Satellite image positioning accuracy detection method, device, equipment and storage medium

Abstract

The present disclosure relates to the field of satellite data processing technology, and specifically to a method, apparatus, device, and storage medium for detecting the positioning accuracy of satellite images. The method comprises: obtaining a first target satellite image to be detected and n first reference high-resolution images that match it, and performing normalization processing and radiation enhancement on them to obtain a second target satellite image and n second reference high-resolution images; cropping the second target satellite image into n third target satellite images corresponding to the positions of the n second reference high-resolution images; converting the second reference high-resolution image into a third reference high-resolution image with the same image size and resolution as the corresponding third target satellite image; and using a preset optical flow algorithm to calculate the pixel offset of each pixel from the third reference high-resolution image to the third target satellite image as the positioning accuracy result. This technical solution can efficiently and accurately detect the positioning accuracy of satellite images.

Description

Technical Field

[0001] The present disclosure relates to the field of satellite data processing technology, and in particular to a method, device, equipment and storage medium for detecting positioning accuracy of satellite images. Background Art

[0002] Satellite image positioning refers to determining the geographic location corresponding to the satellite image observed by the satellite. The higher the positioning accuracy of the satellite image, the more accurate the true geographic location corresponding to the satellite image, and the more effective the subsequent application of satellite imagery in various fields. Therefore, the positioning accuracy of satellite images is one of the important indicators of satellite imagery. Traditional positioning accuracy verification methods mainly involve manually extracting and matching features of the satellite image to be tested with reference images with geographic location tags in a database, and then determining the positioning accuracy of the satellite image to be tested based on the matching results. This process is relatively cumbersome and not only fails to meet the growing demand for accuracy, but also seems to be inadequate in terms of processing efficiency and automation. Summary of the Invention

[0003] In order to solve the problems in the related art, the embodiments of the present disclosure provide a method, apparatus, device and storage medium for detecting the positioning accuracy of satellite images.

[0004] In a first aspect, an embodiment of the present disclosure provides a method for detecting positioning accuracy of satellite images, comprising:

[0005] Acquire a first target satellite image to be detected and acquire n first reference high-resolution images that match the first target satellite image, where n is an integer greater than or equal to 1;

[0006] performing standardization processing and radiation enhancement on the first target satellite image and the n first reference high-resolution images to obtain a second target satellite image and n second reference high-resolution images;

[0007] According to the position range of the n second reference high-resolution images, the second target satellite image is cropped into n third target satellite images corresponding to the positions of the n second reference high-resolution images;

[0008] For each second reference high-resolution image and its corresponding third target satellite image, convert the second reference high-resolution image into a third reference high-resolution image having the same image size and resolution as the corresponding third target satellite image;

[0009] A preset optical flow algorithm is used to calculate the pixel offset of each pixel from the third reference high-resolution image to the third target satellite image as a positioning accuracy result.

[0010] In a possible implementation, obtaining a satellite image of a first target to be detected includes:

[0011] Obtain the original satellite image to be detected and its image channel and calibration coefficient;

[0012] Based on the calibration coefficient and the image channel, calibration calculation is performed on the original satellite image to be detected to obtain a target satellite image;

[0013] Cloud detection is performed on the target satellite image, and the target satellite image whose cloud occlusion ratio exceeds a predetermined threshold is used as the first target satellite image to be detected.

[0014] In a possible implementation, obtaining n first reference high-resolution images that match the first target satellite image includes:

[0015] Based on the first target satellite image, searching from a pre-stored high-resolution image library for n high-resolution images taken by a predetermined satellite, located within the position range of the first target satellite image, having the same image channel as the first target satellite image, having the highest image accuracy level, and being taken at a time closest to that of the first target satellite image as reference high-resolution images;

[0016] The high-resolution image library records a plurality of high-resolution images and their corresponding shooting satellites, shooting times, location ranges, image channels, and positioning accuracy levels of the images.

[0017] In one possible implementation, the method further includes:

[0018] The high-resolution image database is updated by downloading the high-resolution image regularly.

[0019] In a possible implementation, the using an optical flow algorithm to calculate the pixel offset from the third reference high-resolution image to the third target satellite image as a positioning accuracy result includes:

[0020] If the resolution of the third target satellite image is greater than or equal to the preset resolution, an optical flow algorithm is used to calculate the pixel offset from the third reference high-resolution image to the third target satellite image as a positioning accuracy result;

[0021] The method further comprises:

[0022] If the resolution of the third target satellite image is less than the preset resolution, performing resolution upscaling processing on the third target satellite image and the third reference high-resolution image to obtain a fourth reference high-resolution image and the fourth target satellite image;

[0023] An optical flow algorithm is used to calculate the pixel offset from the fourth reference high-resolution image to the fourth target satellite image as a positioning accuracy result.

[0024] In a possible implementation, the using an optical flow algorithm to calculate the pixel offset from the fourth reference high-resolution image to the fourth target satellite image as a positioning accuracy result includes:

[0025] The fourth reference high-resolution image is used as the starting image, and the fourth target satellite image is used as the intermediate image and the ending image. The optical flow calculation model is executed to obtain the forward optical flow vector from the fourth reference high-resolution image to the fourth target satellite image. and the backward optical flow vector from the fourth target satellite image to the fourth target satellite image Based on the forward optical flow vector and the backward optical flow vector Calculate the final optical flow vector F = (F u , F v ) as the positioning accuracy result;

[0026] in, is the lateral optical flow in the forward optical flow vector, is the longitudinal optical flow in the forward optical flow vector, is the lateral optical flow in the backward optical flow vector, is the longitudinal optical flow in the backward optical flow vector, and m is the resolution multiple of the fourth target satellite image relative to the third target satellite image.

