CN115063113A - Image quality evaluation method and device for remote sensing image - Google Patents
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Abstract
The invention provides an image quality evaluation method and device of a remote sensing image, which relate to the technical field of data processing and comprise the following steps: acquiring remote sensing image data to be evaluated; subjective evaluation and objective evaluation are respectively carried out on the remote sensing image data to be evaluated to obtain a subjective evaluation result and an objective evaluation result; auditing the subjective evaluation result, and carrying out confidence coefficient analysis on the subjective evaluation result and the objective evaluation result under the condition that the auditing is passed to obtain an analysis result; if the analysis result is within the preset confidence level space range, the subjective evaluation result and the objective evaluation result are stored in the knowledge base, and the knowledge base is used for training and examining the analysts, so that the technical problem that the evaluation accuracy of the existing image quality evaluation method for the remote sensing image is low is solved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a device for evaluating image quality of a remote sensing image.
Background
With the rapid development of aerospace remote sensing and computer technology, satellite remote sensing images are more and more widely applied, and play an important role in aspects such as military investigation, geodetic survey, mineral prospecting, agricultural investigation and the like. The acquisition and interpretation of high-quality remote sensing data become the key point of academic research, so that many new remote sensing products and image processing methods are generated, and the quality evaluation of remote sensing images is paid more and more attention. The method has the advantages that the obtained satellite remote sensing data are reasonably and correctly evaluated, the earlier-stage work is objectively, qualitatively and quantitatively summarized, and an instructive suggestion is provided for the next-stage work, so that the social and economic benefits are fully exerted.
The existing image quality evaluation method has two modes of subjective evaluation and objective evaluation. However, the accuracy of the evaluation results of the above two evaluation methods is low.
No effective solution has been proposed to the above problems.
Disclosure of Invention
In view of the above, the present invention provides a method and a device for evaluating image quality of a remote sensing image, so as to alleviate the technical problem of low evaluation accuracy of the existing method for evaluating image quality of a remote sensing image.
In a first aspect, an embodiment of the present invention provides an image quality evaluation method for a remote sensing image, including: obtaining remote sensing image data to be evaluated; subjective evaluation and objective evaluation are respectively carried out on the remote sensing image data to be evaluated to obtain a subjective evaluation result and an objective evaluation result; auditing the subjective evaluation result, and carrying out confidence degree analysis on the subjective evaluation result and the objective evaluation result under the condition that the auditing is passed to obtain an analysis result; and if the analysis result is within a preset confidence level space range, storing the subjective evaluation result and the objective evaluation result into a knowledge base, and training and examining the analysts by utilizing the knowledge base.
Further, the method for obtaining the subjective evaluation result and the objective evaluation result by respectively carrying out subjective evaluation and objective evaluation on the remote sensing image data to be evaluated comprises the following steps: subjective evaluation is carried out on the remote sensing image data to be evaluated through an analyst to obtain the subjective evaluation result, wherein the subjective evaluation result comprises: marking content, criterion information and image subjective rating information; and performing objective evaluation on the remote sensing image data to be evaluated by using an objective evaluation algorithm model to obtain an objective evaluation result, wherein the objective evaluation result comprises the following steps: and (4) objectively grading information of the image.
Further, the objective evaluation of the remote sensing image data to be evaluated is performed by using an objective evaluation algorithm model to obtain the objective evaluation result, and the method comprises the following steps: analyzing a source file of the remote sensing image data to be evaluated to obtain an image parameter of the remote sensing image data to be evaluated, wherein the image parameter comprises: shooting date, shooting time, observation height, detection distance, longitude and latitude corresponding to the remote sensing image data to be evaluated and altitude corresponding to the remote sensing image data to be evaluated; inputting the image parameters and the target parameters into the objective evaluation algorithm model to obtain the objective evaluation result, wherein the target parameters comprise: observation mode, minimum wavelength value, maximum wavelength value, target background reflectivity, detector pixel size, focal length, noise equivalent brightness and visibility.
