products based on the user's importance level and feedback evaluation score. The corresponding evaluation factors include the special user's importance level and the special user's feedback evaluation score.

[0008] Furthermore, the specific process for determining the weights in S3 is as follows: S31. Construct a hierarchical model: The comprehensive value of remote sensing data products is the target layer, 12 evaluation indicators are the criteria layer, and the specific remote sensing data products to be evaluated under each evaluation indicator are the implementation layer. S32. Determine subjective weights: Compare and score the evaluation indicators of the criterion layer pairwise, construct a judgment matrix, and calculate the subjective weights after passing the consistency test. S33. Determine the objective weights: Based on the data in the evaluation database, calculate the entropy value of each evaluation indicator, and determine the objective weight of each evaluation indicator according to the magnitude of the entropy value. The smaller the entropy value, the greater the weight. S34. Weight fusion: The weighted average method is used to fuse subjective weights and objective weights.

[0009] Furthermore, the quantitative scoring criteria in S4 are divided into the following ranges: excellent, good, qualified, and unqualified. For quantifiable evaluation indicators, scores are assigned based on the comparison between actual parameters and standard parameters. For indicators that cannot be directly quantified, fuzzy comprehensive evaluation method is used for quantification.

[0010] Furthermore, the formula for calculating the comprehensive value assessment score in S5 is: V = Σ(W i × S i ), where V is the comprehensive value assessment score, and W iS represents the comprehensive weight of the i-th evaluation indicator. i Let be the score of the i-th evaluation indicator, where i = 1, 2, ..., 12.

[0011] Furthermore, the value levels in S6 are divided into: excellent, good, qualified, and unqualified; the evaluation conclusion includes the overall score, value level, and strengths and weaknesses of each indicator, and proposes optimization suggestions.

[0012] Furthermore, the remote sensing data products include optical, synthetic aperture radar, hyperspectral, and infrared data, and their applications include ecological environment monitoring, land spatial planning, agriculture, forestry and water conservancy, and land use surveys and supervision.

[0013] Compared with the prior art, the present invention has the following significant advantages: 1) Comprehensive and complete evaluation indicators: This invention designs 12 core evaluation indicators, which comprehensively cover five aspects: data quality, spatiotemporal characteristics, application effectiveness, cost input, and market demand. It fully covers the core characteristics of remote sensing data products, effectively solves the problems of single indicators and one-sided evaluation in existing evaluation methods, and can truly and comprehensively reflect the comprehensive value of remote sensing data products, adapting to the actual needs of aerospace remote sensing data element evaluation. 2) High degree of standardization: This invention constructs a standardized evaluation index system, quantitative scoring standards and weight determination methods, unifies the evaluation logic and operation process, greatly reduces the influence of human subjective judgment, significantly improves the objectivity, comparability and reliability of evaluation results, and effectively solves the problems of inconsistent evaluation indicators and uncertain valuation in the existing evaluation system. 3) Highly targeted: This invention combines the unique attributes of remote sensing data products (such as spatial resolution, temporal resolution, radiometric accuracy, etc.) to design exclusive evaluation dimensions and specific indicators, which can be adapted to various remote sensing data products such as optical, SAR, hyperspectral, and infrared data, while taking into account the differentiated needs of different application scenarios, making the evaluation results more targeted and valuable for reference. 4) Intelligent and efficient: This invention uses the analytic hierarchy process combined with the entropy weight method to determine the weights, and combines the fuzzy comprehensive evaluation method to achieve accurate quantification of indicators that are difficult to quantify, which greatly reduces manual intervention and significantly improves evaluation efficiency. It can meet the rapid evaluation needs of massive remote sensing data products and adapt to the development pace of the industrialization of aerospace remote sensing data elements. 5) Strong dynamic adaptability: This invention supports the dynamic updating of the evaluation index system, scoring standards and weights, which can flexibly adapt to the development of remote sensing technology, the expansion of application scenarios and changes in market demand, maintain the scientificity and adaptability of the evaluation method in the long term, and ensure that the evaluation results always meet the actual needs. 6) Highly practical: The evaluation results of this invention can directly provide a reliable basis for the pricing, trading, application selection, quality optimization, and asset management of remote sensing data products, effectively promoting the circulation and industrialization of remote sensing data elements and helping to fully release the value of aerospace remote sensing data. Attached Figure Description

[0014] Figure 1 This is a flowchart illustrating the overall process of the multi-dimensional remote sensing data product value assessment method of the present invention.

