CN109671038B - Relative radiation correction method based on pseudo-invariant feature point classification layering - Google Patents
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Abstract
The invention discloses a relative radiation correction method based on pseudo-invariant feature point classification layering, and belongs to the field of remote sensing image radiation correction. The existing radiation correction method has low correction accuracy of relative radiation on remote sensing images comprising coastlines, islands and other dominant ground feature areas. The method comprises the following steps: 1. acquiring a ground object sub-image based on remote sensing image classification; 2. determining an initial relative radiation correction model and an initial PIFs of the ground object sub-image based on nonlinear regression analysis of the spectrum; 3. determining a fine nonlinear relative radiation correction model and fine PIFs of the ground object sub-image based on fine nonlinear regression analysis of the gradient; 4. performing relative radiation correction on the object sub-image to be corrected by using the refined PIFs and the refined nonlinear relative radiation correction model; 5. and synthesizing the corrected images into a complete image. The method is applied to the field of remote sensing image radiation correction.
Description
Technical Field
The invention relates to a relative radiation correction method of a remote sensing image, in particular to a relative radiation correction method combining imaging characteristics of a remote sensing satellite and characteristics of a target area.
Background
The acquisition of the remote sensing image is influenced by factors such as the sensor itself, illumination, atmosphere, topography and the like, so that the spectrum characteristics of the same ground object on different images have great differences. Therefore, before the change detection or the ground feature information extraction is performed by using the multi-source or multi-time-phase remote sensing image, the image needs to be subjected to radiation correction processing, so that the 'pseudo change' of the ground surface landscape caused by the differences of illumination conditions, atmospheric effects, sensor responses and the like is controlled and reduced, and the real ground surface change information is reserved. Therefore, the method has important significance in improving the accuracy of feature information extraction technologies such as change detection and target identification by performing radiation correction on the remote sensing image before application. The existing radiation correction method mainly comprises the following steps: absolute radiation correction is to convert the value directly obtained by a sensor into the ground real reflectivity, and the method relies on synchronously obtaining the environmental parameters of an actual scene, but is difficult and low in efficiency to accurately obtain the parameter data; the relative radiation correction uses one reference image to correct the radiation value of another image so that the radiation value of the image to be corrected matches the radiation value of the reference image.
Aiming at the problem of target identification of island, coastline and other target areas in a remote sensing image, the existing relative radiation correction method has low correction precision, and the main reasons are as follows: (1) the existing relative radiation correction processing method adopts a global processing mode, uses a relative radiation correction model to correct the whole image, does not consider the radiation characteristics of different ground object targets, has different environmental influence factors, causes different radiation distortion rules, does not consider the difference of the relative radiation correction processing models of different types of ground object targets, and only uses one global model for processing, so that a correction result with high precision is difficult to obtain. (2) The existing relative radiation correction model mainly adopts a linear model, and the linear model assumes that the ground object target is linear in the radiation relation of two remote sensing images. According to a remote sensing radiation transmission equation, the image radiation value acquired by the sensor has a complex relationship with the radiation source, the atmospheric condition, the ground object target radiation characteristic and the like, and only the two image radiation relationships are defined as linear relationships deviating from the real radiation relationship, so that a correction result with higher precision is difficult to obtain; (3) on the one hand, the prior relative radiation correction method based on PIF has the precondition that the change types in two images are less and the registration residual error of remote sensing images is ignored, so that the extracted PIF possibly has 'pathological' PIF to participate in calculation, the relative radiation correction result is not high or unpredictable errors are generated, and meanwhile, the condition that if more change areas exist in the images, the prior method extracts PIF with low quality or fails is considered; on the other hand, the existing relative radiation correction method is to fully participate in calculation to solve relative radiation correction model parameters without considering the PIF quality, so as to influence relative radiation correction precision and efficiency; (4) the characteristics of the remote sensing image of the island and other target areas are that the water area or some ground object targets in the remote sensing image are dominant, so that the radiation characteristic of the remote sensing image after the correction by the existing relative radiation correction method is mainly influenced by a large proportion of target ground objects, the dominant ground object targets of the remote sensing image after the relative radiation correction are caused, the correction precision is high, and the radiation correction precision of other ground objects is low. Therefore, by combining the satellite imaging characteristics and the target area characteristics, the automatic relative radiation correction is researched, the target radiation information is recovered, and a foundation is laid for improving the recognition accuracy of the target.
