CN110490061B - Uncertainty modeling and measuring method for remote sensing image characteristics - Google Patents

Uncertainty modeling and measuring method for remote sensing image characteristics Download PDF

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CN110490061B
CN110490061B CN201910624094.2A CN201910624094A CN110490061B CN 110490061 B CN110490061 B CN 110490061B CN 201910624094 A CN201910624094 A CN 201910624094A CN 110490061 B CN110490061 B CN 110490061B
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张齐
肖窈
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Abstract

The invention discloses an uncertainty modeling and measuring method for remote sensing image characteristics. The method mainly comprises Uncertainty modeling of remote sensing image features in a geographic space domain and Uncertainty modeling of the feature space domain, and then the geographic space Uncertainty and the feature space Uncertainty are subjected to weighted combination to obtain a comprehensive feature Uncertainty index FUI (feature Uncertainty index) for more accurately and comprehensively measuring the feature Uncertainty of the remote sensing image. The invention can provide the quantization result of the characteristic uncertainty of the whole image pixel by pixel and can minimize manual intervention by considering different performance characteristics of the image characteristic uncertainty under different visual angles. The method has the advantages of high accuracy and self-adaption degree, high calculation efficiency, strong operability, easy realization and strong expandability of the whole model. The uncertainty quantification result has high indication capability on classification errors, so the method has high practical value.

Description

Uncertainty modeling and measuring method for remote sensing image characteristics
Technical Field
The invention belongs to the field of remote sensing image processing and statistical modeling, and particularly relates to an uncertainty modeling and measuring method for remote sensing image characteristics.
Background
The remote sensing image classification product has very important application value in aspects of natural disaster monitoring, environmental protection, city planning, decision making and the like. However, the current remote sensing image classification technology still cannot achieve 100% accuracy or a reliable enough and completely convincing precision level. The root cause of errors in the image classification result and low reliability of the classification result is that uncertainty exists in each link of remote sensing image classification, and the uncertainty can be continuously spread and accumulated in the classification process, so that the precision and reliability of the classification result are finally influenced.
The existing method for quantifying the classification uncertainty of the remote sensing image mainly evaluates the uncertainty of a classification result so as to further refine the classification result. The method only focuses on the uncertainty of the classification result, ignores the uncertainty in the image classification process, and the uncertainty in the classification process is the source of the uncertainty in the classification result, so the uncertainty quantification result of the existing method has no obvious improvement effect on the classification result. The feature extraction is one of the most critical links of image classification, and features constructed by the feature extraction are the basis and the premise of the image classification, so that the uncertainty of quantitatively describing the image features is very important for realizing reliable image classification.
Disclosure of Invention
The invention aims to provide an uncertainty modeling and measuring method for remote sensing image characteristics, aiming at the defects in the prior art.
The technical scheme adopted by the invention is as follows: an uncertainty modeling and measuring method for remote sensing image features comprises the following steps:
step 1, extracting the characteristics of an original remote sensing image, and setting the extracted characteristic dimension as n;
step 2, modeling and quantifying the uncertainty of the extracted image features from the perspective of the geospatial domain, the implementation of which comprises the following substeps:
step 2.1, calculating the uncertainty contained in each dimensional feature of the image according to the heterogeneity of the pixels in the image between the geographic space and the surrounding neighborhood pixels;
step 2.2, calculating the information entropy of each pixel in the image in each dimensional feature according to the magnitude of the overall spatial heterogeneity of the neighborhood where the pixel is located in the image;
step 2.3, taking the information entropy obtained in the step 2.2 as a weight, carrying out weighted summation on the uncertainty of each characteristic calculated in the step 2.1, and normalizing the result of the weighted summation to obtain an uncertainty description index of the image characteristic in the geographic space, namely a geographic space uncertainty GSU;
step 3, modeling and quantifying the uncertainty of the extracted image features from the perspective of the feature space domain, and the specific implementation thereof comprises the following substeps:
step 3.1, in the feature space, searching a plurality of points closest to each feature point for each feature point, and calculating the local point density of the target feature point according to the distance between the closest points and the target feature point;
step 3.2, carrying out normalization processing on the local point densities of all the points in the feature space to obtain an uncertainty description index, namely feature space uncertainty FSU, of the image features in the feature space;
and 4, carrying out weighted combination on the geographic space uncertainty GSU and the feature space uncertainty FSU by introducing a weight adjusting coefficient lambda to obtain a comprehensive feature uncertainty index FUI so as to more accurately and comprehensively measure the feature uncertainty of the remote sensing image.
