A method for measuring the surface velocity of inland water bodies based on Luojia-2-01 satellite

Abstract

The embodiment of the present application discloses a method for measuring the surface velocity of an inland water body based on the Luojia-2 01 satellite, which relates to the field of remote sensing measurement technology. The method comprises: obtaining multiple remote sensing images of the area to be measured, calculating the offset of the auxiliary image relative to the main image, and performing image registration on the auxiliary image to obtain the registered auxiliary image; generating an interference phase image based on the main image and the registered auxiliary image, performing phase filtering, phase calibration and multi-view processing in sequence, obtaining the phase of each point on the area to be measured based on the processed interference phase image, and calculating the corresponding surface velocity of the water body. The present application can realize the velocity measurement of inland rivers by utilizing the spatial resolution of the Luojia-2 01 satellite up to 1m, and will not be affected by the occlusion of terrain and urban buildings. It can obtain a higher velocity measurement accuracy under the condition of a short baseline and a small working angle of view, and is suitable for the surface velocity inversion task of inland water bodies such as rivers.

Description

Technical Field

[0001] The present application relates to the field of remote sensing measurement technology, and in particular to a method for measuring the surface velocity of inland water bodies based on the Luojia-2 01 satellite. Background Art

[0002] In the field of ocean and inland water velocity measurement, traditional measurement methods mainly include current meter method, buoy method and Doppler current profiler method (ADCP). Although these methods are widely used, they are all contact measurements and are time-consuming and labor-intensive.

[0003] With the development of satellite remote sensing technology, spaceborne SAR along-track interferometric SAR (ATI SAR) has been widely used in the measurement of ocean surface flow fields. It has the advantages of all-day, all-weather, wide mapping swath and high resolution. However, due to the limitations of the SAR band and resolution, this technology has certain limitations in the inversion application of inland river flow velocity.

[0004] Since the spatial resolution of SAR satellites with the ability to measure flow by along-track interferometry is generally low, such as the TerraSAR-X interferometry mode with a spatial resolution of 12m, it is suitable for measuring flow in wide-area water bodies such as the ocean. However, inland rivers are narrower and longer than the ocean, and the presence of vegetation, urban buildings, etc., can block the signal and reduce the measurement accuracy. Therefore, the existing remote sensing data cannot meet the requirements for measuring the flow rate of inland rivers. Summary of the invention

[0005] The embodiment of the present application provides a method for measuring the surface velocity of inland water bodies based on the Luojia-2 01 satellite, so as to solve the defects of the above-mentioned related technologies. The technical solution is as follows:

[0006] In a first aspect, an embodiment of the present application provides a method for measuring the surface velocity of an inland water body based on the Luojia-2 01 satellite, comprising:

[0007] Acquire multiple remote sensing images of the area to be measured taken by the Luojia-2 01 satellite;

[0008] Selecting a main image and at least one auxiliary image from the plurality of remote sensing images, respectively calculating the offset of each auxiliary image relative to the main image, and performing image registration on the corresponding auxiliary images based on the offset to obtain registered auxiliary images;

[0009] Perform phase interference based on the main image and the registered auxiliary image to generate an interference phase image;

[0010] performing phase filtering, phase calibration and multi-view processing on the interference phase image in sequence, and outputting the interference phase image after the phase filtering, phase calibration and multi-view processing;

[0011] The phase of each point on the area to be measured is acquired based on the processed interference phase image, and the corresponding water surface flow velocity is calculated based on the phase of each point.

[0012] In an optional solution of the first aspect, respectively calculating the offset of each auxiliary image relative to the main image, and performing image registration on the corresponding auxiliary images based on the offset to obtain the registered auxiliary images, includes:

[0013] Selecting a matching window of a preset size in the main image, selecting a search window corresponding to the position of the matching window in the auxiliary image, translating the matching window within the search window, calculating a cross-correlation parameter between the matching window and the search window at each translation position, obtaining a position corresponding to a maximum value of the cross-correlation parameter as a control point, and calculating an offset of the auxiliary image relative to the main image at the position of the control point;

[0014] The auxiliary image is registered based on each of the control points and the corresponding offset to obtain the registered auxiliary image.

[0015] In an optional solution of the first aspect, the obtaining of a position corresponding to a maximum value of the cross-correlation parameter as a control point, and after calculating the offset of the auxiliary image relative to the main image at the control point position, further comprising:

[0016] Calculating a coherence coefficient between the main image and the auxiliary image;

[0017] Retaining the control points whose coherence coefficients are greater than a preset threshold;

[0018] The performing image registration on the auxiliary image based on each of the control points and the corresponding offset to obtain the registered auxiliary image includes:

[0019] The auxiliary image is registered based on the control points whose coherence coefficients are greater than a preset threshold and the corresponding offsets to obtain the registered auxiliary image.

