CN114463638B - Geometric correction method for airborne interferometric synthetic aperture radar image - Google Patents
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Abstract
The present disclosure provides a geometric correction method for an airborne interferometric synthetic aperture radar image, comprising: identifying a building area and a non-building area in the airborne interferometric synthetic aperture radar image; performing geometric correction on each pixel in the non-building area; dividing the building area into at least one sub-block according to the size of each building in the building area, and calculating affine transformation coefficients between the sub-blocks under a Gaussian coordinate system and the sub-blocks under an oblique distance coordinate system for each sub-block; geometrically correcting each pixel in the sub-blocks according to the affine transformation coefficient to obtain a corrected amplitude image under Gaussian coordinates; and carrying out interpolation processing on the amplitude image.
Description
Technical Field
The disclosure relates to the technical field of electronic information technology radars, in particular to a geometric correction method for an airborne interferometric synthetic aperture radar image.
Background
The purpose of geometric correction of Synthetic Aperture Radar (SAR) images is to modify the geometric deformation of SAR original images and generate new images meeting certain map projection or graphic expression requirements. At present, an airborne SAR system is provided with a high-precision GPS positioning system, so the influence of topographic relief needs to be considered in a geometric correction process.
The Interferometric synthetic aperture radar (Interferometric SAR, InSAR) utilizes Interferometric phase information of two channels of the Synthetic Aperture Radar (SAR) to extract elevation information of the earth surface, and can expand SAR measurement to a three-dimensional space. Therefore, the airborne interference SAR can perform geometric correction on the image by using the high-precision DEM obtained by the airborne interference SAR, and has the advantage of eliminating the influence of topographic relief on the imaging.
The usual method of geometric correction of the airborne interferometric SAR is to solve its planar localization according to the known DEM, pixel-by-pixel simultaneous range-doppler equations. The problems with this approach are: firstly, the computational efficiency is low, to the large tracts of land image, pixel calculation one by one consumes time long, and in addition, the DEM that the interference SAR obtained itself has certain noise, in building region, receives the fold and covers, shadow geometric distortion influences, and phase noise is big, DEM data noise is great, can cause the deviation of plane location from this, especially at the edge of building, the location deviation will be shown as the distortion of edge on the image after geometric correction, influences the interpretation of visualing.
Disclosure of Invention
In view of the above, the present disclosure provides a geometric correction method for an airborne interferometric synthetic aperture radar image, including: identifying building regions and non-building regions in the airborne interferometric synthetic aperture radar image; performing geometric correction on each pixel in the non-building area; dividing the building area into at least one sub-block according to the size of each building in the building area, and calculating affine transformation coefficients between the sub-blocks under a Gaussian coordinate system and the sub-blocks under an oblique distance coordinate system for each sub-block; geometrically correcting each pixel in the sub-blocks according to the affine transformation coefficient to obtain a corrected amplitude image under Gaussian coordinates; and carrying out interpolation processing on the amplitude image.
According to an embodiment of the present disclosure, wherein the identifying building and non-building areas in the airborne interferometric synthetic aperture radar image comprises: calculating the variation coefficient of the airborne interferometric synthetic aperture radar image; and classifying pixels in the airborne interferometric synthetic aperture radar image by adopting a maximum inter-class variance method based on the variation coefficient, and identifying the building area and the non-building area.
According to an embodiment of the present disclosure, the geometrically correcting each pixel in the non-building area comprises: acquiring the elevation value of each pixel in the non-building area, the position and speed of a carrier when each pixel is imaged, the close distance of the image and the distance to pixel interval; calculating the ground coordinates of each pixel according to the elevation value of each pixel, the position and speed of the carrier when each pixel is imaged, the short distance of the image and the distance to the pixel interval; calculating the corresponding coordinates of the ground coordinates of each pixel in the Gaussian grid; and inserting the image amplitude of each pixel into a corresponding position in the Gaussian grid according to the coordinate of each pixel.
According to an embodiment of the present disclosure, the calculating the ground coordinates of each pixel according to the elevation value of each pixel, the position and speed of the aircraft during imaging of each pixel, the close distance of the image, and the pixel interval from the distance comprises: according to
Calculating ground coordinates of each pixel, wherein (X) S ，Y S ，Z S ) Is the position of the carrier at the time of pixel imaging (V) X ，V Y ，V Z ) Speed of the carrier, R, when imaging the pixels 0 For close range of image, ρ r Is a distance to pixel spacing, f d Is the Doppler center frequency, λ is the wavelength, (X) G ，Y G ) Is the ground coordinate of the pixel, Z G Elevation values of pixels.