Further, the specific implementation manner of step 2.1 is as follows,
for the nth dimension feature of the image, the j column pixel Pij of any ith row of the image, and the K x K neighborhood of the pixel Pij in the nth dimension feature is represented as
Figure BDA0002126492640000021
Uncertainty of its characteristics
Figure BDA0002126492640000022
The calculation formula of (2) is as follows:
Figure BDA0002126492640000023
among them, the simplest is:
Figure BDA0002126492640000024
wherein the pixel Pxy is a neighborhood
Figure BDA0002126492640000025
Any x-th row and y-th column of pixels in,
Figure BDA0002126492640000026
and
Figure BDA0002126492640000027
respectively representing the nth dimension of the pixels Pij and PxyValue, wpxyThe weight of the influence of the pixel Pxy on the target pixel Pij is determined by the distance Pxy from the pixel Pij, and the larger the distance, the smaller the weight.
Further, the specific implementation manner of step 2.2 is as follows,
for the nth dimension feature of the image, let Pij be the pixel in the ith row and jth column of the image, and its K × K neighborhood is expressed as
Figure BDA0002126492640000028
The information entropy of the pixel Pij
Figure BDA0002126492640000029
The calculation formula of (2) is as follows:
Figure BDA00021264926400000210
among them, the simplest is:
Figure BDA0002126492640000031
Figure BDA0002126492640000032
wherein the pixel Pxy is a neighborhood
Figure BDA0002126492640000033
Any x-th row and y-th column of pixels in,
Figure BDA0002126492640000034
and
Figure BDA0002126492640000035
representing the values of the nth dimensional features of the pixels Pij and Pxy, respectively.
Further, the weighted sum in step 2.3 is calculated as follows,
Figure BDA0002126492640000036
wherein the content of the first and second substances,
Figure BDA0002126492640000037
being the characteristic uncertainty of the pixel Pij,
Figure BDA0002126492640000038
is the information entropy of the pixel Pij.
Further, the local point density Φ of the target feature point i in step 3.1iThe expression of (a) is:
Φi＝f(dij,m) (7)
among them, the simplest is:
Figure BDA0002126492640000039
where m is the number of feature points, dijRepresenting the distance of the target feature point i from the neighboring point j.
Further, the expression of FUI in step 4 is:
FUI＝(1-λ)·GSU+λ·FSU (9)
the GSU is the geospatial uncertainty, the FSU is the feature space uncertainty, and the lambda is a normal number between 0 and 1, and is used for adjusting the weight of the geospatial space and the feature space.
Further, the features extracted in step 1 include spectra, textures, shape features and spatial relationship features.
The invention has the advantages that:
(1) the invention considers the heterogeneity of the uncertainty contained in different characteristics of the image and treats them differently in the uncertainty quantization process, so as to obtain more accurate quantization result. The method provided by the invention adaptively determines the weights of different pixels in different characteristics by means of the information entropy, and can minimize manual intervention.
(2) The characteristic uncertainty quantification model simultaneously considers the uncertainty of the image characteristics in the geographic space domain and the uncertainty of the image characteristics in the characteristic space domain, and can measure the uncertainty of the image characteristics more comprehensively.
(3) The method provided by the invention has the advantages of high calculation efficiency, strong operability, easy realization and strong expandability of the whole model. Therefore, the invention has high practical value.