[0015] Figure 2 This is a schematic diagram of the hierarchical structure of the multi-dimensional evaluation index system of this invention.

[0016] Figure 3 This is a schematic diagram illustrating the calculation of the comprehensive value assessment score and the classification of value levels for this invention. Detailed Implementation

[0017] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can understand and implement it.

[0018] Example 1: Taking a certain optical remote sensing satellite data product (used for target surveillance) as an example, the value assessment is performed using the method of this invention, such as... Figure 1 As shown, the specific steps are as follows: S1. Determine the assessment object and collect basic information: Clarify the type of remote sensing data product to be assessed (including but not limited to optical, SAR, hyperspectral, infrared, etc.), application purpose (including but not limited to land use survey and supervision, ecological monitoring, farmland water conservancy monitoring, land planning, etc.), and data source (including but not limited to satellite, UAV, aviation, etc.). Collect core basic information such as basic parameters, observation and production process, and application cases of the remote sensing data product to be assessed, and establish a standardized assessment basic database to provide data support for subsequent assessment work. In this embodiment, the object to be evaluated is a high-resolution optical remote sensing satellite data product, which is used for target surveillance, and the data source is a high-resolution satellite. The system collects the product's basic parameters (spatial resolution 1m, temporal resolution 5 days, radiometric calibration accuracy ±5%, positioning error ≤5m), production process (data acquisition - preprocessing - geometric correction - radiometric correction - product generation - verification and delivery), application cases (aircraft target surveillance in a certain area, airport runway status monitoring), and other core basic information to establish a standardized evaluation database.

[0019] S2. Constructing a multi-dimensional evaluation index system: This invention employs 12 core evaluation indicators, covering five dimensions: data quality, spatiotemporal characteristics, application effectiveness, cost investment, and market demand, forming a complete evaluation index system. Figure 2 As shown, the specific evaluation indicators are as follows: 1) Data spatial resolution: refers to the size of the smallest ground target that remote sensing data products can distinguish, reflecting the spatial detail representation capability of the data. The corresponding evaluation factors include ground sampling distance (1m), image sharpness and detail texture richness. 2) Data temporal resolution: refers to the time interval between repeated observations of the same area by the remote sensing data observation and production system, reflecting the dynamic monitoring capability of the data. The corresponding evaluation factors include revisit period (5 days), observation frequency and temporal continuity. 3) Data radiometric accuracy: refers to the degree to which remote sensing data products accurately reflect the radiometric characteristics of ground targets, reflecting the radiometric authenticity of the data. The corresponding evaluation factors include radiometric calibration accuracy (±5%), radiometric consistency, and noise level. 4) Data geometric accuracy: refers to the degree of agreement between the geometric position of the ground target in the remote sensing data product and its actual position, reflecting the spatial positioning accuracy of the data. The corresponding evaluation factors include positioning error (≤5m), geometric correction accuracy and stitching accuracy. 5) Data integrity: refers to the completeness of the area, band, time range and data volume covered by remote sensing data products. The corresponding evaluation factors include regional coverage integrity (covering more than 98% of the target area), band coverage integrity (covering visible light and near-infrared bands), time series integrity (no missing data for 12 consecutive months) and data no missing rate (99.5%). 6) Data timeliness: refers to the time interval from when remote sensing data is observed and received by remote sensing satellites to when it is delivered to users. It mainly reflects the real-time nature of the data (≤12 hours). 7) Data compatibility: refers to the degree of compatibility between remote sensing data products and existing data processing software, analysis tools, storage formats and other remote sensing data products. The corresponding evaluation factors include format compatibility (support for common remote sensing formats such as GeoTIFF, HDF-EOS, netCDF, MDD, etc.), software compatibility (compatibility with ENVI, ArcGIS software) and data interoperability. 8) Data security: refers to the security assurance capabilities of remote sensing data products during storage, transmission and use, reflecting the confidentiality, integrity and availability of data. The corresponding evaluation factors include data encryption capability (using AES encryption), access control permissions, data backup and recovery capability and classified information processing capability. 9) Application benefits: refers to the economic, social and ecological benefits generated by remote sensing data products in practical applications. The corresponding evaluation factors include economic benefits (reducing monitoring costs by 30%), social benefits (providing support for ecological protection decision-making) and ecological benefits (accurate monitoring of airport runway pollution). 10) Observation production cost: refers to the total cost of remote sensing data products from data demand submission, observation planning and execution, data reception and processing to distribution and delivery. The corresponding evaluation factors include data observation cost, processing cost, labor cost and equipment cost (e.g., total cost of approximately 6,000 yuan per scene). 11) Market demand: refers to the strength of market demand for the remote sensing data products to be evaluated, reflecting the market value of the products. The corresponding evaluation factors include market demand (more than 100 scenes per month), market share (15%), user growth rate (8% / year) and product scarcity (medium-high resolution optical remote sensing data products are moderately scarce).