According to an embodiment of the present disclosure, the calculating the corresponding coordinates of the ground coordinates of each pixel in the gaussian grid includes: converting the ground coordinates of each pixel into coordinates under a Gaussian coordinate system according to the coordinate conversion relation; according to the formula:
calculating the corresponding coordinates of the ground coordinates of the pixels in the Gaussian grid, wherein (X) gauss ，Y gauss ) The coordinates in the Gaussian coordinate system corresponding to the ground coordinates of each pixel (X) gauss_lt ，Y gauss_lt ) Is the coordinate of a vertex angle of the Gaussian grid res X Is the sampling interval, res, in the X direction in a Gaussian grid Y Is the sampling interval in the Y direction in the gaussian grid, and (i, j) is the coordinate under the gaussian grid.
According to an embodiment of the present disclosure, the dividing the building area into at least one sub-block according to a size of each building in the building area comprises: dividing the building area into at least one sub-block according to the circumscribed rectangle of each building, wherein four corner points of each sub-block fall on the non-building area.
According to the embodiment of the present disclosure, the calculating affine transformation coefficients between the sub-block in the gaussian coordinate system and the sub-block in the skew coordinate system includes: for each sub-block, calculating coordinates of four corner points of the sub-block in a Gaussian grid and coordinates of the four corner points of the sub-block in a slant range coordinate system; establishing an affine transformation relation between the sub-block under the Gaussian coordinate system and the sub-block under the slant range coordinate system; and substituting the coordinates of the four corner points in the Gaussian grid and the coordinates under the slant range coordinate system into the affine transformation relation to solve the affine transformation coefficient.
According to the embodiment of the present disclosure, the affine transformation relationship is:
wherein, (x, y) is the coordinates of the corner points of the sub-blocks in the slant range coordinate system, and (i, j) is the coordinates of the corner points of the sub-blocks in the Gaussian grid.
According to an embodiment of the present disclosure, the geometrically correcting each pixel in a sub-block according to the affine transformation coefficient comprises: calculating the corresponding coordinates of each pixel in the sub-block in the Gaussian grid according to the affine transformation coefficient; and inserting the image amplitude of each pixel into the corresponding position in the Gaussian grid according to the coordinate of each pixel.
According to the embodiment of the present disclosure, the amplitude image is interpolated by using a bilinear interpolation method.
Aiming at the difficulties existing in the geometric correction of the airborne interference SAR image, the geometric correction method provided by the embodiment of the disclosure judges the region with larger DEM noise such as buildings and the like in the image through a certain strategy, and adopts a pixel-level geometric correction method for non-building regions to ensure the precision of geometric correction; for the area of the building, the correction quality reduction caused by DEM noise is avoided by a block correction method, and the correction speed can be increased, so that the defects that the pixel-level geometric correction method is low in efficiency and the quality of correction products in the area of the building and the like is poor are overcome, and the airborne interference SAR image geometric correction efficiency and the interpretability of the correction products are improved.
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The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:
fig. 1 schematically illustrates an overall flowchart of a method for geometry correction of an airborne interferometric synthetic aperture radar image provided in accordance with an embodiment of the present disclosure.
FIG. 2 schematically illustrates a flow chart of specific pixel level correction and block correction combination correction provided in accordance with an embodiment of the present disclosure
Detailed Description
For the purpose of promoting a better understanding of the objects, aspects and advantages of the present disclosure, reference is made to the following detailed description taken in conjunction with the accompanying drawings. It is to be understood that the described embodiments are only a few, and not all, of the disclosed embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the disclosure. The terms "comprises," "comprising," and the like, as used herein, specify the presence of stated features, steps, operations, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, or components.
In the present disclosure, unless otherwise explicitly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly, e.g., as being fixedly connected, detachably connected, or integrated; can be mechanically connected, electrically connected or can communicate with each other; either directly or indirectly through intervening media, either internally or in any other suitable relationship. The specific meaning of the above terms in the present disclosure can be understood by those of ordinary skill in the art as appropriate.
In the description of the present disclosure, it is to be understood that the terms "longitudinal," "length," "circumferential," "front," "rear," "left," "right," "top," "bottom," "inner," "outer," and the like are used in the orientation or positional relationship indicated in the drawings for convenience in describing the present disclosure and for simplicity in description, and are not intended to indicate or imply that the referenced subsystems or elements must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present disclosure.
