CN109671038A - One kind is based on the classified and layered relative radiometric correction method of pseudo- invariant features point - Google Patents
One kind is based on the classified and layered relative radiometric correction method of pseudo- invariant features point Download PDFInfo
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Abstract
One kind is based on the classified and layered relative radiometric correction method of pseudo- invariant features point, and the invention belongs to remote sensing images radiant correction fields.The correction accuracy that existing radiation correction method carries out relative radiation to the remote sensing images for occupying advantageously object area comprising coastline, island etc. is not high.Step of the present invention are as follows: one, based on remote sensing image classification obtain atural object subgraph；Two, based on the nonlinear regression analysis of spectrum, the initial relative detector calibration model and initial p IFs of atural object subgraph are determined；Three, the fine nonlinear regression analysis based on gradient determines the non-linear relative detector calibration model of the fining of atural object subgraph and fining PIFs；Four, relative detector calibration is carried out to atural object subgraph to be corrected using fining PIFs and fining non-linear relative detector calibration model；Five, the image after correction is synthesized into the complete image of a width.The present invention is applied to remote sensing images radiant correction field.
Description
Technical Field
The invention relates to a relative radiation correction method for remote sensing images, in particular to a relative radiation correction method combining imaging characteristics of a remote sensing satellite and characteristics of a target area.
Background
The remote sensing image acquisition is influenced by factors such as the sensor, illumination, atmosphere and terrain, so that spectral characteristics of the same ground object on different images have great difference. Therefore, before the multi-source or multi-temporal remote sensing image is used for change detection or ground feature information extraction, the image needs to be subjected to radiation correction processing, so that the 'pseudo change' of the earth surface landscape caused by differences of illumination conditions, atmospheric effects, sensor responses and the like is controlled and reduced, and real earth surface change information is reserved. Therefore, the remote sensing image is subjected to radiation correction before application, and the method has important significance for improving the precision of ground feature information extraction technologies such as change detection, target identification and the like. The existing radiation correction methods mainly comprise: absolute radiation correction is to convert the numerical value directly obtained by a sensor into the real reflectivity of the ground, and the method depends on synchronously obtaining the environmental parameters of the actual scene, but accurately obtaining the parameter data is difficult and low in efficiency; relative radiation correction utilizes one reference image to correct the radiation value of the other image, so that the radiation value of the image to be corrected is matched with the radiation value of the reference image.
The method comprises the steps of ①, wherein a global processing mode is adopted in the existing relative radiation correction processing method, a relative radiation correction model is used for correcting a full image, radiation characteristics of different ground object targets are not considered, environmental influence factors are different, radiation distortion rules are different, a linear model is mainly adopted in the existing relative radiation correction model ②, the radiation relationship of the ground object target in two remote sensing images is assumed to be linear, a complex relationship exists between an image radiation value obtained by a sensor and radiation sources, atmospheric conditions, radiation characteristics of the ground object targets and the like according to a remote sensing radiation transmission equation, the radiation relationship of the two images is defined as a relationship which deviates from a real radiation relationship, a correction result with higher accuracy is difficult to obtain, a PIF selection problem is not changed, on one hand, the existing PIF-based relative radiation correction method is characterized in that the relative radiation correction method is relatively more in a remote sensing image radiation correction model, the relative radiation correction method is relatively more in a remote sensing image calculation correction mode, the relative radiation correction model is more in a remote sensing image correction mode, the remote sensing image radiation correction method is more in a remote sensing image with relatively less radiation correction efficiency, the radiation correction method is more than a remote sensing image correction method, the radiation correction method is more than the method, the method is more than the method, the method is more than the method, the method is used for correcting the method for correcting.
Disclosure of Invention
The invention aims to solve the problem of low correction precision of the existing relative radiation correction method for remote sensing images, and provides a relative radiation correction method based on pseudo-invariant feature point classification layering.
A relative radiation correction method based on pseudo-invariant feature point classification layering comprises the following steps:
the method comprises the following steps: obtaining a ground object sub-image based on remote sensing image classification;
step two: determining an initial relative radiation correction model and initial PIFs of the ground feature sub-image based on the nonlinear regression analysis of the spectrum; wherein, PIFs refers to a pseudo-invariant pixel;
step three: determining a refined nonlinear relative radiation correction model and refined PIFs of the ground feature sub-image based on the refined nonlinear regression analysis of the gradient;
step four: performing relative radiation correction on the ground feature sub-image to be corrected by utilizing the refined PIFs and the refined nonlinear relative radiation correction model;
step five: and synthesizing the corrected images into a complete image.