[0020] In an optional solution of the first aspect, after obtaining the registered auxiliary image, the method further includes:

[0021] The coherence coefficient between the primary image and the registered secondary image is calculated using the formula:

[0022]

[0023] Generating a coherence coefficient distribution map corresponding to the main image and the registered auxiliary image;

[0024] Among them, s1 is the main image, s2 is the auxiliary image, and s2 ′ is the registered auxiliary image, (x, y) is the pixel position of the current coherence coefficient calculation, Ω is a rectangular window centered at (x, y), (l, l) is the pixel coordinate in the rectangular window, (·) * Denotes the complex conjugate.

[0025] In an optional solution of the first aspect, performing phase interference based on the main image and the registered auxiliary image to generate an interference phase image includes:

[0026] The complex data corresponding to the main image is obtained, the complex data of the registered auxiliary image is obtained, and the phase information is extracted based on the conjugate multiplication of the complex data corresponding to the main image and the complex data of the registered auxiliary image to generate the interference phase image.

[0027] In an optional solution of the first aspect, after performing phase interference based on the main image and the registered auxiliary image to generate an interference phase image, the method further includes:

[0028] Obtaining a map containing geographical features of the area to be measured, fitting the map containing geographical features with the interference phase image, and screening out areas where water bodies exist;

[0029] The step of acquiring the phase of each point on the area to be measured based on the processed interference phase image, and calculating the corresponding water surface velocity based on the phase of each point, comprises:

[0030] The phase of each point on the area to be measured is obtained based on the processed interference phase image, the phase of each point on the area where water exists is screened, and the corresponding water surface flow velocity is calculated based on the phase of each point on the area where water exists.

[0031] In an optional solution of the first aspect, the phase of each point on the area to be measured is obtained based on the processed interference phase image, and the corresponding water surface velocity is calculated based on the phase of each point, and the formula is applied:

[0032]

[0033] Generate a water surface flow velocity distribution map on the area to be measured based on the water surface flow velocity corresponding to each point;

[0034] Among them, v ris to invert the radial Doppler velocity, λ is the Ka-band wavelength of the Luojia-2-01 satellite, θ is the incident angle of the Luojia-2-01 satellite, Δt is the shooting time interval of the Luojia-2-01 satellite, is the phase of the corresponding point.

[0035] In a second aspect, the embodiment of the present application further provides an inland water surface velocity measurement device based on the Luojia-2 01 satellite, comprising:

[0036] An image acquisition unit, used to obtain a plurality of remote sensing images of the area to be measured taken by the Luojia-2 01 satellite;

[0037] An image registration unit, configured to select a main image and at least one auxiliary image from the plurality of remote sensing images, respectively calculate an offset of each auxiliary image relative to the main image, and respectively perform image registration on the corresponding auxiliary images based on the offset to obtain registered auxiliary images;

[0038] an interference unit, configured to perform phase interference based on the primary image and the registered auxiliary image to generate an interference phase image;

[0039] An interference image processing unit, used to perform phase filtering, phase calibration and multi-view processing on the interference phase image in sequence, and output the interference phase image after the phase filtering, phase calibration and multi-view processing;

[0040] A calculation unit is used to obtain the phase of each point on the area to be measured based on the processed interference phase image, and calculate the corresponding water surface flow velocity based on the phase of each point.

[0041] In a third aspect, an embodiment of the present application further provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method provided in the first aspect of the embodiment of the present application or any one of the implementation methods of the first aspect is implemented.

[0042] In a fourth aspect, the present application also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method provided by the first aspect of the embodiment of the present application or any one of the implementations of the first aspect.

[0043] The beneficial effects brought about by the technical solutions provided by some embodiments of the present application include at least:

[0044] The embodiment of the present application provides a method for measuring the surface velocity of inland water bodies based on the Luojia-2 01 satellite, which provides a technical means for space-based observation of river flow velocity. The Luojia-2 01 satellite can be used to invert the flow velocity of inland water bodies with high precision, and can avoid the defects of low efficiency, limited accuracy and small coverage of related technologies when measuring water flow velocity through contact. On the basis of existing ground-based cross-sectional flow measurement, rapid and large-scale river flow velocity measurement can be achieved through satellite-borne Ka SAR. The spatial resolution of the Luojia-2 01 satellite is as high as 1m, so the flow velocity of inland rivers can be measured without being affected by terrain or urban buildings. Therefore, higher flow velocity measurement accuracy can be obtained under the condition of short baseline and small working angle of view, which is more suitable for surface flow velocity inversion tasks of inland water bodies such as rivers. The method provided based on the embodiment of the present application can provide effective technical support for the construction of space hydrological stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the present application or related technologies, the drawings required for use in the embodiments or related technical descriptions are briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 It is a flow chart of a method for measuring the surface velocity of an inland water body based on the Luojia-2 01 satellite according to an embodiment of the present application;