Throughout the drawings, like elements are represented by like or similar reference numerals. Conventional structures or constructions will be omitted when they may obscure the understanding of the present disclosure. And the shapes, sizes and positional relationships of the components in the drawings do not reflect the actual sizes, proportions and actual positional relationships. Furthermore, in the claims, any reference signs placed between parentheses shall not be construed as limiting the claim.
Similarly, in the above description of exemplary embodiments of the disclosure, various features of the disclosure are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various disclosed aspects. Reference to the description of the terms "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the disclosure. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present disclosure, "a plurality" means at least two, e.g., two, three, etc., unless explicitly specifically limited otherwise.
The method comprises the steps of firstly judging areas with high noise of Digital Elevation Models (DEMs) such as buildings and the like in an image through a certain strategy, and then adopting a conventional pixel-level geometric correction method for non-building areas to ensure the precision of geometric correction; for the area of the building, the correction quality reduction caused by DEM noise is avoided by a block correction method, and the correction speed can be improved. Therefore, the efficiency of geometric correction of the airborne interference SAR image and the interpretability of a correction product are improved.
Fig. 1 schematically illustrates an overall flowchart of a method for geometry correction of an airborne interferometric synthetic aperture radar image provided in accordance with an embodiment of the present disclosure. Fig. 2 schematically illustrates a flow diagram of specific pixel level correction and block correction combination correction provided in accordance with an embodiment of the present disclosure.
As shown in fig. 1 and 2, the method may include, for example, operations S101 to S106.
In operation S101, building and non-building regions in an airborne interferometric synthetic aperture radar image are identified.
In the embodiment of the present disclosure, the building area and the non-building area may be identified by calculating a variation coefficient of the airborne interferometric synthetic aperture radar image, and classifying pixels in the airborne interferometric synthetic aperture radar image by a maximum inter-class variance method based on the variation coefficient.
Illustratively, this may be achieved by:
and calculating the variation coefficient of the image to obtain a variation coefficient graph of the image. And then, automatically selecting a threshold value by adopting an inter-maximum variance method (OSTU) to classify the regions so as to obtain a building region and a non-building region.
In operation S102, geometric correction is performed on each pixel in the non-building area.
In the disclosed embodiment, the geometric correction for each pixel (x, y) in the non-building area may be: and acquiring the elevation value of each pixel in the non-building area, the position and speed of the carrier when each pixel is imaged, the short distance of the image and the pixel interval from the distance. And calculating the ground coordinates of each pixel according to the elevation value of each pixel, the position and speed of the carrier when each pixel is imaged, the short distance of the image and the distance to the pixel interval. And calculating the corresponding coordinates of the ground coordinates of each pixel in the Gaussian grid. And inserting the image amplitude of each pixel into a corresponding position in the Gaussian grid according to the coordinate of each pixel.
Specifically, first, the elevation value Z of the pixel in the DEM generated by using the airborne interferometric SAR data may be read G 。
Then, according to
Calculating ground coordinates of each pixel, wherein (X) S ，Y S ，Z S ) Is the position of the carrier at the time of pixel imaging (V) X ，V Y ，V Z ) Speed of the carrier in imaging the pixels, R 0 For close range of image, ρ r Is a distance to pixel spacing, f d Is the Doppler center frequency, λ is the wavelength, (X) G ，Y G ) Is the ground coordinate of the pixel, Z G Elevation values of pixels. It can be seen that the above equation is divided by (X) G ，Y G ) Besides, other parameters are known, so solving this equation yields the ground coordinates (X) of the pixel (X, y) G ，Y G )。
Next, the ground coordinates (X) of the pixel are transformed according to the coordinate transformation relation G ，Y G ，Z G ) Conversion to coordinates (X) in the Gaussian coordinate System gauss ，Y gauss ，Z gauss ) And calculating the position of the coordinate in the Gaussian grid as follows:
calculating the corresponding coordinates of the ground coordinates of the pixels in the Gaussian grid, wherein (X) gauss ，Y gauss ) The coordinates in the Gaussian coordinate system corresponding to the ground coordinates of each pixel (X) gauss_lt ，Y gauss_lt ) Is the coordinate of a vertex angle of the Gaussian grid res X Is the sampling interval, res, in the X direction in a Gaussian grid Y Is the sampling interval in the Y direction in the gaussian grid, and (i, j) is the coordinate under the gaussian grid.
And finally, inserting the image amplitude of each pixel into the corresponding position in the Gaussian grid according to the coordinate of each pixel in the non-building area, and finishing the geometric correction of each pixel in the non-building area.
