CN115988334B - Self-correcting digital camera mobile remote sensing system and method - Google Patents
Self-correcting digital camera mobile remote sensing system and method Download PDFInfo
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Abstract
The application provides a self-correcting digital camera mobile remote sensing system and a method, which are applied to the technical field of image data processing, wherein the method comprises the following steps: acquiring an image to be corrected; establishing a remote sensing camera response model, and obtaining an output value of the corrected image pixel according to the sunlight spectrum power distribution and the atmospheric layer radiation spectrum distribution; performing remote sensing automatic classification on the image to be corrected, and selecting a remote sensing digital image of a preset area according to the classification of the image to be corrected; performing color space conversion aiming at the classification result, converting the image to be corrected from an RGB color space to an LMS color space, and performing offset elimination on the image to be corrected by converting the image to be corrected from the LMS color space to a logarithmic LMS color space; performing linear model brightness and color correction on the corresponding types; converting the corrected image into an RGB color space and outputting a corrected image; the color self-correction of the image is realized, and the quality of the color remote sensing image is improved rapidly and effectively.
Description
Technical Field
The present disclosure relates to the field of image data processing technologies, and in particular, to a self-correcting digital camera mobile remote sensing system and method.
Background
At present, remote sensing has become a main means of large-area real-time geosynchronous observation, but besides the influence of sun, atmospheric conditions and satellite parameters, seasonal variation of vegetation features also can cause color and brightness differences between different remote sensing images. These differences not only have a great influence on the subsequent image classification and change detection, but also have difficulty in remote sensing image stitching. The true color remote sensing image is synthesized by red, green and blue wave bands, the color of the shot object is displayed as faithfully as possible, and the true color remote sensing image provides more information than the gray level image, but the image shot by the current camera has the following problems:
(1) The depth information acquired by the camera has a certain error due to the limitation of imaging conditions and the interference of external environment, and the quality of the image is affected;
(2) When the acquired image is corrected, manual operation is needed, the efficiency is low, the use is limited under the condition of strict requirement on the correction time, and automatic correction cannot be realized.
Reference patent application number CN202010298041.9 discloses a single-camera polar line correction method and device, the specific contents of which are: collecting flat plate images projected with speckles at different distances by using a camera and a speckle projector which face the same direction and have unchanged relative positions, and obtaining a first plane speckle image and a second plane speckle image; matching the first plane speckle image and the second plane speckle image through an image matching algorithm to obtain sub-pixel matching points; obtaining a mapping matrix between the two physical coordinates according to the physical coordinates of the sub-pixel matching points corresponding to the first plane speckle image and the physical coordinates corresponding to the second plane speckle image; obtaining a direction vector of the projector center in a camera reference system according to the mapping matrix; adjusting the coordinate axis direction of a camera reference system to align the horizontal axis direction with the direction vector, and updating the imaging matrix of the camera; and mapping the target scene image through the imaging matrix to obtain an epipolar corrected image.
The prior art obtains corrected images through technologies such as an image matching algorithm, an imaging matrix and the like, but a correction method is not applicable to a dual-camera system, errors exist in the correction process, and correction of color images is not considered, so that a self-correction digital camera mobile remote sensing system and a self-correction digital camera mobile remote sensing method are provided.
Disclosure of Invention
The present application provides a self-correcting digital camera mobile remote sensing system and method, which aims to solve the problems that errors exist in the correction process and the correction of color images is not considered.
