CN107274441B - Wave band calibration method and system for hyperspectral image - Google Patents
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Abstract
The invention provides a wave band registration method of a hyperspectral image, which comprises the following steps: selecting a certain wave band as a reference wave band, and generating a new image as a reference image; extracting feature points on the to-be-registered waveband image, and extracting a template window by taking the feature points as centers; selecting coordinates which are the same as the characteristic points on the reference image as a center, extracting a search window, and obtaining coarse matching points by using a template matching method; establishing a rough transformation relation between the band image to be registered and a reference image, calculating a matching point of the feature point on the reference image, taking the matching point as a center to extract a search window, and obtaining a fine matching point by using a template matching method; and establishing a precise transformation relation between the to-be-registered band image and the reference image, and finishing the correction of the to-be-registered band image. By adopting the technical scheme of the invention, the wave band registration of the hyperspectral image of the unmanned aerial vehicle can be automatically completed, so that all wave bands of the hyperspectral image of the unmanned aerial vehicle have space consistency, the processing speed is high, and the registration precision is high.
Description
Technical Field
The invention belongs to the technical field of hyperspectral image processing, and relates to a method and a system for registering hyperspectral image wave bands of an unmanned aerial vehicle.
Background
The unmanned aerial vehicle has the advantages of high maneuverability and low cost, has wide application in the aspects of small-range large-scale drawing, emergency disaster relief, resource environment investigation, fine agriculture and the like in recent years, and becomes an important supplementary mode of satellite remote sensing and traditional aerial remote sensing. Compared with satellite hyperspectral remote sensing, the unmanned aerial vehicle hyperspectral remote sensing has the advantage of being unique, and because the flying height of the unmanned aerial vehicle is low, the acquired hyperspectral image has abundant spectral information and very high spatial resolution, and the spatial resolution can reach the centimeter level, so that the unmanned aerial vehicle hyperspectral remote sensing can be applied to the aspects of environment monitoring, accurate agriculture (crop growth judgment, crop estimation, disease and pest early warning) and the like.
Due to the characteristics of the unmanned aerial vehicle and the imaging mode of the hyperspectral imager carried on the unmanned aerial vehicle, the processing of the hyperspectral image of the unmanned aerial vehicle is a challenge. The traditional hyperspectral imager carried on a satellite is provided with an optical splitter, wave bands are aligned, and registration between the wave bands is not needed. Unmanned aerial vehicle especially revolves wing section unmanned aerial vehicle is generally several kilograms because the load is limited, can not carry on traditional high spectral imager. At present, a hyperspectral imager carried on an unmanned aerial vehicle is mostly a frame-type imager, and a multiband hyperspectral image is obtained by adopting a continuous exposure imaging mode. Since the drone is moving during exposure, different bands of the same scene image are not aligned, and registration between the bands is required.
Disclosure of Invention
In view of this, the present invention provides a method for band registration of a hyperspectral image, including:
s100, selecting a certain wave band as a reference wave band, and then respectively expanding a plurality of pixels on the upper, lower, left and right sides of an image of the wave band to generate a new image as a reference image;
step S200, extracting feature points on a wave band image to be registered, and extracting a template window of each feature point by taking the feature points as centers;
step S300, selecting the same coordinates as the characteristic points on the reference image as the center for each characteristic point, extracting a search window, and obtaining a coarse matching point by using a template matching method;
step S400, establishing a rough transformation relation between the band image to be registered and a reference image based on the rough matching points, calculating the matching points of the feature points on the reference image according to the rough transformation relation for each feature point, extracting a search window by taking the matching points as the center, and obtaining fine matching points by using a template matching method;
step S500, establishing a precise transformation relation between the to-be-registered band image and the reference image based on the precise matching point, and then completing the correction of the to-be-registered band image by using the precise transformation relation;
and S600, repeating the steps S200-S500 to finish the registration of the next wave band.
Further, before extracting the feature points, the enhancement operation of highlighting contrast is carried out on the band image to be registered.
