CN113592737B - Remote sensing image topography correction effect evaluation method based on entropy weight method - Google Patents
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Abstract
The invention provides a remote sensing image terrain correction effect evaluation method based on an entropy weight method, which comprises the following steps: 1. acquiring quantitative evaluation indexes of the topographic correction effect of the remote sensing image; 2. quantitatively evaluating the remote sensing image after the terrain correction from five aspects by using quantitative evaluation indexes; 3. normalizing each quantitative evaluation index value; 4. obtaining a comprehensive evaluation value of the terrain correction effect of each wave band based on an entropy weight method; 5. and calculating the comprehensive score of the topographic correction effect of the whole image based on the principal component analysis method. The method can comprehensively and objectively quantitatively evaluate the topographic correction effect of the remote sensing image, and makes up the defect that the topographic correction effect is evaluated by adopting a single evaluation index and a single wave band at present.
Description
Technical Field
The invention relates to the technical field of remote sensing image preprocessing, in particular to a remote sensing image terrain correction effect evaluation method based on an entropy weight method.
Background
Solar radiation received by the earth's surface on complex terrains is affected by various factors such as the sun, the atmosphere and the terrain, thereby causing non-uniformity in the energy of the solar radiation received by the earth's surface. The remote sensing image obtained in the area has difference in brightness of pixels of the yin-yang slope image due to influence of relief, namely gradient and slope variation, and the topography effect seriously influences quantitative analysis of the remote sensing image, so that the image must be subjected to topography correction to eliminate the influence of topography in order to obtain real spectrum information of a target from the hyperspectral remote sensing image.
Beginning in the 80 s of the 20 th century, students at home and abroad begin to study on accurately acquiring the remote sensing reflectivity of the earth surface in the mountain area, and various terrain correction models are established to reduce or eliminate the influence of the terrain effect in the remote sensing image and reduce the reflectivity difference of the same earth surface type. Although various terrain correction models have been proposed at present, the terrain correction effects and application areas of the respective models are different from each other, so that some students start to study the evaluation method of the terrain correction effects for the problem that the difference in the correction effects of the respective terrain correction models is remarkable. The conventional topography correction effect evaluation method comprises qualitative and quantitative evaluation, wherein the qualitative evaluation method is a visual analysis method, namely, topography correction effect evaluation is obtained by comparing differences of remote sensing images before and after topography correction in a visual interpretation mode. The remote sensing image with good terrain correction effect has the advantages that the tone tends to be consistent, the brightness of the pixels of the illumination image is pressed, the brightness of the pixels of the shadow area is enhanced, and the hidden earth surface coverage type of the shadow area of the original image can be well repaired. The qualitative evaluation method only gives out the advantages and disadvantages of the terrain correction effect in a fuzzy manner, and can not effectively reflect the degree of the terrain correction, so the qualitative evaluation method is often used as an auxiliary means of the quantitative evaluation method.
The quantitative evaluation method for the topographic correction effect mainly uses specific numerical values to represent the topographic correction effect of the remote sensing image from different evaluation angles. For example, reeder doctor at Datts university in U.S. finds that there is a linear correlation between the radiance of the remote sensing image and the cosine value of the incident angle of the sun, so that the most commonly used cosine relation evaluation method for the scholars is generated, and the terrain correction effect of the remote sensing image is determined by utilizing the slope of the linear regression equation of the radiance of the pixels of the image before and after the terrain correction and the cosine value of the incident angle of the sun and the change of the correlation coefficient; davidThe et al (2003) have used the change in the mean value of the remote sensing image band to evaluate the stability of the terrain correction effect; rudolf Richter et al (2009) uses the coefficient of variation of image pixels in the same land cover type as an index for evaluating the heterogeneity of the remote sensing image to study the effect of the topography correction of the remote sensing image; ion Sola et al (2016) adopts the average difference of the brightness of the pixels of the yin and yang slopes in the same land cover type of the remote sensing image as an evaluation index of the terrain correction effect.