In order to achieve the above purpose, the present application provides the following technical solutions:
the application provides a self-correcting digital camera mobile remote sensing method, which comprises the following steps:
s1: acquiring an image to be corrected, and identifying an error area of the image to be corrected to obtain an error point;
s2: establishing a remote sensing camera response model, and obtaining an output value of the corrected image pixel according to the sunlight spectrum power distribution and the atmospheric layer radiation spectrum distribution, wherein the model is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein when->
The unit function indicates that the camera response model is linear, and the unit function is a function subjected to A/D conversion of the remote sensing camera>
Is a monotonic function increasing nonlinear function and indicates that the camera response model is nonlinear, +.>
For the spectral power distribution of the incident light, +.>
Is light ofWavelength of spectral color, < >>
The sensitivity of the red, green and blue spectrum of the camera, noise is noise, and k is the constant of exposure time; when the solar spectral power distribution and the atmospheric radiation spectral distribution are uniformly distributed in space, the model is: />
The method comprises the steps of carrying out a first treatment on the surface of the Wherein the method comprises the steps of
For irradiance of sunlight>
For the transmissivity of the atmosphere in the direction of the remote sensing camera, < ->
H is the elevation of the remote sensing camera, which is the depression angle and azimuth angle +.>
Layer emittance for atmospheric path, k is a constant of exposure time, +.>
For light sensitivity, noise is noise;
s3: the method comprises the steps of implementing remote sensing automatic classification on an image to be corrected, selecting a remote sensing digital image of a preset area according to the classification of the image to be corrected, acquiring a characteristic formulation classification system of the remote sensing digital image, establishing supervision classification in the classification system, training the characteristic, classifying pixels with the same characteristic by adopting a clustering method if the characteristic is not in the supervision classification, and measuring the characteristic;
s4: performing color space conversion aiming at the classification result, converting the image to be corrected from RGB color space to LMS color space, performing offset elimination on the image to be corrected by converting the LMS color space to logarithmic LMS color space, and finally converting the image to be corrected from logarithmic LMS color space to LMS color space
A color space;
performing linear model brightness and color correction on the corresponding types, and respectively calculating
The mean and variance of each type in the image to be corrected after the color space transformation are as follows:
s5: and converting the corrected image into an RGB color space after correction, and outputting a corrected image.
Further, the step of obtaining the image to be corrected, and identifying the error area of the image to be corrected to obtain the error point includes:
and automatically removing the stripe noise of the image to be corrected, wherein the formula is as follows:
Further, after S2, the method includes:
and carrying out geometric fine correction on the image to be corrected, establishing original image coordinates and transformed coordinates, calculating corresponding original coordinates through the central position of each transformed image pixel, judging that the pixel point of the transformed coordinates is not in the center of the original image pixel when the pixel point of the transformed coordinates is not in the original image coordinate system, calculating coordinates in the original image corresponding to each point in the geometric corrected image obtained after correction, calculating according to line point by point in the calculation process, and carrying out next line calculation after each line is ended.
Further, the process of performing geometric fine correction on the image to be corrected includes establishing a corresponding relationship between an original image and a geometric correction image, where the formula is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein x and y are original image coordinates, u and v are transformed image coordinates, and are integers, < >>
Respectively are provided withThe abscissa of 6 known corresponding points.
Further, the step of color correction is as follows:
storing a reflection spectrum distribution set of the color card in a database, and calculating tristimulus values according to the spectrum reflectivity of the color card to obtain a color true value; calculating a remote sensing value of the spectral reflectivity of a preset color card by using the camera response model; and mapping the 3 color components of the image to be corrected onto the 3 color components of the color chart, describing the characteristics of the remote sensing camera by using a linear model to obtain a corrected color image, and carrying out color correction again when the spectrum of the light source and the atmospheric parameters change.
Further, after S4, the method includes:
eliminating standard deviations of various types of images to be corrected, calculating standard deviations of various pixels on corresponding types to obtain distance weight parameters, and multiplying the reverse normalized distance weight parameters by correction values of various types by using a reverse normalization weight method to obtain final pixel values of corresponding pixels.
Further, the clustering method classifies pixels with the same characteristics by adopting a K-means clustering method, and supposedly classifies an image to be corrected into m types, calculates the gravity center of each type and takes the gravity center as an initial center of the m types; in each iteration, any sample is assigned to the corresponding type.