Further, if the number of the analysts is 1, performing confidence analysis on the subjective evaluation result and the objective evaluation result to obtain an analysis result, including: and carrying out confidence degree analysis on the subjective rating information and the objective rating information of the image to obtain a first analysis result.
Further, if the number of the analysts is multiple, performing confidence analysis on the subjective evaluation result and the objective evaluation result to obtain an analysis result, including: setting corresponding weight values for a plurality of analysts, and calculating the rating deviation of each analyst based on the weight values and the subjective image rating information corresponding to the analysts; if the rating deviation of the multiple analysts is smaller than a preset threshold value, calculating target evaluation information based on the weighted values and the image subjective rating information corresponding to the multiple analysts; and carrying out confidence degree analysis on the target rating information and the image objective rating information to obtain a second analysis result.
Further, the rating deviation is calculated by the formula
Wherein, F n Rating deviation for the nth analyst, A n And the image subjective rating information is the image subjective rating information corresponding to the nth analyst, and N is the number of the analysts.
Further, if the analysis result is not in the preset confidence coefficient space range, performing optimization iteration on the objective evaluation algorithm model.
In a second aspect, an embodiment of the present invention further provides an apparatus for evaluating image quality of a remote sensing image, including: the system comprises an acquisition unit, an evaluation unit, an auditing unit and a release unit, wherein the acquisition unit is used for acquiring remote sensing image data to be evaluated; the evaluation unit is used for respectively carrying out subjective evaluation and objective evaluation on the remote sensing image data to be evaluated to obtain a subjective evaluation result and an objective evaluation result; the auditing unit is used for auditing the subjective evaluation result and analyzing the confidence degrees of the subjective evaluation result and the objective evaluation result to obtain an analysis result under the condition that the auditing is passed; and the issuing unit is used for storing the subjective evaluation result and the objective evaluation result into a knowledge base and training and evaluating the analysts by using the knowledge base under the condition that the analysis result is within a preset confidence level space range.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory and a processor, where the memory is used to store a program that supports the processor to execute the method in the first aspect, and the processor is configured to execute the program stored in the memory.
In a fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored.
In the embodiment of the invention, remote sensing image data to be evaluated are obtained; subjective evaluation and objective evaluation are respectively carried out on the remote sensing image data to be evaluated to obtain a subjective evaluation result and an objective evaluation result; auditing the subjective evaluation result, and carrying out confidence degree analysis on the subjective evaluation result and the objective evaluation result under the condition that the auditing is passed to obtain an analysis result; if the analysis result is within the preset confidence level space range, the subjective evaluation result and the objective evaluation result are stored in a knowledge base, and the knowledge base is used for training and examining the analysts, so that the aim of reasonably and correctly evaluating the quality of the remote sensing image is fulfilled, the technical problem of low evaluation accuracy of the conventional image quality evaluation method of the remote sensing image is further solved, and the technical effect of improving the accuracy of the image quality evaluation of the remote sensing image is realized.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of an image quality evaluation method for a remote sensing image according to an embodiment of the present invention;
fig. 2 is a schematic diagram of an image quality evaluation apparatus for remote sensing images according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The existing image quality evaluation method has two modes of subjective evaluation and objective evaluation.
Subjective evaluation is that human eyes directly observe images, because a human is an end user of an image, and it is the most objective evaluation to evaluate the quality of an image through human subjective perception. This method can reflect the visual perception of the observer, but is susceptible to a variety of factors, such as the background in which the target is located, the experience of the observer, visual fatigue, and boredom, among others. The objective evaluation is to express the human perception of the image based on a series of physical indexes or mathematical models which can be quantitatively analyzed, and the method is easy to operate and engineer and has the defect that the perception level of the image or the execution condition of a user task is difficult to accurately reflect.
In view of the above disadvantages, the present application proposes the following embodiments.
The first embodiment is as follows:
in accordance with an embodiment of the present invention, there is provided an embodiment of a method for evaluating image quality of a remote sensing image, wherein the steps illustrated in the flowchart of the drawings may be implemented in a computer system, such as a set of computer executable instructions, and wherein although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than that illustrated.