The present inventors have found that, in the process of implementing the present application, the method in the prior art has at least the following technical problems:
The quantitative evaluation index of the single topography correction effect can evaluate the topography correction effect only from a single angle of the image, and thus the evaluation result depends on the selected evaluation index. Meanwhile, the remote sensing images are often combined in a multiband mode, the current topographic correction effect evaluation indexes can only obtain evaluation results of a single waveband, and most students adopt the topographic correction evaluation results of the single waveband to replace the evaluation results of the remote sensing images. However, the terrain correction effect between the wave bands of the remote sensing image has obvious difference, so that the whole image cannot be effectively evaluated by adopting the evaluation result of a single wave band.
Disclosure of Invention
The invention provides a remote sensing image terrain correction effect evaluation method based on an entropy weight method, which is characterized in that a terrain correction comprehensive evaluation value of each wave band is obtained by integrating terrain correction quantitative evaluation indexes of multiple angles, and then the comprehensive score of the terrain correction effect of the remote sensing image is synthesized by integrating the evaluation values of each wave band, so that a terrain correction effect comprehensive evaluation model is constructed. The model aims at comprehensively and objectively quantitatively evaluating the terrain correction effect of the remote sensing image and provides a powerful support for the development of the remote sensing image terrain correction technology.
The technical method adopted by the invention comprises the following steps: the remote sensing image topography correction effect evaluation method based on the entropy weight method comprises the following steps:
S1: performing terrain correction on the obtained remote sensing image, and obtaining a quantitative evaluation index of the terrain correction effect of the remote sensing image based on the terrain correction result of the remote sensing image and the land coverage data;
s2: quantitatively evaluating each wave band of the remote sensing image after terrain correction by using quantitative evaluation indexes from five aspects respectively to obtain five quantitative evaluation index values of each wave band;
s3: performing standardization processing on each quantitative evaluation index value;
S4: weighting each quantitative evaluation index by using an entropy weight method, and calculating a comprehensive evaluation value of the terrain correction effect of each wave band;
s5: based on the comprehensive evaluation value of the topographic correction effect of each wave band, calculating the comprehensive score of the topographic correction effect of the whole image by using a principal component analysis method, wherein the comprehensive score is used for reflecting the topographic correction effect of the remote sensing image.
In one embodiment, performing terrain correction on the acquired remote sensing image in step S1 includes:
Preprocessing the acquired remote sensing image, and converting an image DN value into pixel radiance;
slope solving is carried out by using DEM data, and the sun incidence angle is calculated by using image illumination information;
And performing terrain correction calculation on the remote sensing image by utilizing various terrain correction models to obtain a plurality of terrain correction result images which are used as terrain correction results of the remote sensing image.
In one embodiment, the obtaining the quantitative evaluation index of the topographic correction effect of the remote sensing image in step S1 includes: the relative correction degree index, the average value difference of the brightness of yin and yang slopes, the median difference of the land cover type, the quarter bit interval difference of the land cover type and the abnormal value proportion.
In one embodiment, step S2 includes:
based on a cosine relation evaluation method, a linear regression equation of the pixel radiance of the remote sensing image and the cosine value of the sun incidence angle is calculated, and a slope k of the linear regression equation is utilized to solve a relative correction degree index RCE, wherein the calculation formula is as follows:
Wherein k b represents the absolute value of the slope of the linear regression equation of the non-terrain-corrected image radiance and the cosine value of the solar incident angle, and k a represents the absolute value of the slope of the linear regression equation of the terrain-corrected image radiance and the cosine value of the solar incident angle;
The SSR is used as a direct evaluation index of the remote sensing image terrain correction effect by using the same land cover type negative-positive slope radiance mean difference SSR before and after the terrain correction, and the calculation formula of the SSR is as follows:
SSR＝(sunlitb-shadyb)-(sunlita-shadya) (2)
Wherein sunlit b and shady b represent the average value of the brightness of the pixels of the sunny slope and the cloudy slope of the non-terrain correction image; sunlit a and shady a represent the average value of the brightness of the pixels of the sunny slope and the cloudy slope of the image after terrain correction;
The median difference MR of each land cover type before and after the terrain correction is used as the stability index of the terrain correction effect of the remote sensing image, and the calculation formula of the MR is as follows:
Wherein MR b and MR a respectively represent the pixel radiance median value in each land cover type of the image after no topography correction and the image after topography correction;
The four-bit spacing difference IQR of each land cover type before and after the terrain correction is used as a heterogeneity index of the terrain correction effect of the remote sensing image, and the calculation formula of the IQR is as follows:
Wherein, IQR b and IQR a respectively represent the interquartile spacing in each of the land cover of the non-terrain corrected image and the terrain corrected image;
The abnormal value proportion OR is used as one of evaluation indexes of the terrain correction effect, and the calculation formula of the OR is as follows:
Wherein NUM represents the number of image pixels, NUM or represents the number of image outlier pixels after terrain correction, wherein the pixel radiance is defined as outlier value higher than the maximum radiance value of uncorrected image or lower than the minimum radiance value of uncorrected image.