The application also provides a self-correcting digital camera mobile remote sensing system, comprising:
the acquisition module is used for: acquiring an image to be corrected, and identifying an error area of the image to be corrected to obtain an error point;
and (3) a building module: establishing a remote sensing camera response model, and obtaining an output value of the corrected image pixel according to the sunlight spectrum power distribution and the atmospheric layer radiation spectrum distribution, wherein the model is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein when->
Indicating camera as unit functionThe response model is linear, and the unit function is a function subjected to A/D conversion of the remote sensing camera,/->
Is a monotonic function increasing nonlinear function and indicates that the camera response model is nonlinear, +.>
For the spectral power distribution of the incident light, +.>
Is the wavelength of the spectral color,
the sensitivity of the red, green and blue spectrum of the camera, noise is noise, and k is the constant of exposure time; when the solar spectral power distribution and the atmospheric radiation spectral distribution are uniformly distributed in space, the model is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein->
For irradiance of sunlight>
For the transmissivity of the atmosphere in the direction of the remote sensing camera, < ->
H is the elevation of the remote sensing camera, which is the depression angle and azimuth angle +.>
Layer emittance for atmospheric path, k is a constant of exposure time, +.>
For light sensitivity, noise is noise.
And a classification module: the method comprises the steps of implementing remote sensing automatic classification on an image to be corrected, selecting a remote sensing digital image of a preset area according to the classification of the image to be corrected, acquiring a characteristic formulation classification system of the remote sensing digital image, establishing supervision classification in the classification system, training the characteristic, classifying pixels with the same characteristic by adopting a clustering method if the characteristic is not in the supervision classification, and measuring the characteristic;
and a correction module: performing color space conversion aiming at the classification result, converting the image to be corrected from RGB color space to LMS color space, performing offset elimination on the image to be corrected by converting the LMS color space to logarithmic LMS color space, and finally converting the image to be corrected from logarithmic LMS color space to LMS color space
A color space;
performing linear model brightness and color correction on the corresponding types, and respectively calculating
The mean and variance of each type in the image to be corrected after the color space transformation are as follows:
and an output module: and converting the corrected image into an RGB color space after correction, and outputting a corrected image.
The application provides a self-correcting digital camera mobile remote sensing system and a self-correcting digital camera mobile remote sensing method, which have the following beneficial effects:
(1) According to the invention, a reflection spectrum distribution set of the color card is stored in a database, and a tristimulus value is calculated according to the spectrum reflectivity of the color card, so that a color true value is obtained; calculating a remote sensing value of the spectral reflectivity of a preset color card by using a camera response model; mapping 3 color components of an image to be corrected onto 3 color components of a color chart, describing the characteristics of a remote sensing camera by using a linear model to obtain a corrected color image, wherein the color correction method can change a color correction matrix along with the change of conditions, and has the advantages of good flexibility and high correction efficiency;
(2) The invention comprehensively uses the technical means such as a remote sensing camera response model, an iterative optimization algorithm and the like, solves the problem that the depth information acquired by the camera has a certain error, prevents the limitation of imaging conditions and the interference of external environment, has stronger universality and practicability, ensures the image correction precision, saves the cost and can meet the self-correction requirement of various remote sensing cameras;
(3) The invention classifies the images to be corrected according to the number of peaks in the histogram, adopts an unsupervised classification algorithm, firstly selects a plurality of samples as clustering centers, gathers the rest samples towards each center according to a minimum distance criterion, thereby obtaining initial clustering, and carries out color space conversion after obtaining a classification result of the images to be corrected, thereby realizing the self-correction of the colors of the images, rapidly and effectively improving the quality of the color remote sensing images, ensuring uniform brightness and clear details.
Drawings
FIG. 1 is a schematic flow chart of a self-correcting digital camera mobile remote sensing method according to an embodiment of the present application;
fig. 2 is a block diagram of a self-correcting digital camera mobile remote sensing system according to an embodiment of the present application.
The implementation, functional features and advantages of the present application will be further described with reference to the accompanying drawings in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
The following description of the embodiments of the present application will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, are intended to be within the scope of the present application.
Referring to fig. 1, a flow chart of a self-correcting digital camera mobile remote sensing method is provided;
the self-correcting digital camera mobile remote sensing method provided by the application comprises the following steps:
s1: acquiring an image to be corrected, and identifying an error area of the image to be corrected to obtain an error point; and automatically removing the stripe noise of the image to be corrected, wherein the formula is as follows:
In this step, since the remote sensing image is easily affected by the atmosphere during imaging, different atmospheric conditions may affect the brightness and color distribution of the image, thereby resulting in subsequent correction deviation; when imaging, the scanning line is dropped due to the fault on a certain scanning line of the detection system, no information is usually generated at this time, only one black line is displayed on the image, and segmented black lines sometimes appear, which are called stripe noise.
