CN113850734A - Poisson equation fused remote sensing image automatic color homogenizing method - Google Patents
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Abstract
The invention discloses an automatic color homogenizing method for remote sensing images fused with Poisson's equation, which comprises the following steps: acquiring a remote sensing image, and preprocessing the remote sensing image; automatically selecting a first remote sensing image and a second remote sensing image from the preprocessed remote sensing images, wherein the first remote sensing image and the second remote sensing image are intersected to form an overlapping area, and the overlapping area comprises a plurality of intersection points; and according to the overlapping area and the plurality of intersection points, performing color homogenization on the first remote sensing image and the second remote sensing image through a Poisson equation. Compared with the traditional color homogenizing method and the mosaic line eliminating method, the method can be suitable for mosaic lines with any shapes and topologies, and can realize true seamless low distortion on the basis of ensuring the normalization precision of relative radiation.
Description
Technical Field
The invention belongs to the field of remote sensing image processing, and particularly relates to an automatic color homogenizing method for a remote sensing image fused with a Poisson equation.
Background
With the rapid development of remote sensing technology, large-scale and high-precision regional monitoring based on remote sensing satellite images is widely applied to various industry fields. Due to the limitation of imaging width, mechanism and cloud coverage, a single remote sensing image cannot cover the whole monitoring area, and multiple remote sensing images need to be spliced.
Remote sensing image splicing, also called remote sensing image mosaic, is a technical process of splicing two or more images with a certain overlapping degree together to form an integral image. Due to the influences of factors such as imaging time, an imaging sensor, an illumination effect, atmospheric attenuation and the like, the radiation levels of remote sensing images are different, so that the radiation distortion of the same object is caused, and the tone difference of ground objects among the images is large. Relative radiation normalization among a plurality of remote sensing images can realize hue difference among the images, and is important work for remote sensing image splicing.
The relative radiation normalization method of the remote sensing image can improve the hue difference between images, but on the mosaic line, post-processing is still needed to realize smooth transition at the mosaic line. Meanwhile, in a complex scene with a large number of remote sensing images, the images have different cross modes or extreme topological relations (cross areas are small), and the removal of the mosaic lines by using the method is more difficult. It can be said that the elimination of the mosaic lines directly affects the image stitching effect, and the manual post-processing of the mosaic lines consumes a lot of manpower and time costs.
The above is only for the purpose of assisting understanding of the technical aspects of the present invention, and does not represent an admission that the above is prior art.
Disclosure of Invention
The invention provides an automatic color homogenizing method for remote sensing images fused with a Poisson equation, which can solve the problems in the prior art.
In order to achieve the technical purpose, the technical scheme adopted by the invention is as follows:
the invention provides an automatic color homogenizing method for remote sensing images fused with Poisson's equation, which comprises the following steps:
s1: acquiring a remote sensing image, and preprocessing the remote sensing image;
s2: automatically selecting a first remote sensing image and a second remote sensing image from the preprocessed remote sensing images, wherein the first remote sensing image and the second remote sensing image are intersected to form an overlapping area, and the overlapping area comprises a plurality of intersection points;
s3: and according to the overlapping area and the plurality of intersection points, performing color homogenization on the first remote sensing image and the second remote sensing image through a Poisson equation.
Further, the S3 includes:
s31: generating mosaic lines according to the intersection points and the overlapping regions, and obtaining mosaic line regions after the overlapping regions are optimized based on the mosaic lines;
s32: constructing a target space omega according to the mosaic line, the color distribution of the first remote sensing image in the mosaic line region and the color distribution of the second remote sensing image in the mosaic line region, wherein the target space omega is composed of a boundary space delta omega and a boundary space omega0Composition of the space omega in the boundary0Composed of one or more pixels, the space omega in the boundary0The gradient field v in the color matching is an optimized gradient field obtained by a first color homogenizing method;
s33: fitting the space in the boundary Ω by Poisson's equation0The pixel value of (2) is calculated to obtain an optimized pixel value, and the optimized pixel value is taken as the space omega in the boundary0The remote sensing image is automatically homogenized by the pixel value of the image sensor.
Further, the poisson equation is as follows:
Δf＝divυoverΩwith f|δΩ＝f*|δΩ
in the formula, f is a pixel value in the target space omega, f is a pixel value outside the target space omega, and v is an optimized gradient field obtained by the first color homogenizing method.
Further, the first color homogenizing method includes a histogram matching method and a Wallis filtering method.