In one embodiment, step S3 includes:
and (5) carrying out data standardization on the five terrain correction evaluation index values by adopting a Z-score standardization method.
In one embodiment, step S4 specifically calculates the weight of each normalized evaluation index by using an entropy weight method, and then weights and superimposes each normalized evaluation index, where the step of calculating the weight by using the entropy weight method is as follows:
Acquiring values Z ij of the n topographic correction result images, m topographic correction effect evaluation indexes and the j-th evaluation index of the standardized i-th topographic correction result image, wherein i=1, … and n; j=1, …, m;
Calculating the proportion p ij of the ith correction image to the index under the jth evaluation index:
Wherein Z ij is each normalized evaluation index value;
calculating the entropy value e j of the j-th evaluation index:
Wherein, Meets the requirement that e j is more than or equal to 0;
calculating information entropy redundancy d j:
dj＝1-ej (9)
Calculating the weight w j of each evaluation index:
and respectively calculating a comprehensive evaluation value C of the terrain correction effect of each wave band:
in one embodiment, step S5:
Obtaining n topographic correction result images, wherein each image has l wave bands, the sample value of the q wave band of the p-th image is C pq, and the original sample matrix X is shown as formula (12):
Calculating a correlation coefficient matrix R as shown in formula (13):
R＝(rpq)l×l (13)
Wherein r pp＝1,rpq＝rqp,rpq is a correlation coefficient between the p index and the q index, k is a k-th topographic correction result image, and k is less than or equal to n; r pp＝1,rpq＝rqp,rqp is the correlation coefficient between the q index and the p index; c kp and C kq represent sample values of the p-th and q-th indices of the kth image;
Calculating eigenvalues and corresponding eigenvectors, specifically, calculating eigenvalues lambda 1≥λ2≥…≥λl of coefficient matrix R to be more than or equal to 0, and corresponding eigenvector u 1,u2,…,ul, wherein u q＝(u1q,u2q,…,unq)T, and forming one new index variable by the eigenvectors:
Wherein y 1 is the first principal component, y 2 is the second principal component, …, y l is the first principal component; c 11,…,Cnl is a sample value;
calculating the contribution rate and the accumulated contribution rate of the main component, wherein the information contribution rate of the main component y q is as follows:
Wherein k is the k-th topography correction result image, and k is less than or equal to n; lambda q is the q-th eigenvalue; Representing an accumulated sum of λ 1,…,λl;
The cumulative contribution rate α t of the principal component y 1,y2,…,yt is:
wherein lambda k represents the characteristic value of the kth index; representing the accumulated summation of the first t principal component eigenvalues; Representing the accumulation and summation of the characteristic values of all the main components, when the accumulation contribution rate alpha t of the main components is larger than a preset proportion, selecting the first t index variables y 1,y2,…,yt as t main components to replace the original l wave bands, and carrying out comprehensive analysis on the t main components;
calculating a comprehensive score of the topographic correction effect of the remote sensing image:
Wherein b q is the information contribution rate of the q-th principal component, y q is the q-th principal component score, and the remote sensing image topography correction effect comprehensive score F synthesizes the evaluation values of each wave band to reflect the remote sensing image topography correction effect.