[0027] In one possible implementation, the method further includes:

[0028] Obtaining pixel offsets of a preset control point set from the pixel offsets of each pixel;

[0029] Based on the pixel offset of the control point set, a random sampling consistency detection algorithm is used to eliminate abnormal points in the control point set, and the pixel offset of normal control points is obtained as the final positioning accuracy result.

[0030] In a second aspect, an embodiment of the present disclosure provides a device for detecting positioning accuracy of satellite images, comprising:

[0031] an image acquisition module configured to acquire a first target satellite image to be detected and acquire n first reference high-resolution images matching the first target satellite image, where n is an integer greater than or equal to 1;

[0032] a preprocessing module configured to perform standardization processing and radiation enhancement on the first target satellite image and the n first reference high-resolution images to obtain a second target satellite image and n second reference high-resolution images;

[0033] a cropping module configured to crop the second target satellite image into n third target satellite images corresponding to the positions of the n second reference high-resolution images according to the position range of the n second reference high-resolution images;

[0034] A conversion module is configured to convert, for each second reference high-resolution image and its corresponding third target satellite image, the second reference high-resolution image into a third reference high-resolution image having the same image size and resolution as the corresponding third target satellite image;

[0035] The positioning analysis module is configured to use a preset optical flow algorithm to calculate the pixel offset of each pixel from the third reference high-resolution image to the third target satellite image as a positioning accuracy result.

[0036] In a third aspect, an embodiment of the present disclosure provides an electronic device comprising a memory and a processor, wherein the memory is used to store one or more computer instructions, and wherein the one or more computer instructions are executed by the processor to implement a method as described in any one of the first aspects.

[0037] In a fourth aspect, an embodiment of the present disclosure provides a computer-readable storage medium having computer instructions stored thereon, which, when executed by a processor, implement the method as described in any one of the first aspects.

[0038] The technical solutions provided by the embodiments of the present disclosure have the following technical effects:

[0039] 1. Significantly improve detection accuracy

[0040] The present disclosure adopts high-resolution images as high-precision reference data. High-resolution images not only have extremely high spatial resolution, but also are rich in ground object information. The rich details of these high-resolution images provide more reference points for positioning analysis, and provide an accurate and reliable data basis for subsequent operations such as feature matching and positioning analysis. At the same time, combined with the feature matching-based optical flow method, it can more accurately capture the subtle feature changes between the reference image and the image to be detected, and also has strong dynamic tracking capabilities, which can track the changes of these feature points in real time. Even when there are scale differences or slight deformations between the images, it can maintain a high matching accuracy, thereby improving the accuracy of the positioning detection results.

[0041] In addition, the traditional image library used for reference has a long update cycle, which makes it difficult to capture and reflect the immediate changes of landmarks, thus limiting its timeliness and accuracy. In contrast, the high-resolution image library disclosed in the present invention can be updated regularly, realizing a near real-time update mechanism, which can closely track the passage of time and subtle or significant changes in the appearance of landmarks. This dynamic update feature ensures that the present invention can match the most appropriate and accurate reference high-resolution image to the satellite image to be detected. Its immediacy and sensitivity provide crucial support for the precise positioning analysis of satellite images, greatly improving the user experience and information value. Moreover, the present invention innovatively uses an optical flow algorithm to calculate the pixel offset of each pixel from the reference high-resolution image to the satellite image to be detected as the positioning accuracy result, which can accurately capture the position offset between the reference image and the image to be detected, and ensure the accuracy of the positioning detection results.

[0042] 2. Significantly improved processing efficiency

[0043] The present invention automates the entire positioning accuracy analysis process from data processing to feature matching. In particular, the introduction of the optical flow algorithm greatly reduces the need for human intervention and improves processing efficiency. At the same time, automated processing also reduces the possibility of human error and improves the accuracy and reliability of positioning detection.

[0044] 3. Strong adaptability and generalization capabilities

[0045] The optical flow algorithm used in the present disclosure can automatically learn and optimize parameters from a large amount of data through a self-supervised learning mechanism without the need for manual labeling, and therefore has strong adaptability and generalization capabilities.

[0046] 4. Excellent robustness and reliability

[0047] The optical flow algorithm used in this paper estimates optical flow by combining sequential information from multiple frames. This spatiotemporal analysis method performs well in complex situations such as occlusion and motion blur. Furthermore, the robustness of deep learning algorithms enables the model to maintain stable performance even in the presence of noisy or outlier data.

[0048] 5. Wide applicability

[0049] The present disclosure offers exceptional flexibility in the selection of high-resolution imagery for reference, primarily due to its wide applicability. Specifically, the present disclosure not only demonstrates excellent performance using high-resolution imagery from the Landsat series of satellites as a reference data source, but can also be seamlessly extended to encompass applications using other high-resolution satellite data sources. This flexibility stems from the versatility of the present disclosure's design and its compatibility with multiple data formats.

[0050] The present disclosure also demonstrates strong adaptability for satellite imagery. Regardless of the satellite system, resolution, or sensor used to acquire these images, as long as they contain sufficient ground object information and feature points, the disclosed method can be used to effectively assess positioning accuracy. This broad applicability is due to the use of feature matching optical flow, which automatically extracts and matches key features from complex and variable image data, thereby achieving high-precision positioning analysis.