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 hierarchy, said relative radiation correction method comprising the steps of:
step one: acquiring a ground object sub-image based on remote sensing image classification;
step two: determining an initial relative radiation correction model and an initial PIFs of the ground object sub-image based on nonlinear regression analysis of the spectrum; wherein PIFs refers to pseudo-unchanged pixels;
step three: determining a fine nonlinear relative radiation correction model and fine PIFs of the ground object sub-image based on fine nonlinear regression analysis of the gradient;
step four: performing relative radiation correction on the object sub-image to be corrected by using 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:
according to the automatic relative radiation correction method, after the ground object sub-image is obtained, the fine relative radiation correction model and the fine pixels of the ground object sub-image are determined through fine nonlinear regression analysis based on the gradient probability, so that the relative radiation correction is carried out on the ground object sub-image to be corrected, the corrected sub-image is synthesized into a complete image, the target radiation information of the remote sensing image is recovered, and a foundation is laid for improving the recognition precision of the target.
The invention is different from the existing correction method, only one global model is not used for processing the reference image and the image to be corrected, and the radiation relationship of the two images is not defined as the linear relationship deviating from the real radiation relationship; has better prospect.
The invention establishes a nonlinear radiation correction model based on a pseudo-invariant feature point classification layering technology, can overcome the problems of disregarding the fact that the change types in two images are less and neglecting registration residual errors of remote sensing images, and disregarding the fact that PIF quality and a certain type of ground object targets in a research area are dominant, and compared with other methods, the accuracy of the correction result obtained by the method can simultaneously ensure that mean value difference and root mean square error are minimum, mean value difference is reduced by about 0.1-17, and root mean square error is reduced by 3-410. 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 remote sensing image of coastline area of the island city of Liaoning, landsat 8 OLI; wherein, fig. 3a is a reference image, and fig. 3b is an image to be corrected;
FIG. 4 is a Landsat 8OLI band 2 relative radiation corrected gray scale histogram; FIGS. 4 a-4 g are graphs of relative radiation corrected gray level histograms of Landsat 8OLI band 2 with respect to a reference image, an image to be corrected, a PIFs-based global linearity correction method, a PIFs-based global nonlinearity correction method, a layered PIFs-based global linearity correction method, a PIFs-based layered classification linearity correction method, and the method of the present invention, respectively; data value is the meaning of the Data value;
FIG. 5 is a Landsat 8OLI band 3 relative radiation corrected gray scale histogram; FIGS. 5 a-5 g are graphs of relative radiation correction gray level histograms of Landsat 8OLI band 2, respectively, for a reference image, an image to be corrected, a PIFs-based global linearity correction method, a PIFs-based global nonlinearity correction, a layered PIFs extraction technique-based correction method, a PIFs-based layered classification linearity correction method, and the method of the present invention; data value is the meaning of the Data value;
FIG. 6 is a graph of the relative radiation correction results of Landsat 8 OLI; landsat 8OLI refers to a remote sensing image shot by a land imager Operational Land Imager (OLI) carried by Landsat 8 satellites transmitted by NASA;
fig. 7 is a schematic diagram for selecting clear cloud cover information.
Detailed Description
The first embodiment is as follows:
a relative radiation correction method based on pseudo-invariant feature point classification hierarchy, said relative radiation correction method comprising the steps of:
step one: acquiring a ground object sub-image based on remote sensing image classification;
step two: determining an initial relative radiation correction model and an initial PIFs of the ground object sub-image based on nonlinear regression analysis of the spectrum; wherein PIFs refers to pseudo-unchanged pixels;
step three: determining a fine nonlinear relative radiation correction model and fine PIFs of the ground object sub-image based on fine nonlinear regression analysis of the gradient;
step four: performing relative radiation correction on the object sub-image to be corrected by using the refined PIFs and the refined nonlinear relative radiation correction model;
step five: and synthesizing the corrected images into a complete image, and evaluating the accuracy of the complete image to verify the reliability of the relative radiation correction method.