Fig. 1 is a flowchart of an image quality evaluation method for remote sensing images according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, obtaining remote sensing image data to be evaluated;
it should be noted that the data type of the remote sensing image data to be evaluated may be visible light, infrared, SAR, and multispectral; the data format of the remote sensing image data to be evaluated can be tif, img, jpeg, bmp, psd and hdr.
Step S104, performing subjective evaluation and objective evaluation on the remote sensing image data to be evaluated respectively to obtain a subjective evaluation result and an objective evaluation result;
step S106, auditing the subjective evaluation result, and carrying out confidence analysis on the subjective evaluation result and the objective evaluation result under the condition that the auditing is passed to obtain an analysis result;
and S108, if the analysis result is within a preset confidence level space range, storing the subjective evaluation result and the objective evaluation result into a knowledge base, and training and assessing the analysts by using the knowledge base.
In the embodiment of the invention, remote sensing image data to be evaluated are obtained; subjective evaluation and objective evaluation are respectively carried out on the remote sensing image data to be evaluated to obtain a subjective evaluation result and an objective evaluation result; auditing the subjective evaluation result, and carrying out confidence degree analysis on the subjective evaluation result and the objective evaluation result under the condition that the auditing is passed to obtain an analysis result; if the analysis result is within the preset confidence level space range, the subjective evaluation result and the objective evaluation result are stored in a knowledge base, and the knowledge base is used for training and examining the analysts, so that the aim of reasonably and correctly evaluating the quality of the remote sensing image is fulfilled, the technical problem of low evaluation accuracy of the conventional image quality evaluation method of the remote sensing image is further solved, and the technical effect of improving the accuracy of the image quality evaluation of the remote sensing image is realized.
In the embodiment of the present invention, step S104 includes the following steps:
step S11, carrying out subjective evaluation on the remote sensing image data to be evaluated through an analyst to obtain the subjective evaluation result, wherein the subjective evaluation result comprises: marking content, criterion information and image subjective rating information;
step S12, performing objective evaluation on the remote sensing image data to be evaluated by using an objective evaluation algorithm model to obtain the objective evaluation result, wherein the objective evaluation result comprises: and (4) objectively grading information of the image.
In the embodiment of the present invention, the number of the analysts may be 1 or more.
If the number of the analysts is 1, carrying out grading judgment task processing by the corresponding analysts; if the multi-person hierarchical judgment is carried out, firstly, a hierarchical judgment group is constructed, then, each group member is endowed with weight (aiming at carrying out scientific configuration according to the service background and professional skills of different members and improving the rationality of subjective judgment), processing tasks are respectively executed in the form of the judgment group, and after all the tasks are finished, the judgment group is used as a unit to uniformly submit a rating result.
The detailed process of subjective evaluation is as follows: and (4) observing the image by an analyst, searching a target ground object on the remote sensing image data to be evaluated, and labeling the target ground object which can be identified on the image. If a car target is found on the image, the marking line is pulled out by taking the target position as a starting point, and the marking character 'car' (namely, marking content) is added at the tail end of the marking line. After the annotation content is added, according to different task requirements, the corresponding criterion information is added according to NIIRS grading standards, for example, in six types of tasks of visible light NIIRS grading standards (including a task of reconnaissance of an air force target, a task of reconnaissance of an electronic target, a task of reconnaissance of an army target, a task of reconnaissance of a missile target, a task of reconnaissance of a navy target and a task of reconnaissance of cultural facilities), the criterion information description (namely the criterion information) of the recognizable cultural facilities such as a car and a van type station wagon is described, and if the corresponding NIIRS grade is 6, the interpretation grade of the image is 6 (namely the subjective image grading information).