[0051] Furthermore, the broad applicability of this disclosure is also reflected in its scalability. With the continuous advancement of remote sensing technology and the emergence of new satellite systems, the methods provided by this disclosure can be quickly adapted to new data sources and application scenarios through simple adjustments and optimizations. This capability gives this disclosure enormous potential and value in the future field of remote sensing data processing and analysis.

[0052] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Other features, objectives and advantages of the present disclosure will become more apparent through the following detailed description of non-limiting embodiments in conjunction with the accompanying drawings. In the accompanying drawings:

[0054] Figure 1 A flowchart of a method for detecting positioning accuracy of satellite images provided by an embodiment of the present disclosure is shown.

[0055] Figure 2 A structural block diagram of a satellite image positioning accuracy detection device according to an embodiment of the present disclosure is shown.

[0056] Figure 3 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown.

[0057] Figure 4 A schematic diagram showing the structure of a computer system suitable for implementing the method of the embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0058] Hereinafter, exemplary embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that those skilled in the art can easily implement them. In addition, for the sake of clarity, parts not related to the description of the exemplary embodiments are omitted in the accompanying drawings.

[0059] In the present disclosure, it should be understood that terms such as "include" or "have" are intended to indicate the presence of features, numbers, steps, actions, components, parts, or combinations thereof disclosed in the present specification, and are not intended to exclude the possibility that one or more other features, numbers, steps, actions, components, parts, or combinations thereof exist or are added.

[0060] It should also be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present disclosure may be combined with each other. The present disclosure will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0061] The following glossary is explained before the examples:

[0062] High-resolution imagery: high-resolution satellite imagery;

[0063] Pixel: the smallest unit that makes up a satellite image;

[0064] Image channel: used to indicate the color or wavelength of satellite images, such as infrared channel, shortwave channel, visible light channel, etc. Figure 1 FIG. 1 is a flow chart showing a method for detecting the positioning accuracy of satellite images provided by an embodiment of the present disclosure. Figure 1 As shown, the satellite image positioning accuracy detection method includes the following steps S101-S105:

[0065] In step S101 , a first target satellite image to be detected is obtained and n first reference high-resolution images matching the first target satellite image are obtained.

[0066] Here, the first target satellite image to be detected can be taken by any camera sensor carried on any satellite system, such as a geostationary satellite, a polar-orbiting satellite, etc. There is no restriction on the resolution, size, image channel, etc. of the first target satellite image. As long as the satellite image is taken by the camera sensor on the satellite, the positioning accuracy can be detected using the method provided in the present disclosure.

[0067] Here, the first reference high-resolution image refers to a high-resolution satellite image used as a reference for evaluating the positioning accuracy of the first target satellite image. Typically, the resolution of the first target satellite image is low, and one frame of the first target satellite image is usually matched with multiple frames of the first reference high-resolution image. The matching here means that the position range of the continuous area formed by the multiple first reference high-resolution images is the same as the position range of the first target satellite image, and the image channels of the first reference high-resolution image and the first target satellite image are the same. Of course, in order to better evaluate the first target satellite image, the first reference high-resolution image and the first target satellite image should preferably be taken at a similar time. As a reference image for positioning accuracy evaluation, the first reference high-resolution image should have higher positioning accuracy.

[0068] In step S102 , the first target satellite image and the n first reference high-resolution images are both subjected to standardization processing and radiation enhancement to obtain a second target satellite image and n second reference high-resolution images.

[0069] Here, in order to ensure the consistency of the first target satellite image and the first reference high-resolution image, the first target satellite image and the n first reference high-resolution images can be standardized. The standardization process refers to scaling the pixel values ​​of the image proportionally, removing the unit restriction of the pixel values, and converting them into dimensionless pure values, so that the first target satellite images and the first reference high-resolution images of different magnitudes can be compared.

[0070] Here, in order to increase the image contrast of the image data and improve the visual effect of the image, after the standardization processing, the first target satellite image and the n first reference high-resolution images can also be subjected to radiation enhancement (Radiance Enhancement), thereby obtaining the second target satellite image and n second reference high-resolution images.

[0071] In step S103, according to the position range of the n second reference high-resolution images, the second target satellite image is cropped into n third target satellite images corresponding to the positions of the n second reference high-resolution images;

[0072] Here, in order to ensure the spatial consistency and comparability of the second target satellite image and the second reference high-resolution image from different sources, and to provide an accurate and reliable data basis for subsequent operations such as feature matching and positioning analysis, n second reference high-resolution images can be projected into the second target satellite image to obtain n projection areas of the n second reference high-resolution images in the second target satellite image. Then, the second target satellite image is cropped into n third target satellite images corresponding to the positions of the n second reference high-resolution images according to the n projection areas. The n second reference high-resolution images correspond one-to-one with the n third target satellite images, and a same-name point association is established. It should be noted that the position range of the n second reference high-resolution images, such as the longitude and latitude range, can be used to search the second target satellite image for the pixel row and column number range corresponding to the position range of each second reference high-resolution image in the second target satellite image. In this way, n projection areas of the n second reference high-resolution images in the second target satellite image can be obtained.

[0073] In step S104 , for each second reference high-resolution image and its corresponding third target satellite image, the second reference high-resolution image is converted into a third reference high-resolution image having the same image size and resolution as the corresponding third target satellite image.

[0074] Here, the second reference high-resolution image is a high-resolution satellite image, which is usually higher in resolution than the corresponding third target satellite image. In order to better compare, resampling can be performed to convert the second reference high-resolution image into a third reference high-resolution image with the same image size and resolution as the corresponding third target satellite image.