The invention has the following effects:
after the geological feature sub-image is obtained, a refined relative radiation correction model and refined pixels of the geological feature sub-image are determined through a refined nonlinear regression analysis based on gradient probability, relative radiation correction is carried out on the geological feature sub-image to be corrected, the corrected sub-images are combined into a complete image, target radiation information of the remote sensing image is recovered, and a foundation is laid for improving the identification precision of the target.
The invention is different from the existing correction method, and does not only utilize a global model to process the reference image and the image to be corrected, nor define the radiation relation of the two images as the linear relation deviating from the real radiation relation, the invention adopts different nonlinear relative radiation correction models to perform radiation correction aiming at each type of ground object sub-images, the correction range comprises different types of ground object target relative radiation correction processing models, and the nonlinear radiation relation is adopted between the reference image and the image to be corrected, so that the real radiation relation between the two images is ensured, and the correction result with higher precision is obtained; has better prospect.
The invention is based on the classification and layering technology of the pseudo-invariant feature points, establishes a nonlinear radiation correction model, and can overcome the problems of little variation type in two images and neglecting the registration residual error of the remote sensing image and the problems of PIF quality and the outstanding dominance of a ground object target of a certain type in a research area. Has better prospect.
Drawings
FIG. 1 is a flow chart of PIF optimization extraction and nonlinear regression model solution;
FIG. 2 is a flow chart of relative radiation correction and accuracy assessment;
FIG. 3 is a diagram showing remote sensing images of coastline areas in Fengshat island City of Landsat 8OLI, two Times of Liaoning province; wherein, fig. 3a is a reference image, and fig. 3b is an image to be corrected;
FIG. 4 is a Landsat 8OLI band 2 vs. gamma corrected gray level histogram; fig. 4a to 4g are a reference image, an image to be corrected, a global linear correction method based on PIFs, a global non-linear correction based on PIFs, a global linear correction method based on hierarchical PIFs, a hierarchical classification linear correction method based on PIFs, and a Landsat 8OLI band 2 relative radiation correction gray histogram of the present invention, respectively; data value means a Data value;
FIG. 5 is a Landsat 8OLI band 3 vs. radiometric-corrected grayscale histogram; fig. 5a to 5g respectively show a reference image, an image to be corrected, a global linear correction method based on PIFs, a global non-linear correction based on PIFs, a correction method based on a hierarchical PIFs extraction technique, a hierarchical classification linear correction method based on PIFs, and a Landsat 8OLI band 2 relative radiation correction gray histogram of the present invention; data value means a Data value;
FIG. 6 is a graph of Landsat 8OLI versus radiometric calibration results; the Landsat 8OLI refers to a remote sensing image shot by a Land Imager Operated Land Imager (OLI) carried by a Landsat 8 satellite transmitted by NASA;
fig. 7 is a schematic diagram for selecting the information of the amount of the clear cloud.
Detailed Description
The first embodiment is as follows:
a relative radiation correction method based on pseudo-invariant feature point classification layering comprises the following steps:
the method comprises the following steps: obtaining a ground object sub-image based on remote sensing image classification;
step two: determining an initial relative radiation correction model and initial PIFs of the ground feature sub-image based on the nonlinear regression analysis of the spectrum; wherein, PIFs refers to a pseudo-invariant pixel;
step three: determining a refined nonlinear relative radiation correction model and refined PIFs of the ground feature sub-image based on the refined nonlinear regression analysis of the gradient;
step four: performing relative radiation correction on the ground feature sub-image to be corrected by utilizing the refined PIFs and the refined nonlinear relative radiation correction model;
step five: and synthesizing the corrected images into a complete image, evaluating the precision of the complete image, and verifying the reliability of the relative radiation correction method.