Further, the rough matching points in step S400 are rough matching points after the mismatching points are removed by using a random sampling consistency method and a least square method, and the fine matching points in step S500 are fine matching points after the mismatching points are removed by using a random sampling consistency method and a least square method.
Further, after each band is registered, the offset of the band with respect to the reference band is calculated, and a search window is extracted for the next band using the coordinates of the feature point plus the offset as the center in step S300.
Further, in step S200, use is made of
Or SIFT or Harris or SURF as an operator for extracting feature points.
The invention also provides a wave band registration system of the hyperspectral image, which comprises:
the reference image generation module is used for selecting a certain wave band as a reference wave band, then expanding a plurality of pixels on the upper, lower, left and right sides of an image of the wave band respectively to generate a new image as a reference image;
the template window extraction module is used for extracting feature points on the wave band image to be registered and extracting a template window of each feature point by taking the feature points as centers;
the rough matching module is used for selecting the same coordinates as the characteristic points on the reference image as the center for each characteristic point, extracting a search window and obtaining rough matching points by using a template matching method;
a fine matching module used for establishing a coarse transformation relation between the band image to be registered and the reference image based on the coarse matching points, calculating the matching points of the feature points on the reference image according to the coarse transformation relation for each feature point, extracting a search window by taking the matching points as the center, and obtaining fine matching points by using a template matching method
The geometric correction module is used for establishing a precise transformation relation between the to-be-registered waveband image and the reference image based on the precise matching point and then completing the correction of the to-be-registered waveband image by using the precise transformation relation;
further, the waveband registration system further comprises an image enhancement module, which is used for performing contrast enhancement operation on the waveband image to be registered before extracting the feature points.
Further, the band registration system further comprises a mismatching point removing module, which is used for removing mismatching points by using a random sampling consistency method and a least square method.
Further, the above band registration system further includes an offset calculation module, configured to calculate an offset of a band with respect to a reference band after registering the band.
By adopting the technical scheme, the wave band registration of the hyperspectral image of the unmanned aerial vehicle can be automatically completed, so that all wave bands of the hyperspectral image of the unmanned aerial vehicle have space consistency, the processing speed is high, and the registration precision is high.
The foregoing summary is provided for the purpose of description only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features of the present invention will be readily apparent by reference to the drawings and following detailed description.
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In the drawings, like reference numerals refer to the same or similar parts or elements throughout the several views unless otherwise specified. The figures are not necessarily to scale. It is appreciated that these drawings depict only some embodiments in accordance with the disclosure and are therefore not to be considered limiting of its scope.
Fig. 1 is a flowchart of a band registration method for hyperspectral images according to an embodiment of the invention.
Fig. 2 is a structural diagram of a band registration system of a hyperspectral image according to an embodiment of the invention.
Detailed Description
In the following, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments may be modified in various different ways, all without departing from the spirit or scope of the present invention. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.
As shown in fig. 1, the method for band registration of hyperspectral images of the embodiment includes the following steps:
step S100, selecting a certain wave band as a reference wave band, and expanding M pixels respectively at the upper, lower, left and right sides of an image of the wave band to generate a new image as a reference image.
The wave band registration is carried out by taking a certain wave band of the hyperspectral image as a reference. Therefore, before registration, a proper waveband needs to be selected as a reference waveband, the selection principle is that the registration success rate and the accuracy of other wavebands and the waveband are higher, and the selection method can be determined through analysis of each spectral range, actual registration experiments and the like, which is common knowledge and is not repeated.
The size of each wave band of the original hyperspectral image is the same, and the size of each wave band of the original hyperspectral image must be the same after registration according to the actual application requirements. However, since the imaging time is inconsistent, the imaging range of the reference band is inconsistent with that of other bands, and if direct registration is performed and the size of the registered image is guaranteed to be unchanged, partial data of the registered image is lost, so after the reference band is selected, the image of the reference band is respectively expanded by M pixels up, down, left and right, and a new image is formed as the reference image. The value of M is determined by the maximum offset between the other bands and the reference band, and the value of M is greater than the maximum offset.
And S200, extracting feature points on the wave band image to be registered, and extracting a template window of each feature point by taking the feature points as centers.