The above technical solutions in the embodiments of the present application at least have one or more of the following technical effects:
According to the remote sensing image terrain correction effect evaluation method based on the entropy weight method, the comprehensive evaluation value of the terrain correction effect of each wave band of the remote sensing image is obtained by the entropy weight method, the evaluation value of each wave band is synthesized by the component analysis method, the comprehensive score of the terrain correction effect of the remote sensing image is obtained, the terrain correction effect of the remote sensing image is reflected, the characteristics of objectivity, comprehensiveness and accuracy are achieved, the accuracy of the terrain correction of the remote sensing image is improved, and the terrain correction effect of the remote sensing image is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a remote sensing image topography correction effect evaluation method based on an entropy weight method in the implementation of the invention.
Detailed Description
Through a great deal of research and practice, the inventor discovers that a single terrain correction effect quantitative evaluation index can only evaluate the terrain correction effect from a single angle of an image, and therefore the evaluation result depends on the selected evaluation index. Meanwhile, the remote sensing images are often combined in a multiband mode, the current topographic correction effect evaluation indexes can only obtain evaluation results of a single waveband, and most students adopt the topographic correction evaluation results of the single waveband to replace the evaluation results of the remote sensing images. However, the terrain correction effect between the wave bands of the remote sensing image has obvious difference, so that the whole image cannot be effectively evaluated by adopting the evaluation result of a single wave band. In summary, obvious irrational effect exists in the evaluation of the terrain correction effect by adopting a single index and a single wave band, and the construction of a comprehensive evaluation model of the terrain correction effect has very important practical significance in objectively evaluating the terrain correction effect of the remote sensing image.
Therefore, the method obtains the comprehensive evaluation value of the terrain correction of each wave band by integrating the quantitative evaluation indexes of the terrain correction of multiple angles, and then synthesizes the comprehensive score of the terrain correction effect of the remote sensing image by integrating the evaluation value of each wave band, thereby constructing a comprehensive evaluation model of the terrain correction effect. The model aims at comprehensively and objectively quantitatively evaluating the terrain correction effect of the remote sensing image and provides a powerful support for the development of the remote sensing image terrain correction technology.
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention provides a remote sensing image terrain correction effect evaluation method based on an entropy weight method, which comprises the following steps:
S1: performing terrain correction on the obtained remote sensing image, and obtaining a quantitative evaluation index of the terrain correction effect of the remote sensing image based on the terrain correction result of the remote sensing image and the land coverage data;
s2: quantitatively evaluating each wave band of the remote sensing image after terrain correction by using quantitative evaluation indexes from five aspects respectively to obtain five quantitative evaluation index values of each wave band;
s3: performing standardization processing on each quantitative evaluation index value;
S4: weighting each quantitative evaluation index by using an entropy weight method, and calculating a comprehensive evaluation value of the terrain correction effect of each wave band;
s5: based on the comprehensive evaluation value of the topographic correction effect of each wave band, calculating the comprehensive score of the topographic correction effect of the whole image by using a principal component analysis method, wherein the comprehensive score is used for reflecting the topographic correction effect of the remote sensing image.
The main conception of the invention is as follows:
1. Acquiring quantitative evaluation indexes of the topographic correction effect of the remote sensing image; 2. quantitatively evaluating the remote sensing image after the terrain correction from five aspects by using quantitative evaluation indexes; 3. normalizing each quantitative evaluation index value; 4. obtaining a comprehensive evaluation value of the terrain correction effect of each wave band based on an entropy weight method; 5. and calculating the comprehensive score of the topographic correction effect of the whole image based on the principal component analysis method. The method can comprehensively and objectively quantitatively evaluate the topographic correction effect of the remote sensing image, and makes up the defect that the topographic correction effect is evaluated by adopting a single evaluation index and a single wave band at present.