S2: establishing a remote sensing camera response model, and obtaining an output value of the corrected image pixel according to the sunlight spectrum power distribution and the atmospheric layer radiation spectrum distribution, wherein the model is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein when->
The unit function indicates that the camera response model is linear, and the unit function is a function subjected to A/D conversion of the remote sensing camera>
Is a monotonic function increasing nonlinear function and indicates that the camera response model is nonlinear, +.>
For the spectral power distribution of the incident light, +.>
Wavelength of spectral color, < >>
The sensitivity of the red, green and blue spectrum of the camera, noise is noise, and k is the constant of exposure time; when the solar spectral power distribution and the atmospheric radiation spectral distribution are uniformly distributed in space, the model is: />
The method comprises the steps of carrying out a first treatment on the surface of the Wherein the method comprises the steps of
For irradiance of sunlight>
For the transmissivity of the atmosphere in the direction of the remote sensing camera, < ->
H is the elevation of the remote sensing camera, which is the depression angle and azimuth angle +.>
Layer emittance for atmospheric path, k is a constant of exposure time, +.>
For light sensitivity, noise is noise; and carrying out geometric fine correction on the image to be corrected, establishing original image coordinates and transformed coordinates, calculating corresponding original coordinates through the central position of each transformed image pixel, judging that the pixel point of the transformed coordinates is not in the center of the original image pixel when the pixel point of the transformed coordinates is not in the original image coordinate system, calculating coordinates in the original image corresponding to each point in the geometric corrected image obtained after correction, calculating according to line point by point in the calculation process, and carrying out next line calculation after each line is ended.
In the step, in the remote sensing process, sunlight consisting of direct sunlight through the atmosphere and scattered light of sky irradiates on a ground object together, is reflected back to the atmosphere by the ground object, and finally reaches a remote sensing camera; the method comprises the steps of carrying out geometric fine correction on an image to be corrected, wherein the image before correction is formed by equidistant pixel points with regular rows and columns, but actually, due to certain geometric distortion, the ground distances corresponding to the pixel points in the image are not equal, the corrected image is formed by equidistant grid points, and the ground is used as a standard, so that the image meets the uniform distribution of certain projection, the final purpose of correction is to determine the row and column values of the corrected image, and then find the brightness value of each pixel in a new image; the geometric fine correction resampling method comprises the following steps: establishing a relation between original image coordinates (x, y) and transformed image coordinates (u, v), and calculating original image coordinate points (x, y) through the central position (u represents the number of rows and v represents the number of columns, and are integers) of each transformed image pixel; analysis shows that the pixel points of the integer (u, v) are generally not located on the integer (x, y) points in the original image coordinate system, namely are not located at the centers of the pixels of the original image, the positions (x, y) in the original image corresponding to each point in the corrected image are calculated, the calculation is carried out point by point according to lines, and the next line calculation is carried out after each line is ended until the whole image is ended.
S3: and carrying out remote sensing automatic classification on the image to be corrected, selecting a remote sensing digital image of a preset area according to the classification of the image to be corrected, acquiring the characteristics of the remote sensing digital image to formulate a classification system, establishing supervision classification in the classification system, training the characteristics, classifying pixels with the same characteristics by adopting a clustering method if the characteristics are not in the supervision classification, and measuring the characteristics.
In the step, classifying the image to be corrected according to the number of peaks in the histogram, adopting an unsupervised classification algorithm, firstly selecting a plurality of samples as clustering centers, aggregating the rest samples towards each center according to a minimum distance criterion, thus obtaining an initial cluster, judging whether the initial cluster result meets the requirements, if not, splitting and merging the clustering set to obtain a new cluster center, determining the clustering center through iterative operation of sample mean values, judging whether the clustering result meets the requirements, and repeating the iteration until the clustering division operation is completed.