[0075] In step S105 , a preset optical flow algorithm is used to calculate the pixel offset of each pixel from the third reference high-resolution image to the third target satellite image as a positioning accuracy result.

[0076] Here, the optical flow algorithm is a computer vision technology used to estimate the pixel motion between consecutive image frames. The concept of optical flow is based on the following assumption: in a short period of time, the movement of an object will cause changes in the position of pixels in the image, and these changes can be captured by analyzing the image sequence. Therefore, the present disclosure innovatively adopts the optical flow algorithm to calculate the pixel offset of each pixel from the third reference high-resolution image to the third target satellite image as the positioning accuracy result. The pixel offset here can include the horizontal offset and vertical offset of each pixel.

[0077] The open and innovative use of optical flow algorithm to calculate the pixel offset of each pixel from the reference high-resolution image to the satellite image to be detected as the positioning accuracy result can accurately capture the position offset between the reference image and the image to be detected, ensuring the accuracy of the positioning detection results.

[0078] In a possible implementation, obtaining a satellite image of a first target to be detected includes:

[0079] Obtain the original satellite image to be detected and its image channel and calibration coefficient;

[0080] Based on the calibration coefficient and the image channel, calibration calculation is performed on the original satellite image to be detected to obtain a target satellite image;

[0081] Cloud detection is performed on the target satellite image, and the target satellite image whose cloud occlusion ratio exceeds a predetermined threshold is used as the first target satellite image to be detected.

[0082] Calibration coefficients are a set of parameters used to convert raw satellite imagery captured by satellite sensors into actual surface reflectivity or emissivity. These coefficients reflect the characteristics of the sensor and its sensitivity to different wavelengths of light. Calibration of raw satellite imagery corrects for errors caused by factors such as sensor characteristics, solar altitude variations, and atmospheric influences, resulting in more realistic surface physical quantities.

[0083] For example, for the original satellite image of the visible light channel taken by a polar-orbiting satellite, the visible light reflectance can be calculated based on the original brightness value DN1 of the pixel of the original satellite image and the calibration coefficients k0, k1 and k2 of the visible light channel. The calculation formula is: value1 = k0 + k1 × DN1 + k2 × DN1 2Based on the calculated visible light reflectance of each pixel, the target satellite image can be obtained.

[0084] Alternatively, for example, for an original satellite image of the infrared channel taken by a polar-orbiting satellite, the infrared channel radiance can be calculated based on the original pixel brightness value DN2 of the original satellite image and the calibration coefficients k3, k4, and k5 of the infrared channel. The calculation formula is: value2 = k3 + k4 × DN2 + k5 × DN2 2 , based on the calculated infrared channel radiance of each pixel, the target satellite image can be obtained.

[0085] Alternatively, for example, the visible reflectance and infrared channel radiance of a geostationary satellite can also be obtained through a lookup table. Here, a rapid cloud detection can also be performed on the obtained target satellite image. If the cloud obstruction is detected to be less than a predetermined threshold, such as 50%, it indicates that the target satellite image is less obscured by clouds, and positioning accuracy detection can be performed, and the subsequent detection process can continue. If the cloud obstruction is detected to be greater than a predetermined threshold, such as 50%, it indicates that the target satellite image is more obscured by clouds and is not suitable for positioning accuracy detection. The process ends, and at this point, other original satellite images taken by the same sensor on the same satellite can be re-acquired for calibration calculation and cloud detection to obtain the first target satellite image to be detected.

[0086] In a possible implementation, obtaining n first reference high-resolution images that match the target satellite image includes:

[0087] Based on the target satellite image, searching from a pre-stored high-resolution image library for n high-resolution images taken by a predetermined satellite, located within the position range of the target satellite image, taken at a time closest to that of the target satellite image, having the same image channel as that of the target satellite image and having the highest image accuracy level as reference high-resolution images;

[0088] The high-resolution image library records a plurality of high-resolution images and their corresponding shooting satellites, shooting times, location ranges, image channels, and positioning accuracy levels of the images.

[0089] The high-resolution image library stores a large number of high-resolution images. These high-resolution images can be Landsat series (such as Landsat 8 and Landsat 9) or other high-resolution satellite images. These high-resolution images are calibrated satellite images and can be full-channel TIFF (Tagged Image File Format) image data.

[0090] Here, the high-resolution image can be a high-resolution image of the area where the corresponding landmark point is located. The landmark point can be a natural lake area with significant landmark features, such as Qinghai Lake, Namtso Lake, Selin Co Lake and Hulun Lake. These areas not only have distinct landmark features, but also cover a wide range of altitudes and diverse geographical environments. They are ideal test areas and provide rich and challenging test scenarios for the effectiveness and accuracy verification of the detection method provided by the present disclosure. It should be noted here that the first target satellite image to be detected is also a satellite image of the area where the above-mentioned landmark point is located.

[0091] Here, the high-resolution image is used as a reference to detect the positioning accuracy of other satellite images. Therefore, in order to ensure the accuracy of the detection, high-resolution images with a cloud cover ratio less than a predetermined ratio, such as 20%, can be selected to construct the high-resolution image library.

[0092] Here, for ease of management and application, each high-resolution image in the high-resolution image library records the corresponding shooting satellite, shooting time, location range (such as latitude and longitude range), image channel (such as infrared channel, visible light channel, etc.), and image positioning accuracy level (level set according to predetermined rules). In this way, after obtaining the first target satellite image to be detected, n high-resolution images captured by the predetermined satellite, located within the location range of the first target satellite image, with the same image channel as the first target satellite image, the highest image accuracy level, and the closest shooting time to the first target satellite image can be searched from the pre-stored high-resolution image library as reference high-resolution images.