The detailed procedure for objective evaluation is as follows: analyzing a source file of the remote sensing image data to be evaluated to obtain image parameters of the remote sensing image data to be evaluated, wherein the image parameters comprise: shooting date, shooting time, observation height, detection distance, longitude and latitude corresponding to the remote sensing image data to be evaluated and altitude corresponding to the remote sensing image data to be evaluated.
Inputting the image parameters and the target parameters into an objective evaluation algorithm model to obtain an objective evaluation result, wherein the target parameters comprise: observation mode, minimum wavelength value, maximum wavelength value, target background reflectivity, detector pixel size, focal length, noise equivalent brightness and visibility.
In the embodiment of the present invention, step S106 includes the following steps:
if the number of the analysts is 1, performing confidence degree analysis on the subjective evaluation result and the objective evaluation result to obtain an analysis result, including:
and carrying out confidence degree analysis on the subjective rating information and the objective rating information of the image to obtain a first analysis result.
If the number of the analysts is multiple, performing confidence degree analysis on the subjective evaluation result and the objective evaluation result to obtain an analysis result, including:
setting corresponding weight values for a plurality of analysts, and calculating the rating deviation of each analyst based on the weight values and the subjective image rating information corresponding to the analysts;
if the rating deviation of the multiple analysts is smaller than a preset threshold value, calculating target evaluation information based on the weighted values and the image subjective rating information corresponding to the multiple analysts;
and carrying out confidence degree analysis on the target rating information and the image objective rating information to obtain a second analysis result.
In the embodiment of the invention, if the number of the analysts is 1, the auditor audits the marked content, the criterion information and the image rating information, if the marked content is correct, for example, the character of the marked content is 'car', the marked content on the image is a car target, and the given criterion information is matched with the marked content, the audit is passed; if the marked content, the criterion information and the given grade are inconsistent, the marked content, the criterion information and the given grade do not pass, and the auditor carries out control by referring to the NIIRS grading standard, refusing the single subjective evaluation result, judging again and giving the refusing reason.
If the number of the analysts is multiple, firstly, corresponding weight values are set for the analysts, and whether subjective evaluation results of the analysts meet a preset audit standard or not is judged.
If the subjective evaluation results of a plurality of analysts all accord with the preset auditing standard, calculating the grading deviation corresponding to each analyst by using a calculation formula of the grading deviation and the weighted value, wherein the calculation formula of the grading deviation is
Wherein, F n Rating deviation for the nth analyst, A n And the image subjective rating information is the image subjective rating information corresponding to the nth analyst, and N is the number of the analysts.
And if the rating standard deviation of the multiple analysts is more than or equal to 1, rejecting the subjective analysis results of the multiple analysts, and subjectively evaluating the task again.
And if the rating standard deviation of a plurality of analysts is less than 1, performing subjective evaluation on the remote sensing image data to be evaluated again by the analysts with excessive rating deviation in the rejection determination group and giving rejection reasons.
After the subjective image rating information of all the analysts is obtained, the sum of products of the subjective image rating information corresponding to the analysts and the corresponding weight values is calculated, and target evaluation information is obtained.
And finally, carrying out confidence degree analysis on the single subjective image rating information and the image objective rating information, or carrying out confidence degree analysis on the target evaluation information and the image objective rating information.
Specifically, it is determined whether the probability that the difference between the single subjective image rating information and the image objective rating information or the difference between the target evaluation information and the image objective rating information is [ -0.5, 0.5] is 95%.
If the level difference between the two is in the confidence coefficient space range of 95% [ -0.5, 0.5], the image interpretation degree grading effect is reasonable and correct;
if the difference between the two is not in the confidence coefficient space range of 95% -0.5, 0.5], the method adjusts the parameters of the objective evaluation algorithm model and optimizes the algorithm, thereby gradually improving the evaluation precision of the image quality evaluation of the remote sensing image and simultaneously providing support and suggestion for the planning design of the image acquisition flight mission in advance.
In an embodiment of the present invention, the method further includes the steps of:
and storing the subjective evaluation result and the objective evaluation result into a knowledge base, and training and assessing the analysts in the knowledge base by using the subjective evaluation result and the objective evaluation result.