S4: performing color space conversion aiming at the classification result, converting the image to be corrected from RGB color space to LMS color space, performing offset elimination on the image to be corrected by converting the LMS color space to logarithmic LMS color space, and finally converting the image to be corrected from logarithmic LMS color space to LMS color space
A color space;
will correspond to the type lineCorrection of brightness and color of the sexual model, respectively, calculation of
The mean and variance of each type in the image to be corrected after the color space transformation are as follows:
In this step, from the image to be corrected, the RGB color space is converted to the LMS color space, and the conversion formula is:
after the image is converted into the LMS color space, the statistical distribution of the converted image data has great deviation, and the LMS color space can be converted into the logarithmic LMS space to be eliminated, wherein the conversion formula is as follows:
finally, the image is converted from the logarithmic LMS color space to the following conversion formula
Color space, the conversion formula is:
s5: and converting the corrected image into an RGB color space after correction, and outputting a corrected image.
Referring to fig. 2, the present invention further provides a self-correcting digital camera mobile remote sensing system, comprising:
the acquisition module is used for: acquiring an image to be corrected, and identifying an error area of the image to be corrected to obtain an error point;
and (3) a building module: establishing a remote sensing camera response model, and obtaining an output value of the corrected image pixel according to the sunlight spectrum power distribution and the atmospheric layer radiation spectrum distribution, wherein the model is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein when->
The unit function indicates that the camera response model is linear, and the unit function is a function subjected to A/D conversion of the remote sensing camera>
Is a monotonic function increasing nonlinear function and indicates that the camera response model is nonlinear, +.>
For the spectral power distribution of the incident light, +.>
Is the wavelength of the spectral color,
the sensitivity of the red, green and blue spectrum of the camera, noise is noise, and k is the constant of exposure time; when the solar spectral power distribution and the atmospheric radiation spectral distribution are uniformly distributed in space, the model is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein->
For the irradiance of sunlight, the light is irradiated,
for the transmissivity of the atmosphere in the direction of the remote sensing camera, < ->
H is the elevation of the remote sensing camera, which is the depression angle and azimuth angle +.>
Layer emittance for atmospheric path, k is a constant of exposure time, +.>
For light sensitivity, noise is noise;
and a classification module: the method comprises the steps of implementing remote sensing automatic classification on an image to be corrected, selecting a remote sensing digital image of a preset area according to the classification of the image to be corrected, acquiring a characteristic formulation classification system of the remote sensing digital image, establishing supervision classification in the classification system, training the characteristic, classifying pixels with the same characteristic by adopting a clustering method if the characteristic is not in the supervision classification, and measuring the characteristic;
and a correction module: performing color space conversion on the classification result, and converting the image to be corrected from RGB color space to LMS color spacePerforming offset cancellation on an image to be corrected by converting an LMS color space into a logarithmic LMS color space, and finally converting the image to be corrected from the logarithmic LMS color space into
A color space;
performing linear model brightness and color correction on the corresponding types, and respectively calculating
The mean and variance of each type in the image to be corrected after the color space transformation are as follows:
and an output module: and converting the corrected image into an RGB color space after correction, and outputting a corrected image.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the claims, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the claims of the present application.
Although embodiments of the present application have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the application, the scope of which is defined in the appended claims and their equivalents.