[0047] Figure 2 It is a coherence coefficient distribution histogram of a method for measuring the surface velocity of an inland water body based on the Luojia-2-01 satellite in an embodiment of the present application;

[0048] Figure 3 is an interference phase diagram provided by an embodiment of the present application;

[0049] Figure 4 is an interference phase diagram provided by an embodiment of the present application;

[0050] Figure 5 is an interference phase diagram provided by an embodiment of the present application;

[0051] Figure 6 is an interference phase diagram provided by an embodiment of the present application;

[0052] Figure 7 is a flow velocity inversion schematic diagram provided in an embodiment of the present application;

[0053] Figure 8 It is a structural schematic diagram of an inland water surface flow velocity measuring device based on the Luojia-2 01 satellite provided in an embodiment of the present application;

[0054] Fig. 9 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions in this application will be clearly and completely described below in conjunction with the drawings in this application. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0056] The terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or modules is not limited to the listed steps or modules, but may optionally include steps or modules that are not listed, or may optionally include other steps or modules that are inherent to these processes, methods, products or devices.

[0057] It should be noted that the terms "first\second" involved in the present application are only used to distinguish similar objects, and do not represent a specific order for the objects. It is understandable that "first\second" can be interchanged with a specific order or sequence where permitted. It should be understood that the objects distinguished by "first\second" can be interchanged where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those described or illustrated herein.

[0058] It should be noted that the current methods for measuring the flow velocity of inland water bodies include the current meter method, the buoy method and the Doppler current profiler method. These methods are all contact flow measurement methods, which can realize the flow velocity measurement of some points. The measurement efficiency is low and can only reflect the flow velocity of some points. If the distribution of measurement points is unreasonable, the water flow velocity in some areas is easy to be missed, and high-precision measurement cannot be achieved all day, all weather and over a large range.

[0059] In the related technology, some SAR satellites can be used to measure the flow velocity of water in the sea. However, inland rivers are long and narrow and the environment around the rivers is relatively complex. The satellite-borne SAR band and resolution in the related technology make the satellite signal easily affected by the environment around the river and easily blocked by mountains, vegetation, buildings, etc., thus making it impossible to achieve high-precision measurement of the flow velocity of inland rivers.

[0060] The present application is described in detail below with reference to specific embodiments.

[0061] Next, combine Figure 1, introduces the inland water surface velocity measurement method based on Luojia-2 01 satellite provided in the embodiment of this application. For details, please refer to Figure 1 , Figure 1 A schematic diagram of a flow chart of a method for measuring the surface velocity of an inland water body based on the Luojia-2 01 satellite provided in an embodiment of the present application is shown, such as Figure 1 As shown, the inland water surface velocity measurement method based on Luojia-2 01 satellite includes the following steps:

[0062] S101, obtaining a plurality of remote sensing images of the area to be measured taken by the Luojia-2 01 satellite.

[0063] S102, selecting a main image and at least one auxiliary image from the plurality of remote sensing images, respectively calculating the offset of each auxiliary image relative to the main image, and performing image registration on the corresponding auxiliary images based on the offset to obtain registered auxiliary images.

[0064] S103, performing phase interference based on the main image and the registered auxiliary image to generate an interference phase image.

[0065] S104, performing phase filtering, phase calibration and multi-view processing on the interference phase image in sequence, and outputting the interference phase image after the phase filtering, phase calibration and multi-view processing.

[0066] S105, acquiring the phase of each point on the area to be measured based on the processed interference phase image, and calculating the corresponding water surface flow velocity based on the phase of each point.

[0067] Specifically, in S101, multiple remote sensing images of any area to be measured can be obtained by taking pictures with the Luojia-2 01 satellite to obtain a remote sensing image set of the corresponding area.

[0068] It should be noted that the spatial resolution of the Ka-band millimeter-wave synthetic aperture radar carried by Luojia-2-01 satellite is as high as 1m, and the wavelength reaches 8mm. Therefore, it can obtain higher flow velocity measurement accuracy under the conditions of short baseline and small working angle of view, which is more suitable for surface flow velocity inversion tasks of inland water bodies such as rivers.

[0069] It should be noted that the Luojia-2 01 satellite runs along a predetermined orbit, transmits signals to the area to be measured, uses the backscattered signals received by the antenna to perform imaging, and analyzes the phase difference between the signals after imaging. The phase difference is proportional to the Doppler frequency shift, and the water flow velocity at the corresponding point can be measured.

[0070] Specifically, in S102, a main image and at least one auxiliary image may be selected based on the image captured in S101, and the offset of each auxiliary image relative to the main image may be calculated.