In one embodiment, performing terrain correction on the acquired remote sensing image in step S1 includes:
Preprocessing the acquired remote sensing image, and converting an image DN value into pixel radiance;
slope solving is carried out by using DEM data, and the sun incidence angle is calculated by using image illumination information;
And performing terrain correction calculation on the remote sensing image by utilizing various terrain correction models to obtain a plurality of terrain correction result images which are used as terrain correction results of the remote sensing image.
Specifically, the DN value (Digital Number) is a pixel brightness value of the remote sensing image, and a gray value of the recorded ground object. DEM data is an index elevation model (Digital Elevation Model) which is a physical floor model that implements a digital simulation of the floor topography (i.e., a digital representation of the topography surface morphology) through limited topography elevation data, and which represents the floor elevation in the form of a set of ordered arrays of values.
In one embodiment, the obtaining the quantitative evaluation index of the topographic correction effect of the remote sensing image in step S1 includes: the relative correction degree index, the average value difference of the brightness of yin and yang slopes, the median difference of the land cover type, the quarter bit interval difference of the land cover type and the abnormal value proportion.
In one embodiment, step S2 includes:
(1) Based on a cosine relation evaluation method, a linear regression equation of the pixel radiance of the remote sensing image and the cosine value of the sun incidence angle is calculated, and a slope k of the linear regression equation is utilized to solve a relative correction degree index RCE, wherein the calculation formula is as follows:
Wherein k b represents the absolute value of the slope of the linear regression equation of the non-terrain-corrected image radiance and the cosine value of the solar incident angle, and k a represents the absolute value of the slope of the linear regression equation of the terrain-corrected image radiance and the cosine value of the solar incident angle;
(2) The SSR is used as a direct evaluation index of the remote sensing image terrain correction effect by using the same land cover type negative-positive slope radiance mean difference SSR before and after the terrain correction, and the calculation formula of the SSR is as follows:
SSR＝(sunlitb-shadyb)-(sunlita-shadya) (2)
Wherein sunlit b and shady b represent the average value of the brightness of the pixels of the sunny slope and the cloudy slope of the non-terrain correction image; sunlit a and shady a represent the average value of the brightness of the pixels of the sunny slope and the cloudy slope of the image after terrain correction;
(3) The median difference MR of each land cover type before and after the terrain correction is used as the stability index of the terrain correction effect of the remote sensing image, and the calculation formula of the MR is as follows:
Wherein MR b and MR a respectively represent the pixel radiance median value in each land cover type of the image after no topography correction and the image after topography correction;
(4) The four-bit spacing difference IQR of each land cover type before and after the terrain correction is used as a heterogeneity index of the terrain correction effect of the remote sensing image, and the calculation formula of the IQR is as follows:
Wherein, IQR b and IQR a respectively represent the interquartile spacing in each of the land cover of the non-terrain corrected image and the terrain corrected image;
(5) The abnormal value proportion OR is used as one of evaluation indexes of the terrain correction effect, and the calculation formula of the OR is as follows:
Wherein NUM represents the number of image pixels, NUM or represents the number of image outlier pixels after terrain correction, wherein the pixel radiance is defined as outlier value higher than the maximum radiance value of uncorrected image or lower than the minimum radiance value of uncorrected image.
Since a remote sensing image with good terrain correction should have a low outlier, the outlier ratio is used as one of the evaluation indexes of the terrain correction effect.
In one embodiment, step S3 includes:
and (5) carrying out data standardization on the five terrain correction evaluation index values by adopting a Z-score standardization method.
The specific implementation process comprises the following steps of:
(1) Firstly, obtaining mathematical expectation X i and standard deviation S i of each evaluation index value;
wherein X ij is each evaluation index value; n is the number of images of the terrain correction result;
(2) And (3) carrying out standardization treatment:
wherein Z ij is each normalized evaluation index value; x ij is each evaluation index value.
(3) The signs before the negative indexes are exchanged.
The normalized data has the characteristics of 0 as the mean value and 1 as the standard deviation. Data after normalization greater than 0 indicates higher than average, and less than 0 indicates lower than average.