Claims (7)
1. A method for self-correcting digital camera mobile remote sensing, comprising:
s1: acquiring an image to be corrected, and identifying an error area of the image to be corrected to obtain an error point;
s2: establishing a remote sensing camera response model, and obtaining an output value of the image pixel to be corrected according to the sunlight spectrum power distribution and the atmospheric layer radiation spectrum distribution, wherein the model is as follows:
wherein when F is a unit function indicating that the camera response model is linear, the unit function is a function after A/D conversion of the remote sensing camera, F is a monotonic function increasing nonlinear function indicating that the camera response model is nonlinear, I (m, n, lambda) is spectral power distribution of incident light, lambda is wavelength of spectral color, D i (m, n, λ), i=1, 2,3 is the red, green, blue spectral sensitivity of the camera, noise is noise, k is a constant of exposure time, respectively; when the solar spectral power distribution and the atmospheric radiation spectral distribution are uniformly distributed in space, the model is:
where E (lambda) is the solar irradiance,
for the transmissivity of the atmosphere in the direction of the remote sensing camera, θ +.>
For the depression and azimuth angle, h is the elevation of the remote sensing camera, P (λ) is the layer radiance of the atmospheric path, k is the constant of the exposure time, D i (lambda) is the light sensitivity, noise is the noise;
s3: the method comprises the steps of implementing remote sensing automatic classification on an image to be corrected, selecting a remote sensing digital image of a preset area according to the classification of the image to be corrected, acquiring a characteristic formulation classification system of the remote sensing digital image, establishing supervision classification in the classification system, training the characteristic, classifying pixels with the same characteristic by adopting a clustering method if the characteristic is not in the supervision classification, and measuring the characteristic;
s4: performing color space conversion aiming at the classification result, converting the image to be corrected from an RGB color space to an LMS color space, performing offset elimination on the image to be corrected by converting the LMS color space to a logarithmic LMS color space, and finally converting the image to be corrected from the logarithmic LMS color space to an lalpha beta color space;
performing linear model brightness and color correction on the corresponding types, and calculating the mean value and variance of each type in the image to be corrected after lalpha beta color space conversion, wherein the formula is as follows:
Y i (p)′＝Y i (p)-μ i1 ，
wherein mu i1 For the image to be corrected, i is the i-th type, Y i (p) pixel values after conversion of the ith type lαβ color space of the image to be corrected, Y i (p)' is the corrected i-th type image element value;
the color correction step is as follows:
storing a reflection spectrum distribution set of the color card in a database, and calculating tristimulus values according to the spectrum reflectivity of the color card to obtain a color true value; calculating a remote sensing value of the spectral reflectivity of a preset color card by using the camera response model; mapping the 3 color components of the image to be corrected onto the 3 color components of a color chart, describing the characteristics of the remote sensing camera by using a linear model to obtain a corrected color image, and carrying out color correction again when the spectrum of the light source and the atmospheric parameters change;
s5: and converting the corrected image into an RGB color space after correction, and outputting a corrected image.
2. The method for mobile remote sensing of a self-correcting digital camera according to claim 1, wherein the step of obtaining the image to be corrected, and identifying the error area of the image to be corrected, and obtaining the error point comprises:
and automatically removing the stripe noise of the image to be corrected, wherein the formula is as follows:
wherein g ij For the input gray values, M is the average value of the gray values of all pixel numbers in the image to be corrected, D is the standard deviation of the gray values of all pixel numbers in the image to be corrected, M i For the average value of the gray values of the pixel numbers on each scanning line, d i Standard deviation of pixel number gray values on each scanning line; if the ith row is a stripe and all pixels on the stripe are zero-order gray values, m is calculated i And d i Also zero value, calculated G ij The gray value of the (i) line is equal to the average value M of the gray values of all the pixels in the image to be corrected, namely, the calculated gray values of all the pixels in the (i) line are equal, and the (i) line is judged to be a strip, so that automatic noise removal is needed.
3. A method of self-correcting digital camera mobile telemetry as claimed in claim 1, wherein after S2, comprising:
and carrying out geometric fine correction on the image to be corrected, establishing original image coordinates and transformed coordinates, calculating corresponding original coordinates through the central position of each transformed image pixel, judging that the pixel point of the transformed coordinates is not in the center of the original image pixel when the pixel point of the transformed coordinates is not in the original image coordinate system, calculating coordinates in the original image corresponding to each point in the geometric corrected image obtained after correction, calculating according to line point by point in the calculation process, and carrying out next line calculation after each line is ended.