[0071] It should be noted that for a dual (multiple) antenna system, these antennas can simultaneously or almost simultaneously transmit radar waves to the ground and receive backscattered echoes from the ground. The image acquired by one of the antennas (or a combination of antennas under a specific configuration) can be selected as the main image, and the image acquired by another or other multiple antennas at the same time can be selected as the auxiliary image; in a single antenna system, the same area can be repeatedly observed by this antenna, and the image acquired for the first time or at other specific times can be selected as the main image, and the image acquired at subsequent times or at times different from the main image can be selected as the auxiliary image. The embodiments of the present application are not limited to this.

[0072] Specifically, image registration is the process of matching and superimposing two or more remote sensing images containing the same scene or target obtained at different times. By observing the same area twice or more times at different times, two or more remote sensing images of the same scene on the ground are obtained; after completing the image registration of two or more remote sensing images, difference phase processing is performed to generate interference phase, thereby obtaining phase information.

[0073] It can be understood that SAR image registration is an important step in interferometry, and the quality of the registration directly affects the quality of the interference fringes. Among them, SAR image registration algorithms mainly include cross-correlation function algorithm, correlation coefficient method, maximum interference spectrum method, phase difference least squares method, etc.

[0074] Specifically, pixel-level registration can be performed first, including:

[0075] First, the remote sensing image of the area to be measured is cropped, for example, an image with a cropped size of 14087×6272 pixels is obtained, so that the positions and image resolution sizes of the main image and the auxiliary image corresponding to the original remote sensing image are the same.

[0076] Specifically, a matching window of a preset size in the main image may be selected, a search window corresponding to the position of the matching window may be selected in the auxiliary image, and the matching window may be translated within the search window. For example, a matching window size of 256×256 pixels may be selected, a search window size of 512×512 pixels may be selected, and the matching window may be translated by integer pixels in rows and columns within the search window.

[0077] Furthermore, the best matching position can be obtained by calculating the cross-correlation parameters and taking the cross-correlation parameters as evaluation indicators, the cross-correlation parameters between the matching window and the search window at each translation position are calculated, the position corresponding to the maximum value of the cross-correlation parameters is obtained as the control point, and the offset of the auxiliary image relative to the main image at the control point position is calculated.

[0078] Among them, the process of moving and searching the maximum cross-correlation function of the window is transformed into the process of locating the maximum value of the cross-correlation function of the main image and the auxiliary image window through Fourier transform, thereby greatly improving the calculation efficiency, and the cross-correlation function is calculated by applying the formula:

[0079] ρ r =|FFTSHIFF(FFT -1 (FFT(y1).*FFT(y2) * ))|;

[0080] Among them, FFT(·) and FFT -1 (·) represents the two-dimensional Fourier transform and inverse transform, .* represents matrix dot multiplication, (·) * represents taking the conjugate complex number, FFTSHIFT(·) represents moving the zero-frequency component of the image spectrum to the middle of the spectrum, y1 and y2 are the window data of the main image and the auxiliary image respectively, and through ρ r The maximum value position can be used to obtain the registration offset of the window center point.

[0081]

[0082] Among them, Δx and Δy are the registration offsets in azimuth and range respectively, M and N are the sizes of the window in azimuth and range respectively, and X max , Y max They are ρ r The azimuth and range position coordinates when the maximum value is obtained.

[0083] In this way, the coordinates and offsets of a series of control points can be obtained.

[0084] Next, sub-pixel level registration is performed, including:

[0085] Specifically, in the sub-pixel level registration process, the cross-correlation function algorithm is continued to be used to register the obtained images.

[0086] First, the 3×3 pixel window centered on each control point is resampled using the Lanczos algorithm to obtain a new window of 300×300 pixels. The matching window size of the main image can be selected as 64×64 pixels and the search window size in the auxiliary image can be selected as 128×128 pixels.

[0087] After calculating the sub-pixel registration displacement corresponding to all windows, the control points with correlation coefficients greater than 0.9 are selected and fitted using a second-order polynomial, for example, the following formula is applied:

[0088] g=a0+a1*x+a2*y+a3*x 2 +a4*y 2 +a5*x*y;

[0089] h=b0+b1*x+b2*y+b3*x 2 +b4*y 2 +b5*x*y;

[0090] Where g and h are the relative offsets of the corresponding registration positions of the main image s1 and the auxiliary image s2, respectively; a0~a5, b0~b5 are fitting parameters, and (x, y) are the coordinates of the control point in the main image s1.

[0091] Thus, the offset corresponding to each control point is calculated, and then the auxiliary image is registered based on the position of each control point and the corresponding offset to obtain the registered auxiliary image.