In one embodiment, step S4 specifically calculates the weight of each normalized evaluation index by using an entropy weight method, and then weights and superimposes each normalized evaluation index, where the step of calculating the weight by using the entropy weight method is as follows:
(1) Acquiring values Z ij of the n topographic correction result images, m topographic correction effect evaluation indexes and the j-th evaluation index of the standardized i-th topographic correction result image, wherein i=1, … and n; j=1, …, m;
(2) Calculating the proportion p ij of the ith correction image to the index under the jth evaluation index:
Wherein Z ij is each normalized evaluation index value;
(3) Calculating the entropy value e j of the j-th evaluation index:
Wherein, Meets the requirement that e j is more than or equal to 0;
(4) Calculating information entropy redundancy d j:
dj＝1-ej (9)
(5) Calculating the weight w j of each evaluation index:
(6) And respectively calculating a comprehensive evaluation value C of the terrain correction effect of each wave band:
in one embodiment, step S5:
(1) Obtaining n topographic correction result images, wherein each image has l wave bands, the sample value of the q wave band of the p-th image is C pq, and the original sample matrix X is shown as formula (12):
(2) Calculating a correlation coefficient matrix R as shown in formula (13):
R＝(rpq)l×l (13)
Wherein r pp＝1,rpq＝rqp,rpq is a correlation coefficient between the p index and the q index, k is a k-th topographic correction result image, and k is less than or equal to n; r pp＝1,rpq＝rqp,rqp is the correlation coefficient between the q index and the p index; c kp and C kq represent sample values of the p-th and q-th indices of the kth image;
(3) Calculating eigenvalues and corresponding eigenvectors, specifically, calculating eigenvalues lambda 1≥λ2≥…≥λl of coefficient matrix R to be more than or equal to 0, and corresponding eigenvector u 1,u2,…,ul, wherein u q＝(u1q,u2q,…,unq)T, and forming one new index variable by the eigenvectors:
Wherein y 1 is the first principal component, y 2 is the second principal component, …, y l is the first principal component; c 11,…,Cnl is a sample value;
(4) Calculating the contribution rate and the accumulated contribution rate of the main component, wherein the information contribution rate of the main component y q is as follows:
Wherein k is the k-th topography correction result image, and k is less than or equal to n; lambda q is the q-th eigenvalue; Representing an accumulated sum of λ 1,…,λl;
The cumulative contribution rate α t of the principal component y 1,y2,…,yt is:
wherein lambda k represents the characteristic value of the kth index; representing the accumulated summation of the first t principal component eigenvalues; Representing the accumulation and summation of the characteristic values of all the main components, when the accumulation contribution rate alpha t of the main components is larger than a preset proportion, selecting the first t index variables y 1,y2,…,yt as t main components to replace the original l wave bands, and carrying out comprehensive analysis on the t main components;
(5) Calculating a comprehensive score of the topographic correction effect of the remote sensing image:
Wherein b q is the information contribution rate of the q-th principal component, y q is the q-th principal component score, and the remote sensing image topography correction effect comprehensive score F synthesizes the evaluation values of each wave band to reflect the remote sensing image topography correction effect.
Specifically, the principal component analysis is to perform optimal synthesis and simplification on a high-dimensional variable system, and extract several fewer synthesized variables to reflect as much information of the original variables as possible. And carrying out principal component analysis on the comprehensive evaluation values of the topographic correction effect of all the wave bands of the remote sensing image, extracting comprehensive variables which can represent information of all the wave bands, and carrying out objective representation on the comprehensive evaluation values of the topographic correction effect of the whole image. The comprehensive score F of the remote sensing image terrain correction effect synthesizes the evaluation values of all the wave bands, and can objectively and comprehensively evaluate the terrain correction effect.