The second embodiment is as follows:
different from the first specific embodiment, in the first step, a specific process of obtaining a geological image based on remote sensing image classification includes:
the method comprises the steps of firstly, making and analyzing reference images, selecting original remote sensing images with the clear cloud amount less than or equal to 2%, carrying out radiometric calibration on the images by using calibration information of original data, and then carrying out atmospheric correction on the remote sensing images by using atmospheric condition data acquired at the same time to obtain reference images required by relative radiometric correction; wherein, the original remote sensing image with the clear cloud amount less than or equal to 2 percent is selected from multiple original remote sensing images with clear cloud amount information provided by an image data selling merchant; for example, the XXX website geospatial data cloud website has the following website address:
http://www.gscloud.cn/sources/list_dataset/411？cdataid＝263&pdataid＝10&datatype＝OLI_TIRS#dlv＝Wzg4LFswLDEwLDEsMF0sW1siZGF0YWRhdGUiLDBdXSxbXSw5OV0％3D
cloud cover information is shown in fig. 7;
step two, classifying and analyzing a reference image based on unsupervised and supervised combination of SVM:
firstly, determining a research region classification system by a visual method according to a national land utilization status classification table and by combining the characteristics of remote sensing images and the characteristics of ground object distribution of the research region;
secondly, performing unsupervised classification on the reference image by adopting an ISODATA method, and establishing an interpretation mark by combining the information of the regional high-resolution image and the regional planning image and a manual visual method so as to establish a training sample and a verification sample;
finally, the supervised classification and the precision thereof are evaluated based on the SVM method (therefore, the method adopts the RBF function to classify the image and adopts the confusion matrix method to evaluate the precision of the classification result);
step one is three: and (2) making a ground object sub-image of the reference image and the image to be corrected by using the formula (1):
wherein ,IijIs the ith wave band jth class ground object sub-image,for a certain pixel in the classification result image corresponding to red, green and blue color values, Rj，Gj，BjIn order to correspond the red, green and blue color values in the j-th ground object, IiIs a remote sensing image of the i-th band]The final return value of the relational operation is 1 or 0;&&representing the logical and operator.
The third concrete implementation mode:
different from the second embodiment, in the second step, based on the nonlinear regression analysis of the spectrum, the specific process of determining the initial relative radiation correction model and the initial PIFs of the ground object image is as follows:
(1) the process of determining the initial relative radiation correction model of the ground feature sub-image based on the nonlinear regression analysis of the spectrum specifically comprises the following steps:
based on the fact that the radiation relationship of the unchanged pixels between the reference image and the ground object sub-images corresponding to the image to be corrected is a nonlinear relationship, a second-order polynomial is adopted to describe the radiation relationship of the corresponding pixels of the two sub-images, and the formula is shown as follows:
wherein ,xijRepresenting the radiant energy value, y, of the corresponding invariant pixel in the ith waveband jth ground object sub-image of the reference imageijRepresenting the radiation energy value of the corresponding invariant pixel in the ith waveband jth ground object sub-image in the image to be corrected; a isij、bij and cijA parameter representing a linear relation;
(2) the process of determining the initial PIFs based on the nonlinear regression analysis of the spectrum specifically comprises the following steps:
firstly: carrying out standardization processing on the original image data by using the formula (4), and calculating the weight of each pixel to ensure that the radiation value ranges of the two images are in the same range:
secondly, the method comprises the following steps: order toCalculating the weight value by using the following formula:
and finally: solving an initial nonlinear regression equation by adopting a weighted least square method, fitting the pixel radiation values of the two ground feature images, wherein the pixels used in the establishment of the nonlinear regression equation are not considered equally because the ground feature pixels possibly have more changes between the two ground feature images, so that the weight of the pixels is calculated, and the calculation formula is as follows:
the fourth concrete implementation mode:
different from the third specific embodiment, in the third step, based on the fine nonlinear regression analysis of the gradient, a refined nonlinear relative radiation correction model and refined PIFs of the ground feature sub-image are determined, and the specific process is as follows:
in order to refine a nonlinear regression equation of a certain type of ground object sub-image in a certain waveband, the method specifically comprises the following steps:
and (3) replacing regression equation residual errors with gradient probabilities, and calculating weights by using a formula shown in formula (6), so that nonlinear regression equation parameters are solved by an iterative weighted least square method and are used as a refined nonlinear relative radiation correction model of the ground feature sub-image:
wherein ,DiRefers to the absolute value of the gradient difference of the ith pixel in the multi-time image, Dk(i)The absolute value of the gradient difference corresponding to the kth pixel in the ith pixel neighborhood; delta is a constant with a small value to avoid zero in the formula, and the value of delta is 1 to 10;
on the other side is provided withIn order to calculate the obtained parameters by equation (5) and use them as initial values, the parameters are updated by equation (7) to obtain refined PIFs:
wherein t represents the iteration number, and the definition of the dynamic weight is shown as formula (8):
the fifth concrete implementation mode:
different from the fourth specific embodiment, in the fourth step of the method for correcting relative radiation based on pseudo-invariant feature point classification and layering of the present embodiment, relative radiation correction is performed on the sub-image of the feature to be corrected by using the refined PIFs and the refined nonlinear relative radiation correction model, and the specific process is as follows:
measuring the radiation information relation between the corresponding ground object reference sub-image and the sub-image spot to be corrected by the formula (9) to judge whether the pixel changes:
if the pixel is not changed, executing the fourth step and the second step;
if the pixel changes, executing the fourth step and the third step;
in the formula,the ith class of ground object corresponds to the jth wave band of the reference sub-image, xijThe radiation energy wave value of the image element on the sub-image to be corrected;
step four, correcting the unchanged pixel by utilizing a relative radiation correction model refined by the local ground object, such as the ith ground object radiation correction model, namely the following formula;
and step three, for the changed pixels, searching the most similar pixel ground object type in the pixel ground object types of the reference image, and then correcting the changed pixels by using a refined nonlinear relative radiation correction model corresponding to the ground object type of the most similar pixels.