[0092] Specifically, in order to solve the above second-order polynomial, the coordinate values ​​of at least 11 control points are generally required. Using the above fitting parameters, an offset is calculated for the coordinates of each pixel of the main image s1, so that its matching position can be found in the auxiliary image s2. For each point in the main image, the corresponding offset relationship is added to calculate the coordinate position in the auxiliary image. The value at this position can be obtained by using the cubic convolution interpolation method to interpolate the complex values ​​of the pixels around the matching position in the auxiliary image, that is, the auxiliary image is resampled to obtain a new auxiliary image s2′. After the registration is completed, the coherence coefficient of the two images is calculated as follows: Figure 2 As shown, it can be seen that image registration has a significant effect on improving the coherence coefficient.

[0093] In some embodiments, the quality of the interference fringes can be measured by the coherence coefficient, thereby determining the accuracy and reliability of the SAR along-track interferometry measurement results. The coherence coefficient map of the image pair can be calculated, and the coherence coefficient map can be used as an evaluation index of the quality of the interference pattern to guide the registration process of the SAR image. The value range of the coherence coefficient is usually between 0 and 1, where 1 indicates complete coherence and 0 indicates complete incoherence. The following formula is used to calculate the registered main image s1 and the registered auxiliary image s2 ′ The corresponding coherence coefficient graph ρ:

[0094]

[0095] Among them, (x, y) is the pixel position of the current coherence coefficient calculation, Ω is the rectangular window centered at (x, y), and (k, l) is the pixel coordinates in the window.

[0096] Specifically, the above formula can also be used in the sub-pixel level registration process to calculate the coherence coefficient between the primary image and the secondary image at each control point.

[0097] In some embodiments, Figure 2 As shown, Figure 2The upper and lower figures are the coherence coefficient distribution histogram before and after registration, respectively. After the above image registration process, the coherence coefficients of 80% of the pixels in the main and auxiliary images are greater than 0.7, indicating that the two images have good coherence and can be interfered.

[0098] Specifically, in S103, the main image s1 and the registered auxiliary image s2′ are conjugate-multiplied and then subjected to interference processing to obtain the interference phase

[0099] Specifically, the complex data corresponding to the main image is obtained, the complex data of the registered auxiliary image is obtained, and the phase information is extracted based on the conjugate multiplication of the complex data corresponding to the main image and the complex data of the registered auxiliary image to generate the interference phase image.

[0100] For example, assuming that any point on the SAR main image s1 is s1(x, y), and any point on the SAR auxiliary image s2 is s2(x, y), each point consists of amplitude and phase information, the application formula is:

[0101]

[0102] The conjugate multiplication of the SAR image pairs gives the interferogram in complex form. The phase of the interferogram is the entangled form of the actual interference phase, which differs from the actual phase by an integer multiple of 2π. The application formula is:

[0103]

[0104] Among them, A1, A2, A are amplitudes, That is the interference phase at the corresponding position.

[0105] In some embodiments, the obtained interference phase map is as follows: Figure 3 shown.

[0106] Specifically, in S104, phase filtering, phase calibration and multi-view processing may be performed on the interference phase image in sequence, and the interference phase image after the phase filtering, phase calibration and multi-view processing may be output.

[0107] Specifically, the quality of the interference phase image is the key to the interference processing results. The ideal interference phase has the characteristics of continuity and periodicity, but the coherent speckle noise and system noise in the image lead to the difference between the phase value and the real phase of the physical quantity it actually reflects, making the interference fringes blurred and affecting the subsequent processing. Therefore, it is necessary to reduce the noise of the interference phase image through filtering.

[0108] Specifically, in order to improve the quality of the interference phase image, make the interference phase have the characteristics of continuity and periodicity, and improve the interference phase sensitivity and signal-to-noise ratio, it is necessary to process the interference phase image through filtering. Figure 3 The interference phase diagram shown in FIG. 1 is filtered by a second circular median filter. The filtered interference phase diagram is shown in FIG. Figure 4 shown.

[0109] Specifically, in the case of an ideal platform, the pointing direction of the main and secondary antennas and their flight orbits are the same, and the interference phase of the ground target should be zero. However, in actual situations, due to the deviation of the pointing direction and flight orbit of the main and secondary antennas during the flight of the satellite, the observation position of the same ground target point changes, causing the slant range from the antenna to the ground stationary target to change, making the interference phase of the stationary target in the interference diagram not zero. Therefore, it is necessary to perform phase calibration on the interference phase diagram. Specifically, the phase offset can be eliminated by uniformly selecting control points and fitting the phase error with a quadratic polynomial. For each coordinate point, the original phase value is subtracted from the fitted error phase value to obtain the interference phase after calibration.