Referring to fig. 1, in a flowchart of a remote sensing image terrain correction effect evaluation method based on an entropy weight method in a specific embodiment, the quantitative evaluation indexes are five, including a mean value difference of brightness of a yin-yang slope, a relative correction degree, a median difference of a land cover type, a quartile interval difference of the land cover type and an abnormal value proportion, the calculated comprehensive evaluation value of the terrain correction effect to each waveband is used for measuring the correction effect of the terrain of each waveband, and the comprehensive evaluation value of each waveband is integrated by the comprehensive score F of the terrain correction effect of the remote sensing image, so that the terrain correction effect can be objectively and comprehensively evaluated.
Compared with the prior art, the invention has the beneficial effects that:
The method of the invention uses the entropy weight method to obtain the comprehensive evaluation value of the terrain correction effect of each wave band of the remote sensing image, and then uses the component analysis method to synthesize the evaluation value of each wave band to obtain the comprehensive score of the terrain correction effect of the remote sensing image, so as to reflect the terrain correction effect of the remote sensing image.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (5)
1. The remote sensing image terrain correction effect evaluation method based on the entropy weight method is characterized by comprising the following steps of:
S1: performing terrain correction on the obtained remote sensing image, and obtaining a quantitative evaluation index of the terrain correction effect of the remote sensing image based on the terrain correction result of the remote sensing image and the land coverage data;
s2: quantitatively evaluating each wave band of the remote sensing image after terrain correction by using quantitative evaluation indexes from five aspects respectively to obtain five quantitative evaluation index values of each wave band;
s3: performing standardization processing on each quantitative evaluation index value;
S4: weighting each quantitative evaluation index by using an entropy weight method, and calculating a comprehensive evaluation value of the terrain correction effect of each wave band;
S5: calculating a comprehensive score of the topographic correction effect of the whole image by using a principal component analysis method based on the comprehensive evaluation value of the topographic correction effect of each wave band, wherein the comprehensive score is used for reflecting the topographic correction effect of the remote sensing image;
The step S1 of obtaining quantitative evaluation indexes of the topographic correction effect of the remote sensing image includes: the relative correction degree index, the average value difference of the brightness of the yin and yang slopes, the median difference of the land cover type, the quarter bit interval difference of the land cover type and the abnormal value proportion;
The step S2 comprises the following steps:
based on a cosine relation evaluation method, a linear regression equation of the pixel radiance of the remote sensing image and the cosine value of the sun incidence angle is calculated, and a slope k of the linear regression equation is utilized to solve a relative correction degree index RCE, wherein the calculation formula is as follows:
Wherein k b represents the absolute value of the slope of the linear regression equation of the non-terrain-corrected image radiance and the cosine value of the solar incident angle, and k a represents the absolute value of the slope of the linear regression equation of the terrain-corrected image radiance and the cosine value of the solar incident angle;
The SSR is used as a direct evaluation index of the remote sensing image terrain correction effect by using the same land cover type negative-positive slope radiance mean difference SSR before and after the terrain correction, and the calculation formula of the SSR is as follows:
SSR＝(sunlitb-shadyb)-(sunlita-shadya) (2)
Wherein sunlit b and shady b represent the average value of the brightness of the pixels of the sunny slope and the cloudy slope of the non-terrain correction image; sunlit a and shady a represent the average value of the brightness of the pixels of the sunny slope and the cloudy slope of the image after terrain correction;
The median difference MR of each land cover type before and after the terrain correction is used as the stability index of the terrain correction effect of the remote sensing image, and the calculation formula of the MR is as follows:
Wherein MR b and MR a respectively represent the pixel radiance median value in each land cover type of the image after no topography correction and the image after topography correction;
The four-bit spacing difference IQR of each land cover type before and after the terrain correction is used as a heterogeneity index of the terrain correction effect of the remote sensing image, and the calculation formula of the IQR is as follows:
Wherein, IQR b and IQR a respectively represent the interquartile spacing in each of the land cover of the non-terrain corrected image and the terrain corrected image;
The abnormal value proportion OR is used as one of evaluation indexes of the terrain correction effect, and the calculation formula of the OR is as follows:
Wherein NUM represents the number of image pixels, NUM or represents the number of image outlier pixels after terrain correction, wherein the pixel radiance is defined as outlier value higher than the maximum radiance value of uncorrected image or lower than the minimum radiance value of uncorrected image.