[0093] It should be noted that the predetermined satellite may be set by the inspection personnel. The high-resolution image located within the position range of the first target satellite image may be determined based on the position range of each high-resolution image.

[0094] The high-resolution imagery used in this implementation not only possesses extremely high spatial resolution, clearly showing surface details, but also boasts excellent temporal and spectral resolution, providing multi-dimensional, comprehensive surface information. This rich source of high-resolution imagery data not only broadens the scope of remote sensing applications but also provides solid, reliable data support for the high-precision positioning analysis disclosed herein.

[0095] In one possible implementation, the method further includes:

[0096] The high-resolution image database is updated by downloading the high-resolution image regularly.

[0097] In this embodiment, the high-resolution images in the high-resolution impact library can be manually downloaded from the official. In order to ensure the timely update of the high-resolution images in the high-resolution image library, they can also be downloaded from the official at a scheduled time. For example, full-channel TIFF image data of areas with cloud cover <20% and landmark points can be downloaded once a day.

[0098] This embodiment updates the high-resolution image library by regularly downloading high-resolution images to ensure the freshness of the high-resolution images in the high-resolution image library. This ensures that the first reference high-resolution image that matches the first target satellite image and has the most recent shooting time can be found in the high-resolution impact library, providing crucial support for the precise positioning analysis of satellite images and making subsequent positioning detection results more accurate.

[0099] In a possible implementation, the using an optical flow algorithm to calculate the pixel offset from the third reference high-resolution image to the third target satellite image as a positioning accuracy result includes:

[0100] If the resolution of the third target satellite image is greater than a preset threshold, an optical flow algorithm is used to calculate the pixel offset from the third reference high-resolution image to the third target satellite image as a positioning accuracy result;

[0101] The method further comprises:

[0102] If the resolution of the third target satellite image is less than a preset threshold, performing resolution upscaling processing on the third target satellite image and the third reference high-resolution image to obtain a fourth reference high-resolution image and the fourth target satellite image;

[0103] An optical flow algorithm is used to calculate the pixel offset from the fourth reference high-resolution image to the fourth target satellite image as a positioning accuracy result.

[0104] Here, in order to ensure the consistency of the reference high-resolution image and its corresponding target satellite image, it is necessary to convert the high-resolution second reference high-resolution image into a third reference high-resolution image with the same image size and resolution as its corresponding third target satellite image. If the resolution of the third target satellite image is large, greater than or equal to the preset resolution (such as less than or equal to 500 meters), it can be ensured that the third reference high-resolution image and the third target satellite image have higher spatial details and clearer feature boundaries. At this time, the optical flow algorithm can be directly used to calculate the pixel offset from the third reference high-resolution image to the third target satellite image as the positioning accuracy result. If the resolution of the third target satellite image is small, less than the preset resolution (such as greater than 500 meters), in order to improve the clarity of the geographic feature boundaries in the image and enhance the accuracy of subsequent optical flow matching, it is necessary to perform resolution up-scaling processing on it. For example, a linear interpolation algorithm can be used to perform resolution up-scaling processing on the third reference high-resolution image and its corresponding third target satellite image, doubling their resolution (or other specified multiples). The linear interpolation method estimates the value of the new pixel by considering the values ​​of the surrounding pixels, thereby improving the resolution of the image without introducing too much human error. The images after resolution upgrade (the fourth reference high-resolution image and the fourth target satellite image) will have higher spatial details and clearer feature boundaries, which will be beneficial for subsequent operations such as feature matching and positioning analysis.

[0105] In a possible implementation, the using an optical flow algorithm to calculate the pixel offset from the fourth reference high-resolution image to the fourth target satellite image as a positioning accuracy result includes:

[0106] The fourth reference high-resolution image is used as the starting image, and the fourth target satellite image is used as the intermediate image and the ending image. The optical flow calculation model is executed to obtain the forward optical flow vector from the fourth reference high-resolution image to the fourth target satellite image. and the backward optical flow vector from the fourth target satellite image to the fourth target satellite image Based on the forward optical flow vector and the backward optical flow vector Calculate the final optical flow vector F = (F u , F v ) as the positioning accuracy result;

[0107] Here, the optical flow calculation model is pre-trained using a self-supervised optical flow learning method. The optical flow calculation model can indirectly evaluate changes in positioning accuracy by analyzing changes in surface features between the target satellite image and the high-resolution image. The positioning accuracy of the target satellite image is obtained using the positioning accuracy of the high-resolution image as a reference. For example, the optical flow calculation model can be a SelFlow optical flow calculation model.

[0108] Here, the input of the optical flow calculation model is three frames of images, representing the starting image, the middle image, and the ending image. In this embodiment, the fourth reference high-resolution image can be used as the starting image, and the fourth target satellite image can be used as the middle image and the ending image, and input into the optical flow calculation model to calculate the optical flow. In this way, the forward optical flow from the fourth reference high-resolution image to the fourth target satellite image will be obtained. and the backward optical flow from the fourth target satellite image to the fourth target satellite image in, is the lateral optical flow in the forward optical flow vector, is the longitudinal optical flow in the forward optical flow vector, is the lateral optical flow in the backward optical flow vector, is the longitudinal optical flow in the backward optical flow vector.