[0110] For example, from Figure 4 It can be seen from the filtered interferometric phase diagram that the phase of the land part is not zero, but there is no radial velocity on the actual land, and the theoretical value of the phase value should be zero or near zero, so it is necessary to perform non-zero phase calibration on the phase diagram. Specifically, after uniformly selecting control points, the phase error can be fitted with a quadratic polynomial to achieve phase offset elimination.

[0111] Specifically, in the filtered interference phase map, 128 points are taken in each row and 50 points are taken in each column to evenly select control points for fitting, and the error phase value is obtained. A quadratic polynomial fitting is performed, and the binomial fitting formula obtained is shown as follows:

[0112]

[0113] in, is the error phase, and x and y are the coordinates of each point in the interference phase diagram.

[0114] For each coordinate point, the original phase value is subtracted from the fitted error phase value, and the calibrated interference phase diagram can be obtained as follows: Figure 5 shown.

[0115] Furthermore, in order to improve the interferometric phase sensitivity and signal-to-noise ratio, it is also necessary to perform multi-view processing on the interferometric phase image after calibration to achieve noise reduction.

[0116] Specifically, phase neighborhood averaging can be performed on the interference phase map after calibration, and N-view processing can be performed on the phase in any direction at the cost of loss of spatial resolution, thereby reducing the spatial resolution in the corresponding direction by N times.

[0117] Generally speaking, if the speed measurement accuracy is to reach 0.1m / s, the number of views needs to reach 60 to 70 or above.

[0118] For example, the average flow velocity of a section of the Yangtze River is above 1m / s. Under this flow velocity background, an error of 0.1m / s may exist. The multi-view number of both the azimuth and distance directions can be selected to be 50. After 50 times multi-view processing in the azimuth and distance directions, the obtained interference phase diagram is as follows: Figure 6 shown.

[0119] In some embodiments, after performing phase interference based on the main image and the registered auxiliary image to generate an interference phase image, the method further includes:

[0120] Obtaining a map containing geographical features of the area to be measured, fitting the map containing geographical features with the interference phase image, and screening out areas where water bodies exist;

[0121] The step of acquiring the phase of each point on the area to be measured based on the processed interference phase image, and calculating the corresponding water surface velocity based on the phase of each point, comprises:

[0122] The phase of each point on the area to be measured is obtained based on the processed interference phase image, the phase of each point on the area where water exists is screened, and the corresponding water surface flow velocity is calculated based on the phase of each point on the area where water exists.

[0123] In some embodiments, water bodies and land areas may also be distinguished based on the phase value of each point on the interferometric phase image after phase filtering, phase calibration, and multi-view processing, which is not limited in this embodiment of the present application.

[0124] Specifically, in S105, the phase of each point on the area to be measured is obtained based on the processed interference phase image, and the corresponding water surface velocity is calculated based on the phase of each point, using the formula:

[0125]

[0126] Generate a water surface flow velocity distribution map on the area to be measured based on the water surface flow velocity corresponding to each point;

[0127] Among them, v ris to invert the radial Doppler velocity, λ is the Ka-band wavelength of the Luojia-2-01 satellite, θ is the incident angle of the Luojia-2-01 satellite, Δt is the shooting time interval of the Luojia-2-01 satellite, is the phase of the corresponding point.

[0128] Thus, according to the phase of each point on the interference phase image, it is easy to obtain the water velocity distribution image of the area to be measured. For example, the velocity inversion image finally calculated by inversion is as follows: Figure 7 shown.

[0129] The following are device embodiments of the present application, which can be used to execute the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0130] See next Figure 8 , is a schematic diagram of the structure of an inland water surface velocity measurement device based on the Luojia-2 01 satellite provided by an exemplary embodiment of the present application. The device can be implemented as all or part of a terminal through software, hardware, or a combination of both, and can also be integrated on a server as an independent module. The inland water surface velocity measurement device based on the Luojia-2 01 satellite in the embodiment of the present application can be applied to a terminal or a cloud. The device 800 includes an image acquisition unit 810, an image registration unit 820, an interference unit 830, an interference image processing unit 840, and a calculation unit 850, wherein:

[0131] The image acquisition unit 810 is used to obtain multiple remote sensing images of the area to be measured taken by the Luojia-2 01 satellite;

[0132] The image registration unit 820 is used to select a main image and at least one auxiliary image from the multiple remote sensing images, respectively calculate the offset of each auxiliary image relative to the main image, and perform image registration on the corresponding auxiliary images based on the offset to obtain registered auxiliary images;

[0133] The interference unit 830 is used to perform phase interference based on the main image and the registered auxiliary image to generate an interference phase image;

[0134] The interference image processing unit 840 is used to perform phase filtering, phase calibration and multi-view processing on the interference phase image in sequence, and output the interference phase image after the phase filtering, phase calibration and multi-view processing;

[0135] The calculation unit 850 is used to obtain the phase of each point on the area to be measured based on the processed interference phase image, and calculate the corresponding water surface flow velocity based on the phase of each point.