Further, the boundary space δ Ω is composed of a mosaic line and a boundary line of the color distribution of the second remote sensing image in the mosaic line region.
Further, the preprocessing comprises orthorectification, geometric rectification and radiation rectification.
Further, the pre-processing also includes atmospheric correction.
Further, the step of automatically selecting the first remote sensing image and the second remote sensing image from the preprocessed remote sensing images comprises:
selecting the neighborhood gray level average value of the preprocessed remote sensing image as a spatial characteristic quantity of gray level distribution, and forming a characteristic binary group by the spatial characteristic quantity and the pixel gray level of the preprocessed remote sensing image;
calculating the two-dimensional entropy of the preprocessed discrete image of the remote sensing image by a discrete image two-dimensional entropy method;
the formula of the discrete image two-dimensional entropy is as follows:
in the formula, H is a two-dimensional entropy of the discrete image, and i is a spatial characteristic quantity of gray distribution; j is the pixel gray scale of the remote sensing image, f (i, j) is the frequency of appearance of the characteristic binary group (i, j), and N is the scale of the remote sensing image;
and selecting the remote sensing image with large two-dimensional entropy of the discrete image as a first remote sensing image, and selecting the remote sensing image with small two-dimensional entropy of the discrete image as a second remote sensing image.
The invention also comprises the step of carrying out automatic color matching on the uniform color image obtained in the step S3 and the reference color image to obtain a uniform color image result meeting the corresponding color requirement.
The invention has the beneficial effects that: the invention discloses an automatic color homogenizing method for fusing a Poisson equation in remote sensing image splicing, which comprises the steps of obtaining a remote sensing image and preprocessing the remote sensing image; automatically selecting a first remote sensing image and a second remote sensing image from the preprocessed remote sensing images, wherein the first remote sensing image and the second remote sensing image are intersected to form an overlapping area, and the overlapping area comprises a plurality of intersection points; and according to the overlapping area and the plurality of intersection points, performing color homogenization on the first remote sensing image and the second remote sensing image through a Poisson equation. Compared with the traditional color homogenizing method and the mosaic line eliminating method, the method can be suitable for mosaic lines with any shapes and topologies, can realize real seamless low distortion on the basis of ensuring the normalization precision of relative radiation, is fully automatic, and saves a large amount of labor and time cost.
Drawings
FIG. 1 is a schematic flow chart of a first embodiment of the method for automatically homogenizing colors of remote sensing images by fusing Poisson's equation;
fig. 2(a) is a schematic diagram of a first remote sensing image region;
fig. 2(b) is a schematic diagram of a second remote sensing image region;
FIG. 2(c) is a schematic view of the overlap region;
FIG. 3 is a schematic view of the composition of the target space;
FIG. 4(a) is a schematic diagram of a remote sensing image before automatic color equalization according to an embodiment of the present invention;
FIG. 4(b) is a schematic diagram of a remote sensing image after automatic color equalization according to an embodiment of the present invention;
FIG. 5(a) is a partial schematic view of a remote sensing image before automatic color equalization according to an embodiment of the present invention;
fig. 5(b) is a schematic partial view of a remote sensing image after automatic color equalization according to an embodiment of the present invention.
The implementation, functional features and advantages of the objects of the present invention will be further explained with reference to the accompanying drawings.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of an automated color-homogenizing method for fusion poisson fusion in remote sensing image stitching, which includes steps S1-S3.
Step S1 is to obtain remote sensing image and to preprocess the remote sensing image.
The remote sensing image preprocessing comprises orthorectification, geometric rectification, radiometric calibration and atmospheric rectification, wherein the image is a panchromatic image or a multispectral image:
(1) orthorectification and geometric correction guarantee the correctness of the spatial position of the remote sensing image to be spliced;
(2) the radiometric calibration converts DN value into apparent reflectivity, converts the brightness gray value of the remote sensing image into absolute radiance, and has the formula as follows:
Lλ＝k*DN+c (1)
wherein L isλIs the radiance value of the band λ, dn (digital number) records the gray value of the feature, k and c are the gain and offset, respectively.
The atmosphere correction is to convert the absolute radiance information into the earth surface reflectivity and eliminate the errors caused by atmosphere scattering, absorption and reflection. The atmospheric correction adopts a 6S atmospheric radiation transmission model. The atmospheric radiation transmission model 6S adopts a most similar and continuous scattering SOS (successful Order of Scattering) method to solve a radiation transmission equation.