[0109] Since the intermediate image and the end image for calculating the backward optical flow vector in this embodiment are the same image (both are the fourth target satellite image), the backward optical flow can be regarded as the calculation error of the optical flow calculation model (the backward optical flow value is generally 1e -1 ~1e -4 In order to eliminate the influence of model error, the forward optical flow vector is subtracted from the backward optical flow vector as the final optical flow vector. The specific formula is as follows: The purpose of dividing by m is to convert the pixel deviation after the resolution upscaling back to the pixel deviation before the resolution upscaling. The m is the resolution multiple of the fourth target satellite image relative to the third target satellite image. In this way, the lateral offset F from the fourth reference high-resolution image pixel to the fourth target satellite image pixel is obtained. u With longitudinal offset F v That is, the final optical flow vector. The positioning accuracy detected by this embodiment is sub-pixel level.

[0110] Here, the set of final optical flow vectors from the n fourth reference high-resolution images to each pixel of the corresponding n fourth target satellite images can be used as the positioning accuracy result.

[0111] It should be noted here that, for the case where no resolution increase is performed, the above solution can be referred to, and the third reference high-resolution image is used as the starting image, and the third target satellite image is used as the intermediate image and the ending image, and input into the optical flow calculation model to calculate the optical flow. In this way, the forward optical flow from the third reference high-resolution image to the third target satellite image will be obtained. and the backward optical flow from the third target satellite image to the third target satellite image in, is the lateral optical flow in the forward optical flow vector, is the longitudinal optical flow in the forward optical flow vector, is the lateral optical flow in the backward optical flow vector, is the longitudinal optical flow in the backward optical flow vector; the forward optical flow vector minus the backward optical flow vector is used as the final optical flow vector. The specific formula is as follows: Since no resolution upscaling is performed, there is no need to divide by m as in the above solution.

[0112] In one possible implementation, the method further includes:

[0113] Obtaining pixel offsets of a preset control point set from the pixel offsets of each pixel;

[0114] Based on the pixel offset of the control point set, a random sampling consistency detection algorithm is used to eliminate abnormal points in the control point set, and the pixel offset of normal control points is obtained as the final positioning accuracy result.

[0115] Here, in order to ensure the accuracy and credibility of the positioning accuracy results, the pixel offset of a reliable control point set (such as a pixel point set on the coastline, etc.) can be obtained from the pixel offset of each pixel to further control the quality of the positioning accuracy results. Then, the random sampling consistency detection method (RANSAC) is used to eliminate false outliers, such as pixels whose pixel offsets differ greatly from other pixel offsets. Finally, the pixel offset of the normal control point is obtained as the final positioning accuracy result.

[0116] It should be noted here that when outputting the positioning accuracy result or the final positioning accuracy result, it can be output in the form of a file in the prescribed format. The positioning accuracy result or the final positioning accuracy result may include the image space coordinates, object space coordinates, sub-pixel level pixel offset and other information of the pixel point, which is convenient for users to view and use.

[0117] The present disclosure also provides a device for detecting the positioning accuracy of satellite images. Figure 2The structure block diagram of the satellite image positioning accuracy detection device according to the embodiment of the present disclosure is shown. The device can be implemented as part or all of an electronic device through software, hardware or a combination of both. Figure 2 As shown, the satellite image positioning accuracy detection device includes:

[0118] The image acquisition module 201 is configured to acquire a first target satellite image to be detected and acquire n first reference high-resolution images matching the first target satellite image, where n is an integer greater than or equal to 1;

[0119] A preprocessing module 202 is configured to perform standardization processing and radiation enhancement on the first target satellite image and the n first reference high-resolution images to obtain a second target satellite image and n second reference high-resolution images;

[0120] The cropping module 203 is configured to crop the second target satellite image into n third target satellite images corresponding to the positions of the n second reference high-resolution images according to the position range of the n second reference high-resolution images;

[0121] The conversion module 204 is configured to convert each second reference high-resolution image and its corresponding third target satellite image into a third reference high-resolution image having the same image size and resolution as the corresponding third target satellite image;

[0122] The positioning analysis module 205 is configured to use a preset optical flow algorithm to calculate the pixel offset of each pixel from the third reference high-resolution image to the third target satellite image as a positioning accuracy result.

[0123] In a possible implementation, the portion of the image acquisition module that acquires the satellite image of the first target to be detected is configured as follows:

[0124] Obtain the original satellite image to be detected and its image channel and calibration coefficient;

[0125] Based on the calibration coefficient and the image channel, calibration calculation is performed on the original satellite image to be detected to obtain a target satellite image;

[0126] Cloud detection is performed on the target satellite image, and the target satellite image whose cloud occlusion ratio exceeds a predetermined threshold is used as the first target satellite image to be detected.

[0127] In a possible implementation, the portion of the image acquisition module that acquires n first reference high-resolution images that match the first target satellite image is configured as follows:

[0128] Based on the first target satellite image, searching from a pre-stored high-resolution image library for n high-resolution images taken by a predetermined satellite, located within the position range of the first target satellite image, having the same image channel as the first target satellite image, having the highest image accuracy level, and being taken at a time closest to that of the first target satellite image as reference high-resolution images;

[0129] The high-resolution image library records a plurality of high-resolution images and their corresponding shooting satellites, shooting times, location ranges, image channels, and positioning accuracy levels of the images.

[0130] In a possible implementation, the device further includes:

[0131] The download module is configured to periodically download high-resolution images to update the high-resolution image library.

