A distortion correction method of pushbroom hyperspectral imager

Abstract

The application discloses a distortion correction method of a push-broom hyperspectral imager, and comprises the following steps: firstly, a specific and identifiable target is imaged by the push-broom hyperspectral imager to obtain a push-broom image; the coordinates of each distortion point on the push-broom image are obtained by an angle point recognition algorithm; the distortion correction parameters are obtained by a method based on linear features in the push-broom direction; the distortion correction parameters are obtained by a method based on the invariance of cross ratio in the direction perpendicular to the push-broom direction; and the push-broom image is corrected according to the obtained distortion correction parameters and combined with image grayscale correction. Compared with the traditional distortion correction method, the method has the advantages of simplicity, high efficiency and wide application range, and has a good correction effect on distortion.

Description

Technical Field

[0001] This invention relates to the field of hyperspectral imaging technology, and in particular to a distortion correction method for a pushbroom hyperspectral imager. Background Technology

[0002] Currently, hyperspectral imagers, capable of simultaneously acquiring geometric and spectral information of a target, are widely used in agriculture, biology, and environmental monitoring. Hyperspectral imagers are evolving towards higher resolution, higher detection sensitivity, wider spectral bands, and broader coverage, meaning that the objective lens for a hyperspectral imager needs a large relative aperture and a wide linear field of view. Pushbroom hyperspectral imagers are an important branch of hyperspectral imagers. Off-axis Schwarzschild two-mirror systems, due to their characteristics of no chromatic aberration, no obstruction, large field of view, high numerical aperture, and high energy, have been widely used in the aerospace field in recent years. However, because off-axis Schwarzschild two-mirror systems have fewer free parameters, after correcting for primary spherical aberration, coma, astigmatism, and field curvature, there are no redundant variables to correct distortion. Therefore, distortion cannot be directly eliminated in the optical design and requires distortion correction through image processing methods.

[0003] The hyperspectral imager based on off-axis Schwarzschild two-mirror systems is a pushbroom imaging spectrometer. Although it uses an area array detector, the two-dimensional dimensions of the detector correspond to the spectral information and spatial information of the imager, respectively. The other spatial information comes from the motion of the mounting platform. The pushbroom imaging spectrometer essentially uses the area array detector as a linear array detector, and therefore cannot completely rely on distortion correction methods based on area array detectors. The common distortion correction method for existing linear array detectors is the precision angle measurement method. This method adjusts the turntable to allow the linear array camera to image the collimator at different angles in one dimension. By using the collimator angle and its image point coordinates, the interior orientation elements are solved, thereby calibrating the principal point coordinates and distortion. This method requires a high-precision turntable, a high-quality collimator, and a well-maintained laboratory environment, making it demanding, complex to operate, time-consuming for each measurement, and inefficient in correction. Summary of the Invention

[0004] The purpose of this invention is to provide a distortion correction method for a pushbroom hyperspectral imager. Compared with traditional distortion correction methods, this method has the advantages of being simple, efficient, and widely applicable, and has a good correction effect on distortion.

[0005] The objective of this invention is achieved through the following technical solution:

[0006] A distortion correction method for a pushbroom hyperspectral imager, the method comprising:

[0007] Step 1: Use a pushbroom hyperspectral imager to perform pushbroom imaging on a specific and identifiable target to obtain a pushbroom image;

[0008] Step 2: Obtain the coordinates of each distortion point on the pushbroom image using a corner recognition algorithm;

[0009] Step 3: Obtain distortion correction parameters along the push-broom direction using a method based on linear features;

[0010] Step 4: Obtain distortion correction parameters in the vertical push-broom direction using the cross-ratio invariance method;

[0011] Step 5: Based on the distortion correction parameters along the push-broom direction obtained in Step 3 and the distortion correction parameters in the vertical push-broom direction obtained in Step 4, the push-broom image is corrected in conjunction with image grayscale correction.

[0012] As can be seen from the technical solution provided by the present invention, the above method has the advantages of being simple, efficient, and widely applicable compared with traditional distortion correction methods, and has a good correction effect on distortion. Attached Figure Description

[0013] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the following description of the embodiments will be briefly introduced. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0014] Figure 1 This is a schematic flowchart of the distortion correction method for a pushbroom hyperspectral imager provided in an embodiment of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments, and do not constitute a limitation of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the protection scope of the present invention.

[0016] like Figure 1 The diagram shows a schematic flow chart of a distortion correction method for a pushbroom hyperspectral imager provided in an embodiment of the present invention. The method includes:

[0017] Step 1: Use a pushbroom hyperspectral imager to perform pushbroom imaging on a specific and identifiable target (such as a checkerboard pattern) to obtain a pushbroom image;

[0018] In this step, the pushbroom hyperspectral imager used can be a hyperspectral imager based on an off-axis Schwarzschild two-mirror system. Due to the limited number of free parameters, the off-axis Schwarzschild two-mirror system has no redundant variables to correct distortion after correcting primary spherical aberration, coma, astigmatism, and field curvature. Since the magnitude of distortion is proportional to the cube of the field of view, the residual distortion of this system is relatively large. The optical system produces distortion because the vertical magnification changes with the field of view on the object-image conjugate plane, causing the image to lose its similarity to the object. Therefore, distortion will cause image distortion, but it does not affect the image sharpness. The image distortion can be regarded as a projective transformation from the object side to the image side.

[0019] The distortion of this pushbroom hyperspectral imager is manifested by different magnification in the pushbroom direction, and by symmetrical bending about the central field of view in the vertical pushbroom direction.