The second embodiment is as follows:
different from the specific embodiment, the specific process of acquiring the ground object sub-image based on the remote sensing image classification in the first step is as follows:
step one, reference image production and analysis, namely selecting an original remote sensing image with the clear cloud content less than or equal to 2%, performing radiation calibration on the image by utilizing calibration information of the original data, and performing atmospheric correction on the remote sensing image by utilizing atmospheric condition data acquired in the same time period to obtain a reference image required by relative radiation correction; the original remote sensing image with the clear cloud quantity less than or equal to 2% is selected from multiple original remote sensing images with clear cloud quantity information provided by image data selling merchants; for example, the XXX website geospatial data cloud website, the website is:
http://www.gscloud.cn/sources/list_dataset/411cdataid＝263&pdataid＝10&datatype＝OLI_TIRS#dlv＝Wzg4LFswLDEwLDEsMF0sW1siZGF0YWRhdGUiLDBdXSxbXSw5OV0％3D
cloud cover information is shown in fig. 7;
step two, reference image classification and analysis based on combination of SVM non-supervision and supervision:
firstly, determining a research area classification system by a visual method according to the national land use status classification form and combining the characteristics of remote sensing images and the characteristics of the ground feature distribution of a research area;
secondly, performing non-supervision classification on the reference image by adopting an ISODATA method, and establishing an interpretation mark by combining the regional high-resolution image and regional planning map information and a manual visual method so as to establish a training sample and a verification sample;
finally, the supervision classification based on the SVM method and the precision thereof are evaluated (therefore, the method adopts RBF function to classify the images and adopts confusion matrix method to evaluate the classification result precision);
step one, three: making a ground object sub-image of the reference image and the image to be corrected by using the formula (1):
wherein ,Iij Is the j-th class ground object sub-image of the i-th wave band,
r is the value of red, green and blue corresponding to a pixel in the classified result image j ，G j ，B j To correspond to the red, green and blue values of the jth ground object, I i For the i-th band remote sensing image []The final return value of the relation operation is 1 or 0;&&an operator representing a logical AND.
And a third specific embodiment:
different from the second embodiment, in the second embodiment, the specific process of determining the initial relative radiation correction model and the initial PIFs of the ground object sub-image 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 object sub-image based on the nonlinear regression analysis of the spectrum specifically comprises the following steps:
based on the fact that the radiation relation of the unchanged pixels between the sub-images of the object corresponding to the reference image and the image to be corrected is a nonlinear relation in theory, a second-order polynomial is adopted to describe the radiation relation of the corresponding pixels of the two sub-images, and the radiation relation is shown in the following formula:
wherein ,xij Representing the radiation energy value of corresponding non-pixel in the ith band and jth ground object sub-image of the reference image, y ij Representing the radiation energy value of a corresponding pixel in a jth object sub-image of an ith wave band in the image to be corrected; a, a ij 、b ij and cij Parameters representing a linear relationship;
(2) The process of determining initial PIFs based on nonlinear regression analysis of spectra is specifically:
first,: carrying out standardization processing on the original image data by using the formula (4), and calculating the weight of each pixel so as to ensure that the radiation value ranges of the two images are in the same range:
secondly: order the
Weights were calculated using the following:
finally: the initial nonlinear regression equation is solved by adopting a weighted least square method, and the pixel radiation values of the two ground object sub-images are fitted, and because more changed ground object pixels possibly exist between the two ground object sub-images, the pixels used in the establishment of the nonlinear regression equation are not considered equally, so that the weight of the pixels is calculated, and the calculation formula is as follows:
the specific embodiment IV is as follows:
in the third step, based on the fine nonlinear regression analysis of the gradient, the fine nonlinear relative radiation correction model and the fine PIFs of the ground object sub-image are determined, and the specific process is as follows:
in order to refine a nonlinear regression equation of a certain class of ground object images in a certain wave band, the nonlinear regression equation is specifically as follows:
the gradient probability is adopted to replace the regression equation residual error, and the formula shown in the formula (6) is utilized to calculate the weight, so that the nonlinear regression equation parameter is solved through the iterative weighted least square method and is used as the refined nonlinear relative radiation correction model of the ground object sub-image:
wherein ,Di Refers to the absolute value of the gradient difference of the ith pixel in the multi-time image, D k(i) The absolute value of the gradient difference corresponding to the kth pixel in the pixel neighborhood of the ith pixel; delta is a constant with a small value so as not to appear zero in the formula, and the value of delta is 1 to 10;
on the other side set
For the parameter obtained by the calculation of the formula (5), and with the parameter as an initial value, the parameter is updated by the formula (7), and the refined PIFs are obtained:
wherein t represents the iteration times, and the definition of the dynamic weight is shown in the formula (8):
fifth embodiment:
in the fourth step, the relative radiation correction is performed on the 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 according to the formula (9) to judge whether the pixel changes or not:
if the pixel is unchanged, executing the fourth step;
if the pixel changes, executing the fourth step;
in the formula ,
the j band of the i-th ground object corresponds to the reference sub-image, x ij Radiating energy wave values for pixels on the sub-image to be corrected;
step four, correcting unchanged pixels by using a relative radiation correction model with refined local ground objects, such as an i-th ground object radiation correction model, namely the following;
and fourthly, for the changed pixels, searching the pixel ground object type which is the most similar to the changed pixels in the pixel ground object type of the reference image, and correcting the changed pixels by using a refined nonlinear relative radiation correction model corresponding to the ground object type of the most similar pixels.
Specific embodiment six:
in the fifth step, the corrected image is synthesized into a complete image, and the specific process is as follows:
the corrected sub-image is synthesized into a complete image as follows:
wherein ,
representing the sub-image after the relative radiation correction by step four +.>
I.e. the synthesized complete image.
And the accuracy is evaluated, and specific evaluation indexes include mean value difference, root mean square error and subjective visual effect.
Embodiment one:
the experimental data uses the data of two time phases of Landsat 8OLI in the United states, the multispectral image (the spatial resolution is 30m, the 16bit quantization and the cloud amount is less than 2%) of the coastal region of the island city of the Liaoning province is 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 and other method accuracy conditions obtained by the present invention are shown in the following tables 1, 2 and fig. 4 a-fig. 4g, and the relative radiation correction results are shown in fig. 6 in fig. 5 a-fig. 5 g. 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 minimum, the histogram after correction is obtained is close to or similar to the histogram of the reference graph, the problem caused by the traditional relative radiation correction method can be solved, and the relative radiation correction method based on the pseudo-invariant feature point classification layering has higher accuracy on the relative radiation correction of the remote sensing image in the areas with island reefs, coastlines and the like.
TABLE 1 Landsat 8 band 2 relative radiation correction precision table
Table 2 Landsat 8 band 3 relative radiation correction accuracy table
The present invention is capable of other and further embodiments and its several details are capable of modification and variation in light of the present invention, as will be apparent to those skilled in the art, without departing from the spirit and scope of the invention as defined in the appended claims.