In a word, the method provided by the invention can effectively and comprehensively measure the uncertainty of the features extracted from the remote sensing image, provides a pixel-by-pixel feature uncertainty measurement result for the remote sensing image, and is beneficial to improving the accuracy and reliability of the image classification result. The method has high accuracy and self-adaptive degree, is easy to expand and has high practical value.
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FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
In order to facilitate the understanding and implementation of the present invention for those of ordinary skill in the art, the present invention is further described in detail with reference to the accompanying drawings and examples, it is to be understood that the embodiments described herein are merely illustrative and explanatory of the present invention and are not restrictive thereof.
The features extracted from the remote sensing images contain uncertainties of different degrees, and the uncertainties are continuously propagated and accumulated in the image classification process, and finally the accuracy and reliability of the classification result are affected. Only by accurately and effectively modeling and measuring the uncertainty of the image characteristics, the image classification method can effectively control and restrict the image characteristics in the image classification process, and further improve the precision and reliability of the classification result. Therefore, the quantitative uncertainty of the image characteristic is important for the remote sensing image classification.
Referring to fig. 1, the method for modeling and measuring uncertainty of remote sensing image features provided by the invention comprises the following steps:
step 1: feature extraction is performed on the original remote sensing image by using an existing image feature extraction tool or algorithm (such as a GLCM extraction tool in the enii software, a feature extraction tool in the ecognion, and the like) (the extracted features include a spectrum, a texture, a shape feature, a spatial relationship feature, and the like). Let the feature dimension extracted be n. From a geospatial perspective, the n-dimensional feature extracted can be stacked layer-by-layer to form a "feature cube". From the viewpoint of the feature space, the n-dimensional features may constitute a feature space in an n-dimensional coordinate system. Pixels (or feature points) with different degrees of uncertainty have different performance characteristics at these two viewing angles.
Step 2: modeling and quantifying the uncertainty of the extracted image features from the perspective of the geospatial domain, the implementation of which comprises the following sub-steps:
step 2.1: in the geospatial domain, pixels with high uncertainty in the imagery tend to have high heterogeneity with its surrounding neighboring pixels. And calculating the uncertainty contained in each dimensional feature of the image according to the heterogeneity of the pixels between the geographic space and the surrounding neighborhood pixels. In calculating the uncertainty of each dimension feature of each pixel, the influence of surrounding neighborhood pixels on the target pixel should be considered. In the actual calculation process, each neighborhood pixel is given a weight with different size according to the distance between the neighborhood pixel and the target pixel (the weight is inversely proportional to the distance).
For the nth dimension feature of the image, the j column pixel Pij of any i row of the image (the neighborhood of K × K in the nth dimension feature of the pixel Pij is represented as
Figure BDA0002126492640000051
) Characteristic uncertainty of
Figure BDA0002126492640000052
The calculation formula of (2) is as follows:
Figure BDA0002126492640000053
among them, the simplest is:
Figure BDA0002126492640000054
wherein the pixel Pxy is a neighborhood
Figure BDA0002126492640000055
Any x-th row and y-th column of pixels in.
Figure BDA0002126492640000056
And
Figure BDA0002126492640000057
representing the values of the nth dimensional features of the pixels Pij and Pxy, respectively. w is apxyThe weight of the influence of the pixel Pxy on the target pixel Pij is determined by the distance Pxy from the pixel Pij, and the larger the distance, the smaller the weight. In addition, the relation of the formula (1) can be adaptively changed according to the characteristics of the image and the description method of the local heterogeneity of the image.
Step 2.2: the higher the spatial heterogeneity in the neighborhood of a pixel (the greater the degree of uncertainty in the pixel characteristics), the greater its entropy. Calculating the information entropy of each pixel in the image in each dimensional feature according to the magnitude of the overall spatial heterogeneity of the neighborhood where the pixel is located in the image;
for the nth dimension feature of the image, let Pij be the pixel in the ith row and jth column of the image, and its K × K neighborhood is expressed as
Figure BDA0002126492640000058
The information entropy of the pixel Pij
Figure BDA0002126492640000059
The calculation formula of (2) is as follows:
Figure BDA00021264926400000515
among them, the simplest is:
Figure BDA00021264926400000510
Figure BDA00021264926400000511
wherein the pixel Pxy is a neighborhood
Figure BDA00021264926400000512
Any x-th row and y-th column of pixels in.