4. A method for mobile remote sensing of a self-correcting digital camera according to claim 3, wherein said performing a geometric fine correction on said image to be corrected comprises establishing a correspondence between an original image and a geometric corrected image, wherein the formula is:
x＝a 00 +a 10 u+a 01 v+a 11 uv+a 20 u 2 +a 02 v 2
y＝b 00 +b 10 u+b 01 v+b 11 uv+b 20 u 2 +b 02 v 2 ，
wherein x, y are original image coordinates, u, v are transformed image coordinates, and are integers, and a, b are respectively the horizontal and vertical coordinates of 6 known corresponding points.
5. A method of self-correcting digital camera mobile telemetry as claimed in claim 1, wherein after S4, comprising:
eliminating standard deviations of various types of images to be corrected, calculating standard deviations of various pixels on corresponding types to obtain distance weight parameters, and multiplying the reverse normalized distance weight parameters by correction values of various types by using a reverse normalization weight method to obtain final pixel values of corresponding pixels.
6. The method for mobile remote sensing of a self-correcting digital camera according to claim 1, wherein the clustering method is to classify pixels with the same characteristics by using a K-means clustering method, and the image to be corrected is assumed to be divided into m types, and the center of gravity of each type is calculated and is taken as the initial center of the m types; in each iteration, any sample is assigned to the corresponding type.
7. A self-correcting digital camera mobile remote sensing system, comprising:
the acquisition module is used for: acquiring an image to be corrected, and identifying an error area of the image to be corrected to obtain an error point;
and (3) a building module: establishing a remote sensing camera response model, and obtaining an output value of the image pixel to be corrected according to the sunlight spectrum power distribution and the atmospheric layer radiation spectrum distribution, wherein the model is as follows:
wherein when F is a unit function indicating that the camera response model is linear, the unit function is a function after A/D conversion of the remote sensing camera, F is a monotonic function increasing nonlinear function indicating that the camera response model is nonlinear, I (m, n, lambda) is spectral power distribution of incident light, lambda is wavelength of spectral color, D i (m, n, λ), i=1, 2,3 is the red, green, blue spectral sensitivity of the camera, noise is noise, k is a constant of exposure time, respectively; when the solar spectral power distribution and the atmospheric radiation spectral distribution are uniformly distributed in space, the model is:
where E (lambda) is the solar irradiance,
for the transmissivity of the atmosphere in the direction of the remote sensing camera, θ +.>
Is the depression angle and azimuth angle, h is the height of the remote sensing camera, and P (lambda) is the atmosphereLayer emittance of course, k is a constant of exposure time, D i (lambda) is the light sensitivity, noise is the noise;
and a classification module: the method comprises the steps of implementing remote sensing automatic classification on an image to be corrected, selecting a remote sensing digital image of a preset area according to the classification of the image to be corrected, acquiring a characteristic formulation classification system of the remote sensing digital image, establishing supervision classification in the classification system, training the characteristic, classifying pixels with the same characteristic by adopting a clustering method if the characteristic is not in the supervision classification, and measuring the characteristic;
and a correction module: performing color space conversion aiming at the classification result, converting the image to be corrected from an RGB color space to an LMS color space, performing offset elimination on the image to be corrected by converting the LMS color space to a logarithmic LMS color space, and finally converting the image to be corrected from the logarithmic LMS color space to an lalpha beta color space;
performing linear model brightness and color correction on the corresponding types, and respectively calculating the mean value and variance of each type in the image to be corrected after the lalpha beta color space conversion, wherein the formula is as follows:
Y i (p)′＝Y i (p)-μ i1 ，
wherein mu i1 For the image to be corrected, i is the i-th type, Y i (p) pixel values after conversion of the ith type lαβ color space of the image to be corrected, Y i (p)' is the corrected i-th type image element value;
the color correction step is as follows:
storing a reflection spectrum distribution set of the color card in a database, and calculating tristimulus values according to the spectrum reflectivity of the color card to obtain a color true value; calculating a remote sensing value of the spectral reflectivity of a preset color card by using the camera response model; mapping the 3 color components of the image to be corrected onto the 3 color components of a color chart, describing the characteristics of the remote sensing camera by using a linear model to obtain a corrected color image, and carrying out color correction again when the spectrum of the light source and the atmospheric parameters change;
and an output module: and converting the corrected image into an RGB color space after correction, and outputting a corrected image.
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