2. The method of evaluating a topography correction effect of a remote sensing image according to claim 1, wherein performing topography correction on the acquired remote sensing image in step S1 comprises:
Preprocessing the acquired remote sensing image, and converting an image DN value into pixel radiance;
slope solving is carried out by using DEM data, and the sun incidence angle is calculated by using image illumination information;
And performing terrain correction calculation on the remote sensing image by utilizing various terrain correction models to obtain a plurality of terrain correction result images which are used as terrain correction results of the remote sensing image.
3. The method of evaluating a topography correction effect of a remote sensing image according to claim 1, wherein the step S3 includes:
and (5) carrying out data standardization on the five terrain correction evaluation index values by adopting a Z-score standardization method.
4. The method for evaluating the topographic correction effect of a remote sensing image according to claim 1, wherein the step S4 is specifically to calculate the weight of each normalized evaluation index by using an entropy weight method, and then to weight and superimpose each normalized evaluation index, and the step of calculating the weight by using the entropy weight method is as follows:
Acquiring values Z ij of the n topographic correction result images, m topographic correction effect evaluation indexes and the j-th evaluation index of the standardized i-th topographic correction result image, wherein i=1, … and n; j=1, …, m;
Calculating the proportion p ij of the ith correction image to the index under the jth evaluation index:
Wherein Z ij is each normalized evaluation index value;
calculating the entropy value e j of the j-th evaluation index:
Wherein, Meets the requirement that e j is more than or equal to 0;
calculating information entropy redundancy d j:
dj＝1-ej (9)
Calculating the weight w j of each evaluation index:
and respectively calculating a comprehensive evaluation value C of the terrain correction effect of each wave band:
5. The method for evaluating the topographic correction effect of a remote sensing image according to claim 1, wherein the step S5:
Obtaining n topographic correction result images, wherein each image has l wave bands, the sample value of the q wave band of the p-th image is C pq, and the original sample matrix X is shown as formula (12):
Calculating a correlation coefficient matrix R as shown in formula (13):
R＝(rpq)l×l (13)
Wherein r pp＝1,rpq＝rqp,rpq is a correlation coefficient between the p index and the q index, k is a k-th topographic correction result image, and k is less than or equal to n; r pp＝1,rpq＝rqp,rqp is the correlation coefficient between the q index and the p index; c kp and C kq represent sample values of the p-th and q-th indices of the kth image;
Calculating eigenvalues and corresponding eigenvectors, specifically, calculating eigenvalues lambda 1≥λ2≥…≥λl of coefficient matrix R to be more than or equal to 0, and corresponding eigenvector u 1,u2,…,ul, wherein u q＝(u1q,u2q,…,unq)T, and forming one new index variable by the eigenvectors:
Wherein y 1 is the first principal component, y 2 is the second principal component, …, y l is the first principal component; c 11,…,Cnl is a sample value;
calculating the contribution rate and the accumulated contribution rate of the main component, wherein the information contribution rate of the main component y q is as follows:
Wherein k is the k-th topography correction result image, and k is less than or equal to n; lambda q is the q-th eigenvalue; Representing an accumulated sum of λ 1,…,λl;
The cumulative contribution rate α t of the principal component y 1,y2,…,yt is:
wherein lambda k represents the characteristic value of the kth index; Representing the accumulated summation of the first t principal component eigenvalues; /(I) Representing the accumulation and summation of the characteristic values of all the main components, when the accumulation contribution rate alpha t of the main components is larger than a preset proportion, selecting the first t index variables y 1,y2,…,yt as t main components to replace the original l wave bands, and carrying out comprehensive analysis on the t main components;
calculating a comprehensive score of the topographic correction effect of the remote sensing image:
Wherein b q is the information contribution rate of the q-th principal component, y q is the q-th principal component score, and the remote sensing image topography correction effect comprehensive score F synthesizes the evaluation values of each wave band to reflect the remote sensing image topography correction effect.
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