Figure BDA00021264926400000513
And
Figure BDA00021264926400000514
representing the values of the nth dimensional features of the pixels Pij and Pxy, respectively. In addition, the relationship of the formula (3) can be adaptively changed depending on the measurement method of the information entropy.
Step 2.3: taking the information entropy obtained by calculation in the step 2.2 as weight, and carrying out weighted summation on the uncertainty of each feature calculated in the step 2.1, wherein the expression is as follows:
Figure BDA0002126492640000061
and normalizing the result of the weighted summation to obtain the geographic space uncertainty GSU (geographic space uncertainty description indicator) of the image characteristics in the geographic space.
And step 3: modeling and quantifying the uncertainty of the extracted image features from the perspective of the feature space domain, the implementation of which comprises the following substeps:
step 3.1: in the feature space, m feature points closest to each feature point i are found for each feature point i, and the distance d between the closest feature points and the target feature point i is used as the basisij(j ═ 1,2, … …, m), the local point density Φ of the target feature point i is calculatedi. The expression is as follows:
Φi＝f(dij,m) (7)
among them, the simplest is:
Figure BDA0002126492640000062
the relationship of the formula (7) can be adaptively changed according to the description model of the point distribution density in the feature space of the image.
Step 3.2: the local point density phi of all points in the feature space calculated in step 3.1 is comparediAnd carrying out normalization processing to obtain an uncertainty description index of the image features in the feature space, namely the feature space uncertainty FSU.
And 4, step 4: by introducing a weight adjusting coefficient lambda, the geographic space uncertainty GSU and the feature space uncertainty FSU are subjected to weighted combination to obtain a comprehensive feature uncertainty index FUI, so that the feature uncertainty of the remote sensing image can be measured more accurately and comprehensively. The expression is as follows:
FUI＝(1-λ)·GSU+λ·FSU (9)
wherein λ is a normal number between 0 and 1, and is used for adjusting the weight of the geographic space and the feature space.
The invention considers the heterogeneity of the characteristics of different pixels in the image on the geographic space and the density difference of the characteristics distributed in the characteristic space, models and quantifies the uncertainty of the image characteristics from two different angles of the geographic space domain and the characteristic space domain, and finally performs weighted integration on the two angles to obtain a comprehensive characteristic uncertainty index FUI which can more effectively and comprehensively measure the characteristic uncertainty of the remote sensing image, thereby being beneficial to the improvement of the accuracy and the reliability of the image classification result. The method has the advantages of high accuracy, strong operability, high calculation efficiency, strong expandability of the whole model and high application value.
It should be understood that parts of the specification not set forth in detail are well within the prior art.
The specific embodiments described herein are merely illustrative of the spirit of the invention. Various modifications or additions may be made to the described embodiments or alternatives may be employed by those skilled in the art without departing from the spirit or ambit of the invention as defined in the appended claims.