[0020] Step 2: Obtain the coordinates of each distortion point on the pushbroom image using a corner recognition algorithm;

[0021] In this step, the corner detection algorithm includes corner detection based on template matching, corner detection based on edge features, and corner detection based on brightness changes; specifically, the corner detection algorithm identifies the intersection position of a specific target on the push-broom image, such as the coordinates of the intersection position on a chessboard, and the intersection position is the coordinate of the distortion point.

[0022] Step 3: Obtain distortion correction parameters along the push-broom direction using a method based on linear features;

[0023] In this step, since pushbroom hyperspectral imagers use pushbroom imaging, the distortion along the pushbroom direction is repetitive, unlike area array detectors which have a fixed distortion center. Therefore, a distortion correction function based on a polynomial model is used. In an ideal imaging model, a straight line in three-dimensional space is still a straight line when projected onto a two-dimensional image plane. However, in the actual projection process, due to the radial distortion of the lens, a straight line in three-dimensional space becomes a curve when projected into two-dimensional space. Therefore, the distortion correction function maps all points on the curve to a straight line, transforming all distorted points on the distorted curve into distortion-free points. These distortion-free points are all on a straight line. Then, by fitting the straight line, the parameters of the polynomial model are obtained, thereby obtaining the distortion correction parameters along the pushbroom direction.

[0024] Step 4: Obtain distortion correction parameters in the vertical push-broom direction using the cross-ratio invariance method;

[0025] In this step, the distortion in the vertical push-broom direction is represented by the radial distortion model, that is, the distorted points are mapped to the undistorted points through the radial distortion model; in perspective projection, there is a cross-ratio invariant property, that is, the cross-ratio of the same typical target in the image is obtained by calculating the cross-ratio of the same typical target in the actual scene and the push-broom, and the two cross-ratios are the same.

[0026] Based on the cross-ratio invariance property, the relevant parameters in the radial distortion model can be calculated, thereby obtaining the distortion correction parameters in the vertical push-broom direction.

[0027] Step 5: Based on the distortion correction parameters along the push-broom direction obtained in Step 3 and the distortion correction parameters in the vertical push-broom direction obtained in Step 4, the push-broom image is corrected in conjunction with image grayscale correction.

[0028] In this step, specifically, the distortion correction parameters along the push-broom direction and the distortion correction parameters perpendicular to the push-broom direction are used to map the coordinates of the distorted points in the push-broom image to the coordinates of the undistorted points, thereby correcting the push-broom image.

[0029] In this step, the image grayscale correction method includes nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation.

[0030] It is worth noting that the contents not described in detail in the embodiments of the present invention belong to the prior art known to those skilled in the art. For example, changing specific targets such as chessboard grids, optimizing algorithms, corner recognition algorithms, image grayscale correction algorithms, etc., these modifications and variations do not depart from the essential scope of the present invention.

[0031] In summary, the method described in this embodiment of the invention uses a linear feature-based approach to correct distortion along the push-broom direction and cross-ratio invariance to correct distortion perpendicular to the push-broom direction. Compared with traditional distortion correction methods, it has the advantages of being simple, efficient, and widely applicable, and has a good correction effect on distortion.

[0032] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims. The information disclosed in the background section is intended only to enhance the understanding of the overall background technology of the present invention and should not be construed as an admission or implication in any way that such information constitutes prior art known to those skilled in the art.

Claims

1. A distortion correction method for a pushbroom hyperspectral imager, characterized in that, The method includes: Step 1: Use a pushbroom hyperspectral imager to perform pushbroom imaging on a specific and identifiable target to obtain a pushbroom image; Step 2: Obtain the coordinates of each distortion point on the pushbroom image using a corner recognition algorithm; Step 3: Obtain distortion correction parameters along the push-broom direction using a method based on linear features; In step 3, since the pushbroom hyperspectral imager uses pushbroom imaging, the distortion along the pushbroom direction is repetitive, unlike the area array detector which has a fixed distortion center. Therefore, a distortion correction function based on a polynomial model is used. The distortion correction function maps all points on the curve to a straight line, transforming all distorted points on the distorted curve into distortion-free points, which are all on a straight line. Then, by fitting the straight line, the parameters of the polynomial model are obtained, thereby obtaining the distortion correction parameters along the pushbroom direction. Step 4: Obtain distortion correction parameters in the vertical push-broom direction using the cross-ratio invariance method; In step 4, the distortion in the vertical push-broom direction is represented by the radial distortion model, that is, the distorted points are mapped to the undistorted points through the radial distortion model; the relevant parameters in the radial distortion model are calculated based on the cross ratio invariance characteristic, thereby obtaining the distortion correction parameters in the vertical push-broom direction. Step 5: Based on the distortion correction parameters along the push-broom direction obtained in Step 3 and the distortion correction parameters in the vertical push-broom direction obtained in Step 4, the push-broom image is corrected in conjunction with image grayscale correction.

2. The distortion correction method for the pushbroom hyperspectral imager according to claim 1, characterized in that, In step 2, the corner detection algorithm includes corner detection based on template matching, corner detection based on edge features, and corner detection based on brightness changes; specifically, the corner detection algorithm identifies the intersection position of a specific target on the push-broom image, and the intersection position is the coordinate of the distortion point.

3. The distortion correction method for a pushbroom hyperspectral imager according to claim 1, characterized in that, In step 5, specifically, based on the distortion correction parameters along the push-broom direction and the distortion correction parameters perpendicular to the push-broom direction, the coordinates of the distorted points in the push-broom image along the push-broom direction and perpendicular to the push-broom direction are mapped to the coordinates of the distortion-free points, thereby correcting the push-broom image. The image grayscale correction methods include nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation.

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