In operation S103, the building area is divided into at least one sub-block according to the size of each building in the building area.
In the embodiment of the disclosure, the geometric correction is performed by using a block correction method for the building area. The size and location of the sub-blocks may be set according to the size of each building area. The sub-blocks are generally set as a circumscribed rectangle of the building area, for example, 200 × 200, and may be divided into a plurality of sub-blocks if the building area has a large range. The four corner points of the sub-block need to fall on non-building areas.
In operation S104, for each sub-block, affine transformation coefficients between the sub-block in the gaussian coordinate system and the sub-block in the skew coordinate system are calculated.
In the disclosed embodiment, for each sub-block, the coordinates of the four corner points of the sub-block in the gaussian grid and in the slant range coordinate system are calculated. And establishing an affine transformation relation between the sub-block under the Gaussian coordinate system and the sub-block under the slant range coordinate system. And substituting the coordinates of the four corner points in the Gaussian grid and the coordinates under the slant range coordinate system into an affine transformation relation to solve the affine transformation coefficient.
Specifically, for each divided sub-block, for the four corner points of the block, the positions (i) of the four corner points in the gaussian grid are calculated according to operation S102 1 ，j 1 )，(i 2 ，j 2 )，(i 3 ，j 3 )，(i 4 ，j 4 )。
For each divided sub-block, establishing an affine transformation relationship between the sub-block in the gaussian coordinate system and the sub-block in the skew coordinate system may be:
wherein, (x, y) is the coordinates of the corner points of the sub-blocks in the slant range coordinate system, and (i, j) is the coordinates of the corner points of the sub-blocks in the Gaussian grid.
The slant distance image coordinates (x) of four corner points 1 ，y 1 )，(x 2 ，y 2 )，(x 3 ，y 3 )，(x 4 ，y 4 ) And Gaussian grid coordinate (i) 1 ，j 1 )，(i 2 ，j 2 )，(i 3 ，j 3 )，(i 4 ，j 4 ) Substituting the above equation to form the following linear equation system:
for each divided sub-block, solving the corresponding linear equation set to obtain an affine transformation coefficient a 0 ，a 1 ，a 2 ，a 3 ，b 0 ，b 1 ，b 2 ，b 3 。
In operation S105, geometric correction is performed on each pixel in the sub-block according to the affine transformation coefficient, so as to obtain a corrected amplitude image in gaussian coordinates.
For each of the divided sub-blocks, for each pixel (x, y) in the block, a gaussian coordinate (i, j) of the image after geometric correction is calculated using the calculated affine transformation coefficient. And inserting the image amplitude of each pixel into a corresponding position in the Gaussian grid according to the coordinate of each pixel, and performing block correction on the complete building area.
In operation S106, an interpolation process is performed on the magnitude image.
In the embodiment of the disclosure, due to the characteristic of side-view imaging of the interferometric SAR, the interferometric SAR image is sampled at equal intervals in the slant range direction, so that the side-view direction is converted into the orthographic direction during geometric correction, which causes uneven sampling and requires interpolation processing on the corrected amplitude image. Illustratively, bilinear interpolation may be used for interpolation.
In summary, the method disclosed by the invention is used for carrying out geometric correction on the airborne interferometric SAR image based on a method combining pixel-level correction and block correction. Firstly, the maximum inter-class variance of the variation coefficient map is used to classify and distinguish the building area and the non-building area in the image. Performing pixel-level geometric correction on the non-building area; and for the building area, dividing the image into sub-blocks, establishing an affine transformation relation in the sub-blocks according to the plane positioning of the four corner points, and performing geometric correction in the sub-blocks according to the affine transformation relation. Therefore, the defects that the pixel-level geometric correction method is low in efficiency and the quality of corrected products in areas such as buildings is poor can be overcome, and the efficiency of geometric correction of the airborne interference SAR image and the interpretability of the corrected products are improved.
The above-mentioned embodiments are intended to illustrate the objects, aspects and advantages of the present disclosure in further detail, and it should be understood that the above-mentioned embodiments are only illustrative of the present disclosure and are not intended to limit the present disclosure, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present disclosure should be included in the scope of the present disclosure.