The sixth specific implementation mode:
different from the fifth embodiment, in the fifth step, the corrected image is synthesized into a complete image, and the specific process is as follows:
the corrected sub-images are synthesized into a complete image according to the following formula:
wherein ,representing the sub-image after the relative radiation correction through step four,i.e. the synthesized complete image.
And carrying out precision evaluation on the evaluation indexes, wherein the specific evaluation indexes comprise average value difference, root mean square error and subjective visual effect.
The first embodiment is as follows:
the experimental data are data of two time phases of Landsat 8OLI in usa, and multispectral images (spatial resolution is 30m, 16bit quantization, cloud amount is less than 2%) of coastal areas in the area of the curbital island city in liaison province are shown in fig. 3a and 3b, wherein fig. 3a is a reference image, and fig. 3b is an image to be corrected. The relative radiation correction obtained by the invention and the precision of other methods are shown in the following table 1 and table 2, and figures 4 a-4 g, and figures 5 a-5 g, and the relative radiation correction result is shown in figure 6. The relative radiation correction method based on the pseudo-invariant feature point classification layering has the advantages that the root mean square error is minimum after the relative radiation correction is carried out on the remote sensing image, the mean value difference is close to the minimum, and the obtained corrected histogram is close to or similar to the histogram of the reference image, so that the relative radiation correction method based on the pseudo-invariant feature point classification layering can overcome the problems generated by the conventional relative radiation correction method.
TABLE 1 Landsat 8 band 2 relative radiation correction precision table
TABLE 2 Landsat 8 band 3 relative radiation correction precision table
The present invention is capable of other embodiments and its several details are capable of modifications in various obvious respects, all without departing from the spirit and scope of the present invention.
Claims (6)
1. A relative radiation correction method based on pseudo-invariant feature point classification layering is characterized by comprising the following steps:
the method comprises the following steps: obtaining a ground object sub-image based on remote sensing image classification;
step two: determining an initial relative radiation correction model and initial PIFs of the ground feature sub-image based on the nonlinear regression analysis of the spectrum; wherein, PIFs refers to a pseudo-invariant pixel;
step three: determining a refined nonlinear relative radiation correction model and refined PIFs of the ground feature sub-image based on the refined nonlinear regression analysis of the gradient;
step four: performing relative radiation correction on the ground feature sub-image to be corrected by utilizing the refined PIFs and the refined nonlinear relative radiation correction model;
step five: and synthesizing the corrected images into a complete image.
2. The relative radiation correction method based on the pseudo-invariant feature point classification layering according to claim 1, wherein in the first step, the specific process of obtaining the geological feature image based on the remote sensing image classification comprises the following steps:
the method comprises the steps of firstly, making and analyzing reference images, selecting original remote sensing images with the clear cloud amount less than or equal to 2%, carrying out radiometric calibration on the images by using calibration information of original data, and then carrying out atmospheric correction on the remote sensing images by using atmospheric condition data acquired at the same time to obtain reference images required by relative radiometric correction;
step two, classifying and analyzing a reference image based on unsupervised and supervised combination of SVM:
firstly, determining a research region classification system by a visual method according to a national land utilization status classification table and by combining the characteristics of remote sensing images and the characteristics of ground object distribution of the research region;
secondly, performing unsupervised classification on the reference image by adopting an ISODATA method, and establishing an interpretation mark by combining the information of the regional high-resolution image and the regional planning image so as to establish a training sample and a verification sample;
step one is three: and (2) making a ground object sub-image of the reference image and the image to be corrected by using the formula (1):
wherein ,IijIs the ith wave band jth class ground object sub-image,for a certain pixel in the classification result image corresponding to red, green and blue color values, Rj，Gj，BjIn order to correspond the red, green and blue color values in the j-th ground object, IiIs a remote sensing image of the i-th band]The final return value of the relational operation is 1 or 0;&&representing the logical and operator.