Claims (7)

1. An uncertainty modeling and measuring method for remote sensing image features is characterized by comprising the following steps:
step 1, extracting the characteristics of an original remote sensing image, and setting the extracted characteristic dimension as n;
step 2, modeling and quantifying the uncertainty of the extracted image features from the perspective of the geospatial domain, the implementation of which comprises the following substeps:
step 2.1, calculating the uncertainty contained in each dimensional feature of the image according to the heterogeneity of the pixels in the image between the geographic space and the surrounding neighborhood pixels;
step 2.2, calculating the information entropy of each pixel in the image in each dimensional feature according to the magnitude of the overall spatial heterogeneity of the neighborhood where the pixel is located in the image;
step 2.3, taking the information entropy obtained in the step 2.2 as a weight, carrying out weighted summation on the uncertainty of each characteristic calculated in the step 2.1, and normalizing the result of the weighted summation to obtain an uncertainty description index of the image characteristic in the geographic space, namely a geographic space uncertainty GSU;
step 3, modeling and quantifying the uncertainty of the extracted image features from the perspective of the feature space domain, and the specific implementation thereof comprises the following substeps:
step 3.1, in the feature space, searching a plurality of points closest to each feature point for each feature point, and calculating the local point density of the target feature point according to the distance between the closest points and the target feature point;
step 3.2, carrying out normalization processing on the local point densities of all the points in the feature space to obtain an uncertainty description index, namely feature space uncertainty FSU, of the image features in the feature space;
and 4, carrying out weighted combination on the geographic space uncertainty GSU and the feature space uncertainty FSU by introducing a weight adjusting coefficient lambda to obtain a comprehensive feature uncertainty index FUI so as to more accurately and comprehensively measure the feature uncertainty of the remote sensing image.
2. The method for modeling and measuring the uncertainty of the characteristics of the remote sensing image according to claim 1, characterized in that: the specific implementation of step 2.1 is as follows,
for the nth dimension feature of the image, the j column pixel Pij of any ith row of the image, and the K x K neighborhood of the pixel Pij in the nth dimension feature is represented as
Figure FDA0003182378520000011
Uncertainty of its characteristics
Figure FDA0003182378520000012
The calculation formula of (2) is as follows:
Figure FDA0003182378520000013
wherein the pixel Pxy is a neighborhood
Figure FDA0003182378520000014
Any x-th row and y-th column of pixels in,
Figure FDA0003182378520000015
and
Figure FDA0003182378520000016
values, w, representing the nth dimensional characteristics of the pixels Pij and Pxy, respectivelypxyThe weight of the influence of the pixel Pxy on the target pixel Pij is determined by the distance Pxy from the pixel Pij, and the larger the distance, the smaller the weight.
3. The method for modeling and measuring the uncertainty of the characteristics of the remote sensing image according to claim 2, characterized in that: the specific implementation of step 2.2 is as follows,
for the nth dimension feature of the image, let Pij be the pixel in the ith row and jth column of the image, and its K × K neighborhood tableShown as
Figure FDA0003182378520000021
The information entropy of the pixel Pij
Figure FDA0003182378520000022
The calculation formula of (2) is as follows:
Figure FDA0003182378520000023
Figure FDA0003182378520000024
wherein the pixel Pxy is a neighborhood
Figure FDA0003182378520000025
Any x-th row and y-th column of pixels in,
Figure FDA0003182378520000026
and
Figure FDA0003182378520000027
representing the values of the nth dimensional features of the pixels Pij and Pxy, respectively.
4. The method for modeling and measuring the uncertainty of the characteristics of the remote sensing image according to claim 3, characterized in that: the weighted sum in step 2.3 is calculated as follows,
Figure FDA0003182378520000028
wherein the content of the first and second substances,
Figure FDA0003182378520000029
being the characteristic uncertainty of the pixel Pij,
Figure FDA00031823785200000210
is the information entropy of the pixel Pij.
5. The method for modeling and measuring the uncertainty of the characteristics of the remote sensing image according to claim 1, characterized in that: local point density phi of target feature point i in step 3.1iThe expression of (a) is:
Figure FDA00031823785200000211
where m is the number of feature points, dijRepresenting the distance of the target feature point i from the neighboring point j.
6. The method for modeling and measuring the uncertainty of the characteristics of the remote sensing image according to claim 1, characterized in that: the expression of FUI in step 4 is:
FUI＝(1-λ)·GSU+λ·FSU (9)
the GSU is the geospatial uncertainty, the FSU is the feature space uncertainty, and the lambda is a normal number between 0 and 1, and is used for adjusting the weight of the geospatial space and the feature space.
7. The method for modeling and measuring the uncertainty of the characteristics of the remote sensing image according to claim 1, characterized in that: the features extracted in step 1 include spectra, textures, shape features and spatial relationship features.
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