[0136] It should be noted that the device 800 provided in the above embodiment only uses the division of the above functional modules as an example when executing the inland water surface velocity measurement method based on the Luojia-2 01 satellite. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device provided in the above embodiment and the embodiment of the inland water surface velocity measurement method based on the Luojia-2 01 satellite belong to the same concept. The implementation process is detailed in the method embodiment, which will not be repeated here.

[0137] An embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of any of the above-mentioned method embodiments when executing the program.

[0138] See also Fig. 9 , which is a structural block diagram of an electronic device provided in an embodiment of the present application.

[0139] like Fig. 9 As shown, the electronic device 900 includes: a processor 901 and a memory 902 .

[0140] In the embodiment of the present application, the processor 901 is the control center of the computer system, which can be a processor of a physical machine or a processor of a virtual machine. The processor 901 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 901 may be implemented in at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), and PLA (Programmable Logic Array).

[0141] The processor 901 may also include a main processor and a coprocessor. The main processor is a processor for processing data in the awake state, also called a CPU (Central Processing Unit); the coprocessor is a low-power processor for processing data in the standby state.

[0142] The memory 902 may include one or more computer-readable storage media, which may be non-transitory. The memory 902 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage devices, flash memory storage devices. In some embodiments of the present application, the non-transitory computer-readable storage medium in the memory 902 is used to store at least one instruction, and the at least one instruction is used to be executed by the processor 901 to implement the method in the embodiment of the present application.

[0143] In some embodiments, the electronic device 900 further includes: a peripheral device interface 903 and at least one peripheral device 904. The processor 901, the memory 902 and the peripheral device interface 903 can be connected via a bus or a signal line. Each peripheral device 904 can be connected to the peripheral device interface 903 via a bus, a signal line or a circuit board. Specifically, the peripheral device 904 includes: a display screen, a camera and an audio circuit. The peripheral device interface 903 can be used to connect at least one peripheral device related to I / O (Input / Output) to the processor 901 and the memory 902.

[0144] In some embodiments of the present application, the processor 901, the memory 902, and the peripheral device interface 903 are integrated on the same chip or circuit board; in some other embodiments of the present application, any one or two of the processor 901, the memory 902, and the peripheral device interface 903 can be implemented on a separate chip or circuit board. This embodiment of the present application does not specifically limit this.

[0145] The electronic device structure block diagram shown in the embodiment of the present application does not constitute a limitation on the electronic device 900. The electronic device 900 may include more or fewer components than shown in the figure, or combine certain components, or adopt a different component arrangement.

[0146] The embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the steps of the method of any of the above embodiments are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0147] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, by hardware. Based on this understanding, the above technical solution can be essentially or in other words, the part that contributes to the relevant technology can be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiment.

[0148] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for measuring the surface velocity of inland water bodies based on Luojia-2 01 satellite, characterized in that: include: Acquire multiple remote sensing images of the area to be measured taken by the Luojia-2 01 satellite; Selecting a main image and at least one auxiliary image from the plurality of remote sensing images, respectively calculating the offset of each auxiliary image relative to the main image, and performing image registration on the corresponding auxiliary images based on the offset to obtain registered auxiliary images; Perform phase interference based on the main image and the registered auxiliary image to generate an interference phase image; performing phase filtering, phase calibration and multi-view processing on the interference phase image in sequence, and outputting the interference phase image after the phase filtering, phase calibration and multi-view processing; Acquire the phase of each point on the area to be measured based on the processed interference phase image, and calculate the corresponding water surface velocity based on the phase of each point; The performing phase interference based on the main image and the registered auxiliary image to generate an interference phase image includes: Acquire complex data corresponding to the main image, acquire complex data of the registered auxiliary image, extract phase information based on conjugate multiplication of the complex data corresponding to the main image and the complex data of the registered auxiliary image, and generate the interference phase image; After performing phase interference based on the main image and the registered auxiliary image to generate an interference phase image, the method further includes: Obtaining a map containing geographical features of the area to be measured, fitting the map containing geographical features with the interference phase image, and screening out areas where water bodies exist; The step of acquiring the phase of each point on the area to be measured based on the processed interference phase image, and calculating the corresponding water surface velocity based on the phase of each point, comprises: Based on the processed interference phase image, the phase of each point on the area to be measured is acquired, the phase of each point on the area where water exists is screened, and the corresponding water surface velocity is calculated based on the phase of each point on the area where water exists; The phase of each point on the area to be measured is obtained based on the processed interference phase image, and the corresponding water surface velocity is calculated based on the phase of each point, using the formula: Generate a water surface flow velocity distribution map on the area to be measured based on the water surface flow velocity corresponding to each point; Among them, v r is to invert the radial Doppler velocity, λ is the Ka-band wavelength of the Luojia-2-01 satellite, θ is the incident angle of the Luojia-2-01 satellite, Δt is the shooting time interval of the Luojia-2-01 satellite, is the phase of the corresponding point.