Step S2, automatically selecting a first remote sensing image and a second remote sensing image from the preprocessed remote sensing images, wherein the first remote sensing image and the second remote sensing image are intersected to form an overlapping area, and the overlapping area comprises a plurality of intersection points.
Screening the first remote sensing image and the second remote sensing image by using a discrete image two-dimensional entropy method, wherein the discrete image two-dimensional entropy is larger than that of the first remote sensing image and is used as a reference image; and the discrete image with small two-dimensional entropy is a second remote sensing image which is used as an image to be homogenized. As a result, as shown in fig. 2, fig. 2(a) shows a first remote sensing image region, i.e., a reference image; fig. 2(b) is a second remote sensing image, i.e. an image area to be homogenized; fig. 2(c) is a schematic view of the overlapping region.
The specific process of selecting the first remote sensing image and the second remote sensing image is as follows:
(1) selecting the neighborhood gray level average value of the preprocessed remote sensing image as a spatial characteristic quantity of gray level distribution, and forming a characteristic binary group by the spatial characteristic quantity and the pixel gray level of the preprocessed remote sensing image;
(2) calculating the probability of a certain gray level appearing in the remote sensing image according to the spatial characteristic quantity of the gray level distribution and the pixel gray level of the remote sensing image:
wherein Pij is the probability of a certain gray level appearing in the remote sensing image, and i is the spatial characteristic quantity of gray level distribution; j is the pixel gray scale of the remote sensing image, f (i, j) is the frequency of the appearance of the characteristic binary group (i, j), and N is the scale of the remote sensing image.
(3) Constructing a discrete image two-dimensional entropy according to the probability of a certain gray level appearing in the remote sensing image:
wherein H is the two-dimensional entropy of the discrete image.
(4) And selecting the first remote sensing image with the large two-dimensional entropy of the discrete image and the second remote sensing image with the small two-dimensional entropy of the discrete image.
And step S3, according to the overlapping area and the plurality of intersection points, homogenizing the first remote sensing image and the second remote sensing image through a Poisson equation.
S31: and generating mosaic lines according to the intersection points and the overlapping regions, wherein the overlapping regions are optimized based on the mosaic lines to obtain mosaic line regions.
(1) Acquiring an intersection set I ═ I where the overlapping region and the reference image intersect1,I2,...，In}；
(2) Obtaining the connection sequence C of each intersection point In the intersection point set I according to the sequence of the intersection points In on the intersection line of the overlapping region and the reference image1,C2,...，Cn-1}；
(3) Establishing a buffer area B ═ B on the overlapping area according to the connection sequence C1,B2,...，Bn-1In which BtIs CtA corresponding cache region, t ∈ (1, 2.. n-1);
(4) in each buffer BtAccording to BtAnd constructing a loss function according to the color difference of each pixel of the upper reference image and the image to be color-homogenized, and solving the optimal mosaic line path according to the loss function. The constructed chromatic aberration construction loss function is as follows:
wherein the content of the first and second substances,
the loss values of the pixels (x, y) on the reference image and the image to be homogenized,
is defined as:
wherein k is the number of bands of the image,
is the color value at the reference image (x, y),
to be color-homogenizedColor values at the image (x, y).
S32: constructing a target space omega according to the mosaic line, the color distribution of the first remote sensing image in the mosaic line region and the color distribution of the second remote sensing image in the mosaic line region, wherein the target space omega is composed of a boundary space delta omega and a boundary space omega0Composition of the space omega in the boundary0Composed of one or more pixels, the space omega in the boundary0The gradient field v in (1) is the gradient field optimized by the first color homogenizing method.
The target space omega is composed of a boundary space delta omega and a boundary space omega00 composition, see fig. 3, boundary space δ Ω or boundary 31, and boundary space Ω00 is 32. Space omega in the boundary0An optimization space is obtained after optimization by a histogram and other methods. Optimized boundary inner space omega0The noise immunity to water, cloud and change abnormal response can be improved, and simultaneously the gradient field value can be changed to the maximum extent, and the color homogenizing effect is improved.
S33: fitting the space in the boundary Ω by Poisson's equation0The pixel value of (2) is calculated to obtain an optimized pixel value, and the optimized pixel value is taken as omega0The remote sensing image is automatically homogenized by the pixel value of the image sensor.