[0132] In a possible implementation, the positioning analysis module is configured to:

[0133] If the resolution of the third target satellite image is greater than or equal to the preset resolution, an optical flow algorithm is used to calculate the pixel offset from the third reference high-resolution image to the third target satellite image as a positioning accuracy result;

[0134] The device further comprises:

[0135] an upscaling module configured to, if the resolution of the third target satellite image is less than a preset resolution, perform upscaling processing on the third target satellite image and the third reference high-resolution image to obtain a fourth reference high-resolution image and the fourth target satellite image;

[0136] The calculation module is configured to use an optical flow algorithm to calculate the pixel offset from the fourth reference high-resolution image to the fourth target satellite image as a positioning accuracy result.

[0137] In a possible implementation, the calculation module is configured to:

[0138] The fourth reference high-resolution image is used as the starting image, and the fourth target satellite image is used as the intermediate image and the ending image. The optical flow calculation model is executed to obtain the forward optical flow vector from the fourth reference high-resolution image to the fourth target satellite image. and the backward optical flow vector from the fourth target satellite image to the fourth target satellite image Based on the forward optical flow vector and the backward optical flow vector Calculate the final optical flow vector F = (F u , F v ) as the positioning accuracy result;

[0139] in, is the lateral optical flow in the forward optical flow vector, is the longitudinal optical flow in the forward optical flow vector, is the lateral optical flow in the backward optical flow vector, is the longitudinal optical flow in the backward optical flow vector, and m is the resolution multiple of the fourth target satellite image relative to the third target satellite image.

[0140] In a possible implementation, the device further includes:

[0141] The quality control module is configured to obtain the pixel offset of a preset control point set from the pixel offset of each pixel; based on the pixel offset of the control point set, a random sampling consistency detection algorithm is used to eliminate abnormal points in the control point set, and the pixel offset of the normal control point is obtained as the final positioning accuracy result.

[0142] The technical terms and technical features mentioned in the implementation of this device are the same as or similar to those mentioned in the implementation of the above method. For the interpretation and description of the technical terms and technical features involved in this device, please refer to the explanation of the implementation of the above method, and no further details will be given here.

[0143] The present disclosure also discloses an electronic device, Figure 3 A structural block diagram of an electronic device according to an embodiment of the present disclosure is shown.

[0144] like Figure 3 As shown, the electronic device 300 includes a memory 301 and a processor 302, wherein the memory 301 is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor 302 to implement the method according to the embodiment of the present disclosure.

[0145] Figure 4 A schematic diagram showing the structure of a computer system suitable for implementing the method of the embodiment of the present disclosure is shown.

[0146] like Figure 4 As shown, the computer system 400 includes a processing unit 401, which can execute various processes in the above-mentioned embodiments according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage unit 408 into a random access memory (RAM) 403. Various programs and data required for the operation of the computer system 400 are also stored in the RAM 403. The processing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0147] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed so that a computer program read therefrom can be installed into the storage section 408 as needed. Among them, the processing unit 401 can be implemented as a processing unit such as a CPU, a GPU, a TPU, an FPGA, an NPU, etc.

[0148] In particular, according to embodiments of the present disclosure, the methods described above can be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising computer instructions that, when executed by a processor, implement the method steps described above. In such embodiments, the computer program product can be downloaded and installed from a network via the communication portion 409 and / or installed from removable media 411.

[0149] What needs to be explained here is that Figure 4 The computer system shown is only an example of a computer system. The computer system may also have other structures, for example, without the above-mentioned driver, etc., as long as it can implement the method of the embodiment of the present disclosure, and is not limited here.

[0150] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of the systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, program segment, or portion of code, which contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in an order different from that marked in the accompanying drawings. For example, two blocks shown in succession may actually be executed substantially in parallel, or they may sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, as well as combinations of blocks in the block diagram and / or flowchart, may be implemented using a dedicated hardware-based system that performs the specified functions or operations, or may be implemented using a combination of dedicated hardware and computer instructions. The units or modules described in the embodiments of the present disclosure may be implemented using software or programmable hardware. The described units or modules may also be provided in a processor, and the names of these units or modules do not, in some cases, constitute limitations on the units or modules themselves.

[0151] As another aspect, the present disclosure further provides a computer-readable storage medium. This computer-readable storage medium may be included in the electronic device or computer system described in the above embodiments, or may be a standalone computer-readable storage medium not incorporated into the device. The computer-readable storage medium stores one or more programs, which are used by one or more processors to execute the methods described in the present disclosure.

[0152] The above description is merely a preferred embodiment of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of the invention herein is not limited to the technical solutions formed by the specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the inventive concept. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in this disclosure.

Claims

1. A method for detecting positioning accuracy of satellite images, characterized in that: include: Acquire a first target satellite image to be detected and acquire n first reference high-resolution images that match the first target satellite image, where n is an integer greater than or equal to 1; performing standardization processing and radiation enhancement on the first target satellite image and the n first reference high-resolution images to obtain a second target satellite image and n second reference high-resolution images; According to the position range of the n second reference high-resolution images, the second target satellite image is cropped into n third target satellite images corresponding to the positions of the n second reference high-resolution images; For each second reference high-resolution image and its corresponding third target satellite image, convert the second reference high-resolution image into a third reference high-resolution image having the same image size and resolution as the corresponding third target satellite image; Using a preset optical flow algorithm, a pixel offset of each pixel from the third reference high-resolution image to the third target satellite image is calculated as a positioning accuracy result; The method of using a preset optical flow algorithm to calculate the pixel offset of each pixel from the third reference high-resolution image to the third target satellite image as a positioning accuracy result includes: The third reference high-resolution image is used as the starting image, and the third target satellite image is used as the intermediate image and the ending image. The optical flow calculation model is executed to obtain the forward optical flow vector from the third reference high-resolution image to the third target satellite image. and the backward optical flow vector from the third target satellite image to the third target satellite image in, is the lateral optical flow in the forward optical flow vector, is the longitudinal optical flow in the forward optical flow vector, is the lateral optical flow in the backward optical flow vector, is the longitudinal optical flow in the backward optical flow vector; the forward optical flow vector minus the backward optical flow vector is taken as the positioning accuracy result.