2. The method for measuring the surface velocity of inland water bodies based on Luojia-2-01 satellite according to claim 1 is characterized in that: The step of respectively calculating the offset of each auxiliary image relative to the main image, and performing image registration on the corresponding auxiliary images based on the offset to obtain the registered auxiliary images includes: Selecting a matching window of a preset size in the main image, selecting a search window corresponding to the position of the matching window in the auxiliary image, translating the matching window within the search window, calculating a cross-correlation parameter between the matching window and the search window at each translation position, obtaining a position corresponding to a maximum value of the cross-correlation parameter as a control point, and calculating an offset of the auxiliary image relative to the main image at the position of the control point; The auxiliary image is registered based on each of the control points and the corresponding offset to obtain the registered auxiliary image.

3. The method for measuring the surface velocity of inland water bodies based on Luojia-2-01 satellite according to claim 2 is characterized in that: After obtaining the position corresponding to the maximum value of the cross-correlation parameter as the control point and calculating the offset of the auxiliary image relative to the main image at the control point, the method further includes: Calculating a coherence coefficient between the main image and the auxiliary image; Retaining the control points whose coherence coefficients are greater than a preset threshold; The performing image registration on the auxiliary image based on each of the control points and the corresponding offset to obtain the registered auxiliary image includes: The auxiliary image is registered based on the control points whose coherence coefficients are greater than a preset threshold and the corresponding offsets to obtain the registered auxiliary image.

4. The method for measuring the surface velocity of inland water bodies based on Luojia-2-01 satellite according to claim 3 is characterized in that: After obtaining the registered auxiliary image, the method further includes: The coherence coefficient between the primary image and the registered secondary image is calculated using the formula: Generating a coherence coefficient distribution map corresponding to the main image and the registered auxiliary image; Among them, s1 is the main image, s2 is the auxiliary image, and s2 ′ is the registered auxiliary image, (x, y) is the pixel position of the current coherence coefficient calculation, Ω is a rectangular window centered at (x, y), (k, l) is the pixel coordinates in the rectangular window, (·) * Denotes the complex conjugate.

5. An inland water surface velocity measurement device based on Luojia-2 01 satellite, characterized in that: include: An image acquisition unit, used to obtain a plurality of remote sensing images of the area to be measured taken by the Luojia-2 01 satellite; An image registration unit, configured to select a main image and at least one auxiliary image from the plurality of remote sensing images, respectively calculate an offset of each auxiliary image relative to the main image, and respectively perform image registration on the corresponding auxiliary images based on the offset to obtain registered auxiliary images; an interference unit, configured to perform phase interference based on the primary image and the registered auxiliary image to generate an interference phase image; An interference image processing unit, used to perform phase filtering, phase calibration and multi-view processing on the interference phase image in sequence, and output the interference phase image after the phase filtering, phase calibration and multi-view processing; A calculation unit, used for acquiring the phase of each point on the area to be measured based on the processed interference phase image, and calculating the corresponding water surface velocity based on the phase of each point; The performing phase interference based on the main image and the registered auxiliary image to generate an interference phase image includes: Acquire complex data corresponding to the main image, acquire complex data of the registered auxiliary image, extract phase information based on conjugate multiplication of the complex data corresponding to the main image and the complex data of the registered auxiliary image, and generate the interference phase image; After performing phase interference based on the main image and the registered auxiliary image to generate an interference phase image, the method further includes: Obtaining a map containing geographical features of the area to be measured, fitting the map containing geographical features with the interference phase image, and screening out areas where water bodies exist; The step of acquiring the phase of each point on the area to be measured based on the processed interference phase image, and calculating the corresponding water surface velocity based on the phase of each point, comprises: Based on the processed interference phase image, the phase of each point on the area to be measured is acquired, the phase of each point on the area where water exists is screened, and the corresponding water surface velocity is calculated based on the phase of each point on the area where water exists; The phase of each point on the area to be measured is obtained based on the processed interference phase image, and the corresponding water surface velocity is calculated based on the phase of each point, using the formula: Generate a water surface flow velocity distribution map on the area to be measured based on the water surface flow velocity corresponding to each point; Among them, v r is to invert the radial Doppler velocity, λ is the Ka-band wavelength of the Luojia-2-01 satellite, θ is the incident angle of the Luojia-2-01 satellite, Δt is the shooting time interval of the Luojia-2-01 satellite, is the phase of the corresponding point.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method according to any one of claims 1 to 4 are implemented.

7. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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