Before extracting the feature points, enhancement operation is carried out on the band image to be registered, the contrast is highlighted, and the number of the extracted feature points is larger.
In this step, can be used
Or SIFT (Scale-invariant feature transform) or Harris or SURF (Speeded-Up Robust Features) as an operator for extracting feature points, and this embodiment is preferred to use
And the operator searches a point of an error ellipse which is as small as possible and is close to a circle in the wave band image to be registered as a characteristic point by calculating the Robert gradient of each pixel of the wave band image to be registered and a gray covariance matrix of a window with the pixel (c, r) as the center.
And step S300, selecting the same coordinates as the characteristic points on the reference image as the center for each characteristic point, extracting a search window, and then completing coarse matching by using the normalized correlation coefficient to obtain coarse matching points.
After the coarse matching is completed, some mismatching points may exist, and the mismatching points are removed by using a random sample Consensus (RANSAC) method and a least square method to obtain correct coarse matching points.
And S400, establishing a rough transformation relation between the band image to be registered and the reference image based on the correct rough matching points, calculating the matching point of the feature point on the reference image according to the rough transformation relation for each feature point, taking the matching point as the center to extract a search window, and completing fine matching by using a normalized correlation coefficient to obtain a fine matching point.
Compared with coarse matching, fine matching uses a smaller search window and a higher normalized correlation coefficient threshold value to find a more accurate fine matching point. And after the accurate matching is finished, eliminating mismatching points by using RANSAC and a least square method to obtain correct accurate matching points.
And S500, establishing a precise transformation relation between the to-be-registered waveband image and the reference image based on the accurate matching point, and then finishing the correction of the to-be-registered waveband image by using the precise transformation relation.
The correction model uses a polynomial model, and the order of the polynomial model is determined according to the number of matching points and the image deformation condition. The polynomial model requires a relatively uniform distribution of matching points. Homogenizing the matching points before correction, wherein the specific method comprises the following steps: dividing the image according to grids, distributing the matching points to different grids, reserving a control point with the maximum matching degree for the grids with the matching points, and then constructing a model by using the homogenized matching points.
And S600, repeating the steps S200-S500 to finish the registration of the next wave band.
In order to improve the registration efficiency, after each band is registered, the offset of the band with respect to the reference band is calculated, and a search window is extracted for the next band using the coordinates of the feature point plus the offset as the center in step S300.
As shown in fig. 2, this embodiment further provides a system for the above-mentioned wavelength band registration method for hyperspectral images, including:
a reference image generating module 100, configured to select a certain waveband as a reference waveband, and then expand a plurality of pixels up, down, left, and right of an image of the waveband to generate a new image as a reference image;
the template window extraction module 200 is configured to extract feature points on a to-be-registered band image, and extract a template window of each feature point with the feature points as centers;
the rough matching module 300 is configured to select, for each feature point, the same coordinate as the feature point on the reference image as a center, extract a search window, and obtain a rough matching point by using a template matching method;
a fine matching module 400, configured to establish a coarse transformation relationship between the band image to be registered and the reference image based on the coarse matching points, calculate, for each feature point, a matching point of the feature point on the reference image according to the coarse transformation relationship, extract a search window with the matching point as a center, and obtain a fine matching point by using a template matching method
And the geometric correction module 500 is configured to establish a precise transformation relationship between the to-be-registered band image and the reference image based on the precise matching point, and then use the precise transformation relationship to complete correction of the to-be-registered band image.
The waveband registration system of the embodiment further includes an image enhancement module, which is configured to perform an enhancement operation of highlighting contrast on the waveband image to be registered before extracting the feature points.
The band registration system of the embodiment further comprises a mismatching point removing module, which is used for removing mismatching points by using a random sampling consistency method and a least square method.
The waveband registration system of the embodiment further includes an offset calculation module, configured to calculate an offset of a waveband with respect to a reference waveband after registering the waveband.