[0020] 12) Special User Feedback Evaluation: This refers to the evaluation of remote sensing data products by relevant users in specific business scenarios, based on the application value of these products in certain special industries. These users provide feedback online through the remote sensing data service system. The system conducts a special user weighted evaluation of the remote sensing data products based on the user's importance level and feedback evaluation score. The corresponding evaluation factors include the special user's importance level (A key user level weight 0.7, B key user level weight 0.3, other user level weight 0) and the special user feedback evaluation score (A key user feedback data value 96 points, B key user feedback data value 86 points).

[0021] S3. Using the analytic hierarchy process combined with the entropy weight method, and integrating subjective experience and objective data, the weights of specific evaluation indicators under each evaluation dimension are determined. The final weights are a weighted fusion of subjective and objective weights. The specific process for determining the weights is as follows: Using the analytic hierarchy process (AHP) combined with the entropy weight method, five senior experts in the field of remote sensing were invited to score the data, constructing a judgment matrix and passing a consistency check to obtain the subjective weights. Based on the actual data in the evaluation database, the entropy value of each evaluation indicator was calculated to obtain the objective weights; the smaller the entropy value, the greater the weight. A weighted average method was used to integrate the subjective weights (50%) and the objective weights (50%) to obtain the comprehensive weights of the 12 evaluation indicators (example: data spatial resolution weight 0.10, data temporal resolution weight 0.09, data radiometric accuracy weight 0.10, data geometric accuracy weight 0.09, data integrity weight 0.08, data timeliness weight 0.08, data compatibility weight 0.07, data security weight 0.07, application benefit weight 0.10, production input cost weight 0.06, market demand weight 0.06, and special user feedback evaluation weight 0.10).

[0022] S4. Quantitative Scoring of Each Evaluation Indicator: Based on the standardized quantitative scoring criteria (scoring range 0-100 points, Excellent (90-100 points), Good (80-89 points), Pass (60-79 points), Unsatisfactory (0-59 points)), combined with the basic information in the evaluation database, on-site verification data, and user feedback, each indicator is precisely quantitatively scored to obtain the individual scores of 12 evaluation indicators (Example: Data spatial resolution 92 points, data temporal resolution 88 points, data radiometric accuracy 90 points, data geometric accuracy 89 points, data integrity 91 points, data timeliness 87 points, data compatibility 85 points, data security 86 points, application benefits 92 points, production input cost 78 points, market demand 84 points, special user feedback evaluation 93 points).

[0023] S5. Calculate the comprehensive value assessment score: According to the formula V = Σ(W i × S i The comprehensive value assessment score is calculated as follows: V = 0.10×92 + 0.09×88 + 0.10×90 + 0.09×89 + 0.08×91 + 0.08×87 + 0.07×85 + 0.07×86 + 0.10×93 + 0.10×92 + 0.06×78 + 0.06×84 = 88.56 points. A diagram illustrating the calculation of the comprehensive value assessment score and the classification of value levels is shown below. Figure 3 As shown.