Claims (3)
1. The relative radiation correction method based on the pseudo-invariant feature point classification layering is characterized by comprising the following steps of:
step one: acquiring a ground object sub-image based on remote sensing image classification;
step two: determining an initial relative radiation correction model and an initial PIFs of the ground object sub-image based on nonlinear regression analysis of the spectrum; wherein PIFs refers to pseudo-unchanged pixels, and the specific process is as follows:
(1) The process of determining the initial relative radiation correction model of the ground object sub-image based on the nonlinear regression analysis of the spectrum specifically comprises the following steps:
based on the fact that the radiation relation of the unchanged pixels between the reference image and the corresponding object sub-image of the image to be corrected is a nonlinear relation, the radiation relation of the corresponding pixels of the two sub-images is described by a second-order polynomial, and the radiation relation is shown in the following formula:
wherein ,xij Representing the radiation energy value of corresponding non-pixel in the ith band and jth ground object sub-image of the reference image, y ij Representing the radiation energy value of a corresponding pixel in a jth object sub-image of an ith wave band in the image to be corrected; a, a ij 、b ij and cij Parameters representing a linear relationship;
(2) The process of determining initial PIFs based on nonlinear regression analysis of spectra is specifically:
first,: carrying out standardization processing on the original image data by using the formula (2), and calculating the weight of each pixel so as to ensure that the radiation value ranges of the two images are in the same range:
secondly: order the
Weights were calculated using the following:
finally: solving an initial nonlinear regression equation by adopting a weighted least square method, fitting the radiation values of pixels of two ground object sub-images, and calculating the weight of the pixels, wherein the calculation formula is as follows:
step three: based on fine nonlinear regression analysis of gradients, a fine nonlinear relative radiation correction model and fine PIFs of the ground object sub-image are determined, and the specific process is as follows:
the gradient probability is adopted to replace the regression equation residual error, and the formula shown in the formula (5) is utilized to calculate the weight, so that the nonlinear regression equation parameter is solved through the iterative weighted least square method and is used as the refined nonlinear relative radiation correction model of the ground object sub-image:
wherein ,Di Refers to the absolute value of the gradient difference of the ith pixel in the multi-time image, D k(i) The absolute value of the gradient difference corresponding to the kth pixel in the pixel neighborhood of the ith pixel; delta is a constant of small value;
on the other side set
And (3) calculating the acquired parameters by the formula (4), and taking the parameters as initial values, updating the parameters by the formula (6), thereby acquiring refined PIFs: />
Wherein t represents the iteration times, and the definition of the dynamic weight is shown in the formula (7):
step four: the method comprises the following specific processes of carrying out relative radiation correction on the object sub-image to be corrected by using a refined PIFs and a refined nonlinear relative radiation correction model:
measuring the radiation information relation between the corresponding ground object reference sub-image and the sub-image spot to be corrected according to the formula (8) to judge whether the pixel changes or not:
if the pixel is unchanged, executing the fourth step;
if the pixel changes, executing the fourth step;
in the formula ,
the j band of the i-th ground object corresponds to the reference sub-image, x ij Radiating energy values for pixels on a sub-image to be corrected;
step four, correcting unchanged pixels by using a relative radiation correction model with refined local ground objects, wherein the i-th ground object radiation correction model is the following;
step four, for the changed pixels, searching the pixel ground object type most similar to the changed pixels in the pixel ground object type of the reference image, and correcting the changed pixels by using a refined nonlinear relative radiation correction model corresponding to the ground object type of the most similar pixels;
step five: and synthesizing the corrected images into a complete image.
2. The method for correcting relative radiation based on pseudo-invariant feature point classification and layering as claimed in claim 1, wherein in the first step, the specific process of obtaining the ground object sub-image based on remote sensing image classification is as follows:
step one, reference image production and analysis, namely selecting an original remote sensing image with the clear cloud content less than or equal to 2%, performing radiation calibration on the image by utilizing calibration information of the original data, and performing atmospheric correction on the remote sensing image by utilizing atmospheric condition data acquired in the same time period to obtain a reference image required by relative radiation correction;
step two, reference image classification and analysis based on combination of SVM non-supervision and supervision:
firstly, determining a research area classification system by a visual method according to the existing land use current situation classification table and combining the characteristics of remote sensing images and the characteristics of the ground feature distribution of a research area;
secondly, performing non-supervision classification on the reference image by adopting an ISODATA method, and establishing an interpretation mark by combining the regional high-resolution image and regional planning map information so as to establish a training sample and a verification sample;
step one, three: making a ground object sub-image of the reference image and the image to be corrected by using the formula (10):
wherein ,Iij Is the j-th class ground object sub-image of the i-th wave band,
r is the value of red, green and blue corresponding to a pixel in the classified result image j ，G j ，B j To correspond to the red, green and blue values of the jth ground object, I i For the i-th band remote sensing image []The final return value of the relation operation is 1 or 0;&&an operator representing a logical AND. />
3. The method for correcting relative radiation based on pseudo-invariant feature point classification and layering according to claim 2, wherein in the fifth step, the corrected image is synthesized into a complete image, and the specific process is as follows:
the corrected sub-image is synthesized into a complete image as follows:
wherein ,
representing the sub-image after the relative radiation correction by step four +.>
Namely, is a combinationA complete image is formed. />
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