The following explains the method by taking the cultural tif remote sensing image data and 4 analysts as an example:
importing a set of cultural tif remote sensing image data compression package comprising image data and a source file;
and carrying out four-person subjective evaluation on the imported data.
Subjective evaluation tasks are distributed to an analyst 1, an analyst 2, an analyst 3 and an analyst 4, because the training and assessment results of the analyst 1 are excellent and the professional skills are tight, the analyst 1 is given a weight of 40%, and the rest of the analysts are given weights of 20% respectively to form a four-person judgment group for image interpretation grading judgment;
when each analyst carries out subjective evaluation, firstly, visual interpretation is carried out on an image according to NIIRS (network information identification) classification standards of a specific task industry, target marking is carried out on the image, corresponding criterion information is selected according to the classification standards of different industries, the corresponding interpretation degree grades (0-9 grades, 10 grades in total) of the image are given, and after the objective evaluation is finished, the subjective evaluation results of individuals are submitted;
after the execution of all four people is finished, carrying out comprehensive evaluation according to the weights of the four people and the given rating information of the viewed image to obtain target evaluation information of the image, wherein a specific calculation formula is as follows:
NIIRS＝A 1 *Q 1 +A 2 *Q 2 +A 3 *Q 3 +A 4 *Q 4
in the formula A n Representing the value of the degree of interpretation given by the nth analyst in the decision group, Q n Representing the weight given to the nth analyst in the judgment group, wherein n is 4 in the example, NIIRS represents the target evaluation information finally obtained by the judgment of multiple persons, and two decimal places are reserved as the result;
analyzing the remote sensing image source file, calling an image quality equation, carrying out image interpretation degree evaluation, and carrying out objective evaluation on the image interpretation degree by using the revised and verified image quality equation. The GIQE model applicable to visible light images is:
NIIRS＝10.251-alog 10 GSD GM +blog 10 RER GM -0.656H GM -0.334(G/SNR)
in the formula RER GM Is the geometric mean of normalized Relative Edge Response (RER), GSD GM Is the geometric mean (in inches) of the ground sample distance, H GM Compensating for geometric height overshoot caused by a Modulation Transfer Function (MTFC), G is noise gain caused by the MTFC, and SNR is signal-to-noise ratio;a and b are constants, and classified values are taken according to the distribution condition of normalized Relative Edge Response (RER), and NIIRS is image objective rating information.
And auditing the subjective evaluation result and the objective evaluation result.
When the rating standard deviation of all members of the judgment group is more than or equal to 1, the whole multi-person judgment task is automatically rejected, and the task is redistributed for grading judgment;
when the rating standard deviation of all the members of the judgment group is less than 1, auditing personnel audit the marked content, the criterion information, the image rating information and the rating deviation of each analyst of the judgment group, refusing the task of the member with the overlarge rating deviation in the judgment group, giving the refusing reason, carrying out the judgment again by the analyst, enabling the whole auditing task to be in a waiting state until the refused grading judgment task is reprocessed and submitted by the analyst, activating the auditing list again and entering a to-be-audited state, and carrying out a fourth step after the auditing personnel reexamine the grading judgment information of all the analysts to reach the standard;
performing confidence analysis based on the target evaluation information and the image objective rating information, and judging whether the range of the range is not more than level 1, namely in a confidence spatial range of 95% [ -0.5, 0.5], wherein the step needs to be based on a large amount of sample data accumulation;
if the subjective evaluation result and the objective evaluation result do not exceed the first level, the subjective evaluation result and the objective evaluation result are published by adopting a self-research PIE-Map geographic information system through auditing, the tif file to be published is uploaded to the PIE-Map geographic information system through an FTP (file transfer protocol), the PIE-Map geographic information system publishes a new subjective evaluation result and an objective evaluation result in real time through scanning a file path at regular time, and after the publication is finished, a user can browse and look up the subjective evaluation result and the objective evaluation result of the image in a knowledge base, so that the achievement reuse of professionals in the field is facilitated;
training and examining of analysts are carried out on the basis of a shared knowledge base, professional image interpretation degree grading judgment staff are trained, and training mechanisms of the analysts comprise random practice, special practice, online examination and skill ranking.