2. The method according to claim 1, characterized in that The step of obtaining the first target satellite image to be detected includes: obtaining the original satellite image to be detected and its image channel and calibration coefficient; Based on the calibration coefficient and the image channel, calibration calculation is performed on the original satellite image to be detected to obtain a target satellite image; Cloud detection is performed on the target satellite image, and the target satellite image whose cloud occlusion ratio exceeds a predetermined threshold is used as the first target satellite image to be detected.

3. The method according to claim 1, characterized in that The acquiring of n first reference high-resolution images matching the first target satellite image includes: Based on the first target satellite image, searching from a pre-stored high-resolution image library for n high-resolution images taken by a predetermined satellite, located within the position range of the first target satellite image, having the same image channel as the first target satellite image, having the highest image accuracy level, and being taken at a time closest to that of the first target satellite image as reference high-resolution images; The high-resolution image library records a plurality of high-resolution images and their corresponding shooting satellites, shooting times, location ranges, image channels, and positioning accuracy levels of the images.

4. The method according to claim 3, characterized in that The method further comprises: The high-resolution image database is updated by downloading the high-resolution image regularly.

5. The method according to claim 1, characterized in that The method of using a preset optical flow algorithm to calculate the pixel offset of each pixel from the third reference high-resolution image to the third target satellite image as a positioning accuracy result includes: If the resolution of the third target satellite image is greater than or equal to the preset resolution, an optical flow algorithm is used to calculate the pixel offset from the third reference high-resolution image to the third target satellite image as a positioning accuracy result; The method further comprises: If the resolution of the third target satellite image is less than the preset resolution, performing resolution upscaling processing on the third target satellite image and the third reference high-resolution image to obtain a fourth reference high-resolution image and a fourth target satellite image; A preset optical flow algorithm is used to calculate the pixel offset from the fourth reference high-resolution image to the fourth target satellite image as a positioning accuracy result.

6. The method according to claim 5, characterized in that The method of using a preset optical flow algorithm to calculate the pixel offset from the fourth reference high-resolution image to the fourth target satellite image as a positioning accuracy result includes: The fourth reference high-resolution image is used as the starting image, and the fourth target satellite image is used as the intermediate image and the ending image. The optical flow calculation model is executed to obtain the forward optical flow vector from the fourth reference high-resolution image to the fourth target satellite image. and the backward optical flow vector from the fourth target satellite image to the fourth target satellite image Based on the forward optical flow vector and the backward optical flow vector Calculate the final optical flow vector F = (F u , F v ) as the positioning accuracy result; in, is the lateral optical flow in the forward optical flow vector, is the longitudinal optical flow in the forward optical flow vector, is the lateral optical flow in the backward optical flow vector, is the longitudinal optical flow in the backward optical flow vector, and m is the resolution multiple of the fourth target satellite image relative to the third target satellite image.

7. The method according to claim 1, characterized in that The method further comprises: Obtaining pixel offsets of a preset control point set from the pixel offsets of each pixel; Based on the pixel offset of the control point set, a random sampling consistency detection algorithm is used to eliminate abnormal points in the control point set, and the pixel offset of normal control points is obtained as the final positioning accuracy result.

8. A device for detecting positioning accuracy of satellite images, characterized in that: include: an image acquisition module configured to acquire a first target satellite image to be detected and acquire n first reference high-resolution images matching the first target satellite image, where n is an integer greater than or equal to 1; a preprocessing module configured to perform standardization processing and radiation enhancement on the first target satellite image and the n first reference high-resolution images to obtain a second target satellite image and n second reference high-resolution images; a cropping module configured to crop the second target satellite image into n third target satellite images corresponding to the positions of the n second reference high-resolution images according to the position range of the n second reference high-resolution images; A conversion module is configured to convert, for each second reference high-resolution image and its corresponding third target satellite image, the second reference high-resolution image into a third reference high-resolution image having the same image size and resolution as the corresponding third target satellite image; a positioning analysis module configured to use a preset optical flow algorithm to calculate a pixel offset of each pixel from the third reference high-resolution image to the third target satellite image as a positioning accuracy result; The method of using a preset optical flow algorithm to calculate the pixel offset of each pixel from the third reference high-resolution image to the third target satellite image as a positioning accuracy result includes: The third reference high-resolution image is used as the starting image, and the third target satellite image is used as the intermediate image and the ending image. The optical flow calculation model is executed to obtain the forward optical flow vector from the third reference high-resolution image to the third target satellite image. and the backward optical flow vector from the third target satellite image to the third target satellite image in, is the lateral optical flow in the forward optical flow vector, is the longitudinal optical flow in the forward optical flow vector, is the lateral optical flow in the backward optical flow vector, is the longitudinal optical flow in the backward optical flow vector; the forward optical flow vector minus the backward optical flow vector is taken as the positioning accuracy result.

9. An electronic device, characterized in that: The method comprises a memory and a processor, wherein the memory is used to store one or more computer instructions, wherein the one or more computer instructions are executed by the processor to implement the method according to any one of claims 1 to 7.

10. A readable storage medium, characterized in that: Computer instructions are stored thereon, and when the computer instructions are executed by a processor, the method steps described in any one of claims 1 to 7 are implemented.

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