Claims (7)
1. A geometric correction method for an airborne interferometric synthetic aperture radar image comprises the following steps:
identifying building regions and non-building regions in the airborne interferometric synthetic aperture radar image;
geometrically correcting each pixel in the non-building area, comprising: acquiring the elevation value of each pixel in the non-building area, the position and speed of a carrier when each pixel is imaged, the close distance of the image and the distance to pixel interval; calculating the ground coordinates of each pixel according to the elevation value of each pixel, the position and speed of the carrier when each pixel is imaged, the short distance of the image and the distance to the pixel interval; calculating the corresponding coordinates of the ground coordinates of each pixel in the Gaussian grid; inserting the image amplitude of each pixel into the corresponding position in the Gaussian grid according to the coordinate of each pixel;
dividing the building area into at least one sub-block according to the size of each building in the building area,
for each sub-block, calculating an affine transformation coefficient between the sub-block in the Gaussian coordinate system and the sub-block in the slant range coordinate system;
geometrically correcting each pixel in the sub-block according to the affine transformation coefficient to obtain a corrected amplitude image under Gaussian coordinates, wherein the method comprises the following steps: calculating the corresponding coordinates of each pixel in the sub-block in the Gaussian grid according to the affine transformation coefficients, and inserting the image amplitude of each pixel into the corresponding position in the Gaussian grid according to the coordinates of each pixel to obtain an amplitude image corrected under the Gaussian coordinates;
and carrying out bilinear interpolation on the corrected amplitude image under the Gaussian coordinate to eliminate the phenomenon of uneven sampling.
2. The geometry correction method of claim 1, wherein said identifying building and non-building regions in the airborne interferometric synthetic aperture radar image comprises:
calculating the variation coefficient of the airborne interferometric synthetic aperture radar image;
and classifying pixels in the airborne interferometric synthetic aperture radar image by adopting a maximum inter-class variance method based on the variation coefficient, and identifying the building area and the non-building area.
3. The geometry correction method according to claim 1, wherein the calculating the ground coordinates of each pixel according to the elevation value of each pixel, the position and speed of the carrier when each pixel is imaged, the image close distance and the distance to pixel interval comprises:
according to
Calculating ground coordinates of each pixel, wherein (X) S ，Y S ，Z S ) Is the position of the carrier at the time of pixel imaging (V) X ，V Y ，V Z ) Speed of the carrier in imaging the pixels, R 0 For close range of image, ρ r Is a distance to pixel spacing, f d Is the Doppler center frequency, λ is the wavelength, (X) G ，Y G ) Is the ground coordinate of the pixel, Z G The elevation value of a pixel, x is the abscissa of the pixel in the non-building area.
4. The geometric correction method according to claim 1, wherein the calculating the corresponding coordinates of the ground coordinates of each pixel in the gaussian grid comprises:
converting the ground coordinates of each pixel into coordinates under a Gaussian coordinate system according to the coordinate conversion relation;
according to the formula:
calculating the corresponding coordinates of the ground coordinates of the pixels in the Gaussian grid, wherein (X) gauss ，Y gauss ) The coordinates in the Gaussian coordinate system corresponding to the ground coordinates of each pixel (X) gausss_lt ，Y gauss_lt ) Is the coordinate of a vertex angle of the Gaussian grid res X Is the sampling interval, res, in the X direction in a Gaussian grid Y Is the sampling interval of the Y direction in the Gaussian gridAnd (i, j) is the coordinates under the Gaussian grid.
5. The geometry correction method of claim 1, wherein the dividing the building area into at least one sub-block according to a size of each building in the building area comprises:
dividing the building area into at least one sub-block according to the circumscribed rectangle of each building, wherein four corner points of each sub-block fall on the non-building area.
6. The geometric correction method according to claim 5, wherein the calculating affine transformation coefficients between the sub-block in the Gaussian coordinate system and the sub-block in the slant range coordinate system comprises:
for each sub-block, calculating coordinates of four corner points of the sub-block in a Gaussian grid and coordinates of the four corner points of the sub-block in a slant range coordinate system;
establishing an affine transformation relation between the sub-block under the Gaussian coordinate system and the sub-block under the slant range coordinate system;
and substituting the coordinates of the four corner points in the Gaussian grid and the coordinates under the slant range coordinate system into the affine transformation relation to solve the affine transformation coefficient.
7. The geometry correction method according to claim 6, wherein the affine transformation relation is:
wherein (x, y) is the coordinates of the corner point of the sub-block in the slant range coordinate system, (i, j) is the coordinates of the corner point of the sub-block in the Gaussian grid, and a 0 ，a 1 ，a 2 ，a 3 ，b 0 ，b 1 ，b 2 ，b 3 Are affine transform coefficients.
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