[0024] S6. Determine the value level and assessment conclusion: The criteria for classifying the value level are: Excellent (V≥90 points), Good (80≤V<90 points), Pass (60≤V<80 points), and Unpass (V<60 points). Based on the comprehensive score of 88.56 points, the value level of this remote sensing data product is determined to be "Good". The assessment conclusion is: This optical remote sensing satellite data product has a good comprehensive value, and performs outstandingly in dimensions such as data spatial resolution, application adaptability, application benefits, and radiation accuracy, which can fully meet the application needs of target monitoring. The score of the production input cost index is relatively low. It is recommended to reduce costs by optimizing the production process and improving processing efficiency to further enhance the comprehensive value of the product.

[0025] S7. Evaluation Result Verification and Dynamic Update: The evaluation conclusions are compared and verified with the actual market transaction price of the product (800,000-1,000,000 RMB / batch) and user application feedback to confirm the rationality of the evaluation results. A comprehensive review of the evaluation indicator system, scoring standards and weights is conducted every 6 months. Relevant parameters are dynamically adjusted according to the development of remote sensing technology and changes in market demand to ensure the adaptability and scientific nature of the evaluation method.

Claims

1. A method for evaluating the value of multi-dimensional remote sensing data products, characterized in that, Includes the following steps: S1. Determine the type, purpose, and data source of the remote sensing data product to be evaluated, collect basic information on the remote sensing data product to be evaluated, including basic parameters, observation and production processes, and application cases, and establish an evaluation basic database; S2. Construct a multi-dimensional evaluation index system, including multiple evaluation dimensions, such as data quality, spatiotemporal characteristics, application effectiveness, cost input, and market demand. Each evaluation dimension corresponds to one or more specific evaluation indicators. The multiple evaluation indicators include: data spatial resolution, data temporal resolution, data radiometric accuracy, data geometric accuracy, data integrity, data timeliness, data compatibility, data security, application benefits, observation and production costs, market demand, and feedback evaluation from special users. S3. Using the analytic hierarchy process combined with the entropy weight method, and integrating subjective experience and objective data, the subjective and objective weights of the specific evaluation indicators under each evaluation dimension are determined. The final weight is a weighted fusion of the subjective and objective weights. S4. For each evaluation indicator, develop a quantitative scoring standard, and combine the information in the evaluation base database, field verification data, industry standards and application feedback to quantitatively score each evaluation indicator and obtain the individual score for each evaluation indicator. S5. Based on the weight of each evaluation indicator and the corresponding individual score, the weighted summation method is used to calculate the comprehensive value evaluation score of the remote sensing data product. S6. Based on the comprehensive value assessment score, classify different value levels, combine the individual scores of each assessment indicator, analyze the advantages and disadvantages of remote sensing data products, and form a complete assessment conclusion. S7. Compare and verify the evaluation conclusions with actual application effects and market transaction prices, and revise the evaluation indicators and weights accordingly. At the same time, dynamically update the evaluation indicator system, scoring standards and weights based on the development of remote sensing technology, changes in application scenarios and market demand.