3. The method according to claim 2, wherein in the second step, the specific process of determining the initial relative radiation correction model and the initial PIFs of the geological sub-images based on the nonlinear regression analysis of the spectrum is as follows:
(1) the process of determining the initial relative radiation correction model of the ground feature sub-image based on the nonlinear regression analysis of the spectrum specifically comprises the following steps:
based on the fact that the radiation relationship of the unchanged pixels between the reference image and the corresponding ground object sub-images of the image to be corrected is a nonlinear relationship, a second-order polynomial is adopted to describe the radiation relationship of the corresponding pixels of the two sub-images, and the following formula is shown:
wherein ,xijRepresenting the radiant energy value, y, of the corresponding invariant pixel in the ith waveband jth ground object sub-image of the reference imageijRepresenting the radiation energy value of the corresponding invariant pixel in the ith waveband jth ground object sub-image in the image to be corrected; a isij、bij and cijA parameter representing a linear relation;
(2) the process of determining the initial PIFs based on the nonlinear regression analysis of the spectrum specifically comprises the following steps:
firstly: carrying out standardization processing on the original image data by using the formula (4), and calculating the weight of each pixel to ensure that the radiation value ranges of the two images are in the same range:
secondly, the method comprises the following steps:order toCalculating the weight value by using the following formula:
and finally: solving an initial nonlinear regression equation by adopting a weighted least square method, fitting pixel radiation values of two geological feature images, and calculating the weight of the pixel, wherein the calculation formula is as follows:
4. the method according to claim 3, wherein in the third step, a refined nonlinear relative radiation correction model and refined PIFs of the geological sub-image are determined based on a gradient-based refined nonlinear regression analysis, and the specific process is as follows:
and (3) replacing regression equation residual errors with gradient probabilities, and calculating weights by using a formula shown in formula (6), so that nonlinear regression equation parameters are solved by an iterative weighted least square method and are used as a refined nonlinear relative radiation correction model of the ground feature sub-image:
wherein ,DiRefers to the absolute value of the gradient difference of the ith pixel in the multi-time image, Dk(i)The absolute value of the gradient difference corresponding to the kth pixel in the ith pixel neighborhood; δ is a constant of small value;
on the other side is provided withFor the calculation of the acquired parameters by the equation (5) and the use thereof as initial values, the parameters are updated by the equation (7)And obtaining refined PIFs:
wherein t represents the iteration number, and the definition of the dynamic weight is shown as formula (8):
5. the method according to claim 4, wherein in the fourth step, the relative radiation correction is performed on the geological object sub-image to be corrected by using the refined PIFs and the refined nonlinear relative radiation correction model, and the specific process is as follows:
measuring the radiation information relation between the corresponding ground object reference sub-image and the sub-image spot to be corrected by the formula (9) to judge whether the pixel changes:
if the pixel is not changed, executing the fourth step and the second step;
if the pixel changes, executing the fourth step and the third step;
in the formula,the ith class of ground object corresponds to the jth wave band of the reference sub-image, xijThe radiation energy wave value of the image element on the sub-image to be corrected;
step four, correcting the unchanged pixel by utilizing a relative radiation correction model refined by the local ground object, such as the ith ground object radiation correction model, namely the following formula;
and step three, for the changed pixels, searching the most similar pixel ground object type in the pixel ground object types of the reference image, and then correcting the changed pixels by using a refined nonlinear relative radiation correction model corresponding to the ground object type of the most similar pixels.
6. The relative radiation correction method based on pseudo-invariant feature point classification layering according to claim 5, wherein in the fifth step, the corrected images are combined into a complete image, and the specific process is as follows:
the corrected sub-images are synthesized into a complete image according to the following formula:
wherein ,representing the sub-image after the relative radiation correction through step four,i.e. the synthesized complete image.
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