Example two:
the embodiment of the present invention further provides an image quality evaluation device for a remote sensing image, where the image quality evaluation device for a remote sensing image is used to execute the image quality evaluation method for a remote sensing image provided in the foregoing content of the embodiment of the present invention, and the following is a specific description of the image quality evaluation device for a remote sensing image provided in the embodiment of the present invention.
As shown in fig. 2, fig. 2 is a schematic view of the image quality evaluation device for remote sensing images, which includes: the system comprises an acquisition unit 10, an evaluation unit 20, an auditing unit 30 and a publishing unit 40.
The acquiring unit 10 is used for acquiring remote sensing image data to be evaluated;
the evaluation unit 20 is configured to perform subjective evaluation and objective evaluation on the remote sensing image data to be evaluated respectively to obtain a subjective evaluation result and an objective evaluation result;
the review unit 30 is configured to review the subjective evaluation result, and perform confidence analysis on the subjective evaluation result and the objective evaluation result to obtain an analysis result when the review is passed;
the issuing unit 40 is configured to, when the analysis result is within a preset confidence level spatial range, store the subjective evaluation result and the objective evaluation result in a knowledge base, and train and examine the analyst by using the knowledge base.
In the embodiment of the invention, remote sensing image data to be evaluated are obtained; subjective evaluation and objective evaluation are respectively carried out on the remote sensing image data to be evaluated to obtain a subjective evaluation result and an objective evaluation result; auditing the subjective evaluation result, and carrying out confidence degree analysis on the subjective evaluation result and the objective evaluation result under the condition that the auditing is passed to obtain an analysis result; if the analysis result is within the preset confidence level space range, the subjective evaluation result and the objective evaluation result are stored in a knowledge base, and the knowledge base is used for training and examining the analysts, so that the aim of reasonably and correctly evaluating the quality of the remote sensing image is fulfilled, the technical problem of low evaluation accuracy of the conventional image quality evaluation method of the remote sensing image is further solved, and the technical effect of improving the accuracy of the image quality evaluation of the remote sensing image is realized.
Example three:
the embodiment of the present invention further provides an electronic device, which includes a memory and a processor, where the memory is used to store a program that supports the processor to execute the method in the first embodiment, and the processor is configured to execute the program stored in the memory.
Referring to fig. 3, an embodiment of the present invention further provides an electronic device 100, including: the processor 50, the memory 51, the bus 52 and the communication interface 53, wherein the processor 50, the communication interface 53 and the memory 51 are connected through the bus 52; the processor 50 is arranged to execute executable modules, such as computer programs, stored in the memory 51.
The Memory 51 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 53 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The bus 52 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 3, but this does not indicate only one bus or one type of bus.
The memory 51 is used for storing a program, and the processor 50 executes the program after receiving an execution instruction, and the method executed by the apparatus defined by the flow process disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 50, or implemented by the processor 50.
The processor 50 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 50. The Processor 50 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in the memory 51, and the processor 50 reads the information in the memory 51 and completes the steps of the method in combination with the hardware thereof.
Example four:
the embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program executes the steps of the method in the first embodiment.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of a unit is merely a division of one logic function, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that the following descriptions are only illustrative and not restrictive, and that the scope of the present invention is not limited to the above embodiments: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. An image quality evaluation method of a remote sensing image is characterized by comprising the following steps:
obtaining remote sensing image data to be evaluated;
subjective evaluation and objective evaluation are respectively carried out on the remote sensing image data to be evaluated to obtain a subjective evaluation result and an objective evaluation result;
auditing the subjective evaluation result, and carrying out confidence degree analysis on the subjective evaluation result and the objective evaluation result under the condition that the auditing is passed to obtain an analysis result;
and if the analysis result is within a preset confidence level space range, storing the subjective evaluation result and the objective evaluation result into a knowledge base, and training and assessing the analysts by utilizing the knowledge base.