2. The method for evaluating the value of multi-dimensional remote sensing data products according to claim 1, characterized in that, The specific definitions of the evaluation indicators in S2 are as follows: 1) Data spatial resolution: refers to the size of the smallest ground target that remote sensing data products can distinguish, reflecting the spatial detail representation capability of the data. Corresponding evaluation factors include ground sampling distance, image sharpness, and richness of detail and texture. 2) Data temporal resolution: refers to the time interval between repeated observations of the same area by the remote sensing data observation and production system, reflecting the dynamic monitoring capability of the data. The corresponding evaluation factors include revisit cycle, observation frequency and temporal continuity. 3) Data radiometric accuracy: refers to the degree to which remote sensing data products accurately reflect the radiometric characteristics of ground targets, reflecting the radiometric authenticity of the data. The corresponding evaluation factors include radiometric calibration accuracy, radiometric consistency, and noise level. 4) Data geometric accuracy: refers to the degree of agreement between the geometric position of the ground target in the remote sensing data product and its actual position, reflecting the spatial positioning accuracy of the data. The corresponding evaluation factors include positioning error, geometric correction accuracy and stitching accuracy. 5) Data integrity: refers to the completeness of the area, band, time range and data volume covered by remote sensing data products. The corresponding evaluation factors include regional coverage integrity, band coverage integrity, time series integrity and data missing rate. 6) Data timeliness: refers to the time interval from when remote sensing data is observed and received by remote sensing satellites to when it is delivered to users. It reflects the real-time nature of the data. The corresponding evaluation factors include the time from when remote sensing satellites conduct observations to when the data is received, the data processing cycle, and the distribution and delivery time. 7) Data compatibility: refers to the degree of compatibility between remote sensing data products and existing data processing software, analysis tools, storage formats and other remote sensing data products. The corresponding evaluation factors include format compatibility, software adaptability and data interoperability. 8) Data security: refers to the security assurance capabilities of remote sensing data products during storage, transmission and use, reflecting the confidentiality, integrity and availability of data. The corresponding evaluation factors include data encryption capabilities, access control permissions, data backup and recovery capabilities and classified information processing capabilities. 9) Application benefits: refers to the economic, social and ecological benefits generated by remote sensing data products in practical applications. The corresponding evaluation factors include economic benefits, social benefits and ecological benefits. Among them, economic benefits include cost savings and efficiency improvement, social benefits include decision support and emergency response, and ecological benefits include environmental monitoring and ecological protection. 10) Observation production cost: refers to the total cost of remote sensing data products from data demand submission, observation planning and execution, data reception and processing to distribution and delivery. The corresponding evaluation factors include data observation cost, processing cost, labor cost and equipment cost. 11) Market demand: refers to the strength of market demand for the remote sensing data products to be evaluated, reflecting the market value of the products. The corresponding evaluation factors include market demand, market share, user growth rate and product scarcity. 12) Special User Feedback Evaluation: This refers to the evaluation provided by relevant users through the remote sensing data service system when remote sensing data products demonstrate application value to certain special industry users in specific business scenarios. The system conducts a special user weighted evaluation of the remote sensing data products based on the user's importance level and feedback evaluation score. The corresponding evaluation factors include the special user's importance level and the special user's feedback evaluation score.

3. The method for evaluating the value of multi-dimensional remote sensing data products according to claim 1, characterized in that, The specific process for determining the weights in S3 is as follows: S31. Construct a hierarchical model: The comprehensive value of remote sensing data products is the target layer, 12 evaluation indicators are the criteria layer, and the specific remote sensing data products to be evaluated under each evaluation indicator are the implementation layer. S32. Determine subjective weights: Compare and score the evaluation indicators of the criterion layer pairwise, construct a judgment matrix, and calculate the subjective weights after passing the consistency test. S33. Determine the objective weights: Based on the data in the evaluation database, calculate the entropy value of each evaluation indicator, and determine the objective weight of each evaluation indicator according to the magnitude of the entropy value. The smaller the entropy value, the greater the weight. S34. Weight fusion: The weighted average method is used to fuse subjective weights and objective weights.

4. The method for evaluating the value of multi-dimensional remote sensing data products according to claim 1, characterized in that, The quantitative scoring criteria in S4 are divided into four ranges: excellent, good, qualified, and unqualified. For quantifiable evaluation indicators, scores are assigned based on the comparison between actual parameters and standard parameters. For indicators that cannot be directly quantified, fuzzy comprehensive evaluation method is used for quantification.

5. A method for evaluating the value of multi-dimensional remote sensing data products according to claim 1, characterized in that, The calculation formula of the comprehensive value evaluation score in S5 is: V = Σ(W i × S i ), wherein V is the comprehensive value evaluation score, W i is the comprehensive weight of the i th evaluation index, S i is the single item score of the i th evaluation index, i = 1, 2, …, 12.

6. The method for evaluating the value of multi-dimensional remote sensing data products according to claim 1, characterized in that, The value levels in S6 are divided into: excellent, good, qualified, and unqualified; the evaluation conclusion includes the overall score, value level, and strengths and weaknesses of each indicator, and proposes optimization suggestions.

7. The method for evaluating the value of multi-dimensional remote sensing data products according to claim 1, characterized in that, The remote sensing data products include optical, synthetic aperture radar, hyperspectral, and infrared data, and are used for ecological environment monitoring, land spatial planning, agriculture, forestry and water conservancy, and land use surveys and supervision.

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