The wave band calibration method and the wave band calibration system for the hyperspectral images can automatically complete wave band registration of the hyperspectral images of the unmanned aerial vehicle, so that all wave bands of the hyperspectral images of the unmanned aerial vehicle have space consistency, the processing speed is high, and the registration precision is high.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive various changes or substitutions within the technical scope of the present invention, and these should be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (9)
1. A wave band registration method of a hyperspectral image is characterized by comprising the following steps:
step S100, selecting a certain wave band as a reference wave band, then expanding M pixels on the upper side, the lower side, the left side and the right side of an image of the wave band respectively to generate a new image as the reference image, wherein the value of M is determined by the maximum offset between other wave bands and the reference wave band, and the value of M is greater than the maximum offset;
step S200, extracting feature points on a wave band image to be registered, and extracting a template window of each feature point by taking the feature points as centers;
step S300, selecting the same coordinates as the characteristic points on the reference image as the center for each characteristic point, extracting a search window, and obtaining a coarse matching point by using a template matching method;
step S400, establishing a rough transformation relation between the band image to be registered and a reference image based on the rough matching points, calculating the matching points of the feature points on the reference image according to the rough transformation relation for each feature point, extracting a search window by taking the matching points as the center, and obtaining fine matching points by using a template matching method;
step S500, establishing a precise transformation relation between the to-be-registered band image and the reference image based on the precise matching point, and then completing the correction of the to-be-registered band image by using the precise transformation relation;
and S600, repeating the steps S200-S500 to finish the registration of the next wave band.
2. The band registration method according to claim 1, wherein before extracting the feature points, the enhancement operation of highlighting contrast is performed on the band image to be registered.
3. The band registration method according to claim 1, wherein the coarse matching points in step S400 are coarse matching points after the mismatching points are removed by using a random sampling consistency method and a least square method, and the fine matching points in step S500 are fine matching points after the mismatching points are removed by using a random sampling consistency method and a least square method.
4. The band registration method according to claim 1, wherein after each band is registered, an offset of the band with respect to a reference band is calculated, and a search window is extracted using coordinates of the feature point plus the offset as a center for a next band in step S300.
5. The band registration method of claim 1, wherein in step S200, a method is used
Or SIFT or Harris or SURF as an operator for extracting feature points.
6. A waveband registration system for hyperspectral images, comprising:
the reference image generation module is used for selecting a certain wave band as a reference wave band, then expanding M pixels on the upper side, the lower side, the left side and the right side of an image of the wave band to generate a new image as a reference image, wherein the value of M is determined by the maximum offset between other wave bands and the reference wave band, and the value of M is greater than the maximum offset;
the template window extraction module is used for extracting feature points on the wave band image to be registered and extracting a template window of each feature point by taking the feature points as centers;
the rough matching module is used for selecting the same coordinates as the characteristic points on the reference image as the center for each characteristic point, extracting a search window and obtaining rough matching points by using a template matching method;
a fine matching module used for establishing a coarse transformation relation between the band image to be registered and the reference image based on the coarse matching points, calculating the matching points of the feature points on the reference image according to the coarse transformation relation for each feature point, extracting a search window by taking the matching points as the center, and obtaining fine matching points by using a template matching method
And the geometric correction module is used for establishing a precise transformation relation between the band image to be registered and the reference image based on the precise matching points, and performing geometric correction on each feature point by using a polynomial model according to the precise transformation relation.
7. The waveband registration system of claim 6, further comprising an image enhancement module configured to perform a contrast-highlighting enhancement operation on the waveband image to be registered before extracting the feature points.
8. The band registration system of claim 6, further comprising a mis-match point culling module to cull mis-match points using a random sampling consistency method and a least squares method.
9. The band registration system of claim 6, further comprising an offset calculation module to calculate an offset of a band relative to a reference band after registration of the band.
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| CN112598717B (en) * | 2020-12-14 | 2024-05-17 | 珠海欧比特卫星大数据有限公司 | Full spectrum registration method and medium for hyperspectral satellite images |
| CN113658063B (en) * | 2021-07-28 | 2023-05-26 | 中国科学院西安光学精密机械研究所 | Automatic data correction method and system for AOTF type spectrum imager |
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