2. The method according to claim 1, wherein the subjective evaluation and the objective evaluation are respectively performed on the remote sensing image data to be evaluated to obtain a subjective evaluation result and an objective evaluation result, and the method comprises the following steps:
subjective evaluation is carried out on the remote sensing image data to be evaluated through an analyst to obtain the subjective evaluation result, wherein the subjective evaluation result comprises: marking content, criterion information and image subjective rating information;
and performing objective evaluation on the remote sensing image data to be evaluated by using an objective evaluation algorithm model to obtain an objective evaluation result, wherein the objective evaluation result comprises the following steps: and (4) image objective rating information.
3. The method according to claim 2, wherein the objective evaluation of the remote sensing image data to be evaluated is performed by using an objective evaluation algorithm model to obtain the objective evaluation result, and the method comprises the following steps:
analyzing the source file of the remote sensing image data to be evaluated to obtain the image parameters of the remote sensing image data to be evaluated, wherein the image parameters comprise: shooting date, shooting time, observation height, detection distance, longitude and latitude corresponding to the remote sensing image data to be evaluated and altitude corresponding to the remote sensing image data to be evaluated;
inputting the image parameters and the target parameters into the objective evaluation algorithm model to obtain the objective evaluation result, wherein the target parameters comprise: observation mode, minimum wavelength value, maximum wavelength value, target background reflectivity, detector pixel size, focal length, noise equivalent brightness and visibility.
4. The method according to claim 2, wherein if the number of analysts is 1, performing confidence analysis on the subjective evaluation result and the objective evaluation result to obtain an analysis result, comprising:
and carrying out confidence degree analysis on the subjective rating information and the objective rating information of the image to obtain a first analysis result.
5. The method according to claim 2, wherein if the number of the analysts is multiple, performing confidence analysis on the subjective evaluation result and the objective evaluation result to obtain an analysis result, comprising:
setting corresponding weight values for a plurality of analysts, and calculating the rating deviation of each analyst based on the weight values and the subjective image rating information corresponding to the analysts;
if the rating deviation of the multiple analysts is smaller than a preset threshold value, calculating target evaluation information based on the weighted values and the image subjective rating information corresponding to the multiple analysts;
and carrying out confidence degree analysis on the target rating information and the image objective rating information to obtain a second analysis result.
6. The method of claim 5,
the rating deviation is calculated by the formula
Wherein, F n Rating deviation for the nth analyst, A n And the image subjective rating information is the image subjective rating information corresponding to the nth analyst, and N is the number of the analysts.
7. The method of claim 2, further comprising:
and if the analysis result is not in the preset confidence coefficient space range, performing optimization iteration on the objective evaluation algorithm model.
8. An image quality evaluation device for a remote sensing image, comprising: an acquisition unit, an evaluation unit, an auditing unit and a release unit, wherein,
the acquisition unit is used for acquiring remote sensing image data to be evaluated;
the evaluation unit is used for respectively carrying out subjective evaluation and objective evaluation on the remote sensing image data to be evaluated to obtain a subjective evaluation result and an objective evaluation result;
the auditing unit is used for auditing the subjective evaluation result and analyzing the confidence degrees of the subjective evaluation result and the objective evaluation result to obtain an analysis result under the condition that the auditing is passed;
and the issuing unit is used for storing the subjective evaluation result and the objective evaluation result into a knowledge base and training and assessing the analysts by utilizing the knowledge base under the condition that the analysis result is within a preset credibility space range.
9. An electronic device comprising a memory for storing a program that enables a processor to perform the method of any of claims 1 to 7 and a processor configured to execute the program stored in the memory.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of the claims 1 to 7.
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| CN115797362A (en) * | 2023-02-13 | 2023-03-14 | 航天宏图信息技术股份有限公司 | Quality evaluation method and device for high-resolution remote sensing image and electronic equipment |
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