The poisson equation is as follows:
Δf＝divυoverΩwith f|δΩ＝f*|δΩ (6)
wherein f is a pixel value in the target space omega, f is a pixel value outside the target space omega, and v is an optimized gradient field obtained by the first color homogenizing method.
Fig. 4(a) is a schematic diagram of a remote sensing image before automatic color homogenization according to an embodiment of the present invention, and fig. 5(a) is a schematic diagram of a partial enlargement of fig. 4 (a). Fig. 4(b) is a schematic diagram of a remote sensing image after automatic color homogenizing according to an embodiment of the present invention, and fig. 5(b) is a schematic diagram of a partial enlargement of fig. 4 (b).
Claims (8)
1. A remote sensing image automatic color homogenizing method fused with a Poisson equation is characterized by comprising the following steps:
s1: acquiring a remote sensing image, and preprocessing the remote sensing image;
s2: automatically selecting a first remote sensing image and a second remote sensing image from the preprocessed remote sensing images, wherein the first remote sensing image and the second remote sensing image are intersected to form an overlapping area, and the overlapping area comprises a plurality of intersection points;
s3: and according to the overlapping area and the plurality of intersection points, performing color homogenization on the first remote sensing image and the second remote sensing image through a Poisson equation.
2. The method for automatically homogenizing remote sensing images fused with Poisson' S equation according to claim 1, wherein the S3 includes:
s31: generating mosaic lines according to the intersection points and the overlapping regions, and obtaining mosaic line regions after the overlapping regions are optimized based on the mosaic lines;
s32: constructing a target space omega according to the mosaic line, the color distribution of the first remote sensing image in the mosaic line region and the color distribution of the second remote sensing image in the mosaic line region, wherein the target space omega is composed of a boundary space delta omega and a boundary space omega0Composition of the space omega in the boundary0Composed of one or more pixels, the space omega in the boundary0The gradient field v in the color matching is an optimized gradient field obtained by a first color homogenizing method;
s33: fitting the space in the boundary Ω by Poisson's equation0The pixel value of (2) is calculated to obtain an optimized pixel value, and the optimized pixel value is taken as the space omega in the boundary0The remote sensing image is automatically homogenized by the pixel value of the image sensor.
3. The automatic color-homogenizing method for the remote sensing image fused with the poisson equation, as claimed in claim 1, wherein the poisson equation is as follows:
Δf＝div v overΩwithf|δΩ＝f*|δΩ
in the formula, f is a pixel value in the target space omega, f is a pixel value outside the target space omega, and v is an optimized gradient field obtained by the first color homogenizing method.
4. The automatic color-homogenizing method for the remote sensing image fused with the Poisson equation, as recited in claim 1, wherein the first color-homogenizing method comprises a histogram matching method and a Wallis filtering method.
5. The automatic color-homogenizing method for the remote sensing image fused with the Poisson equation as claimed in claim 1, wherein the boundary space δ Ω is composed of a mosaic line and a boundary line of the color distribution of the second remote sensing image in the mosaic line region.
6. The method for automatically homogenizing the remote sensing images fused with the Poisson equation, according to claim 1, wherein the preprocessing comprises orthorectification, geometric rectification and radiation rectification.
7. The method for automatically homogenizing remote sensing images fused with Poisson's equation according to claim 6, wherein the preprocessing further comprises atmospheric correction.
8. The automatic color homogenizing method for fusing the Poisson equation in the remote sensing image splicing according to any one of claims 1 to 7, wherein the step of automatically selecting the first remote sensing image and the second remote sensing image from the preprocessed remote sensing images comprises the following steps:
selecting the neighborhood gray level average value of the preprocessed remote sensing image as a spatial characteristic quantity of gray level distribution, and forming a characteristic binary group by the spatial characteristic quantity and the pixel gray level of the preprocessed remote sensing image;
calculating the two-dimensional entropy of the preprocessed discrete image of the remote sensing image by a discrete image two-dimensional entropy method;
the formula of the discrete image two-dimensional entropy is as follows:
in the formula, H is a two-dimensional entropy of the discrete image, and i is a spatial characteristic quantity of gray distribution; j is the pixel gray scale of the remote sensing image, f (i, j) is the frequency of appearance of the characteristic binary group (i, j), and N is the scale of the remote sensing image;
and selecting the remote sensing image with large two-dimensional entropy of the discrete image as a first remote sensing image, and selecting the remote sensing image with small two-dimensional entropy of the discrete image as a second remote sensing image.
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