CN111652056A - Pedestrian road network solar irradiation index detection method based on remote sensing image - Google Patents
Pedestrian road network solar irradiation index detection method based on remote sensing image Download PDFInfo
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Abstract
The invention discloses a pedestrian road network solar irradiation index detection method based on remote sensing images, which comprises the following steps: obtaining a remote sensing image of a region to which a pedestrian road network belongs, and processing the remote sensing image to obtain a normalized difference vegetation index and textural features; randomly selecting part of position points in a road network, collecting solar irradiance as an attribute for constructing training data, and obtaining attribute information through a model by using the rest position points as detection data; constructing a multiple regression model by using the training data, and acquiring a solar irradiation index of the detection data; and matching the acquired solar radiation indexes to position points in the road network, and then obtaining road network basic data with attribute information. The invention aims to realize automatic detection of solar irradiation indexes of road networks in different areas so as to obtain road network data with the attribute, provide a certain data basis for personalized pedestrian navigation and solve the problem that the prior art cannot provide path recommendation with specific requirements for users.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a pedestrian road network solar irradiation index detection method based on remote sensing images.
Background
Nowadays, in daily life, the use of pedestrian navigation systems is becoming more and more common, becoming an indispensable daily requirement. However, most of the existing navigation systems perform path analysis based on the shortest path, and the measurement of road environmental factors is lacked. The most important reason is the lack of this type of information from the basic data required by the navigation system.
Under the strong irradiation of sunlight and a hot environment, the solar radiation intensity of the external environment is extremely high, so that the ultraviolet index of the environment is increased dramatically, and the damage to human skin is increased. Simultaneously, along with the rising of earth's surface temperature, human perception temperature also promotes to some extent, and the very big degree has influenced the comfort level of pedestrian's trip.
Therefore, the prior art needs to be improved and developed, and the invention provides a simple solar irradiance detection method, which can be used for obtaining the road network irradiation indexes of different areas and improving the existing problems.
Disclosure of Invention
The invention aims to solve the technical problem that a pedestrian road network solar irradiation index detection method based on remote sensing images is provided aiming at the defects in the prior art, and the problem that a navigation system in the prior art cannot analyze a path by considering the shade degree of a road is solved.
The technical scheme adopted by the invention for solving the technical problem is as follows:
a pedestrian road network solar irradiation index detection method based on remote sensing images comprises the following steps:
obtaining a remote sensing image of a region to which a pedestrian road network belongs, and processing the remote sensing image to obtain a normalized difference vegetation index and textural features;
randomly selecting position points of a road network part, and collecting solar irradiance and corresponding position information;
acquiring a normalized difference vegetation index and texture characteristics corresponding to the solar irradiance of each position point through the position information, and fusing and constructing a training data set; the residual data are used as a detection data set for attribute detection based on position information fusion normalization difference vegetation indexes and texture features;
constructing a multiple linear regression model suitable for irradiance detection based on the training dataset;
automatically identifying irradiance of the detection data set by using a multiple linear regression model;
map matching is carried out based on the existing pedestrian road network, and the detected attribute information is given to position points in the pedestrian road network, so that the road network basic data with the attribute information is obtained.
The pedestrian road network solar irradiation index detection method based on the remote sensing image comprises the following steps of obtaining the remote sensing image of the region to which the pedestrian road network belongs, and processing to obtain a normalized difference vegetation index and textural features:
acquiring a remote sensing image and preprocessing the remote sensing image to obtain a preprocessed remote sensing image;
calculating the normalized difference vegetation index of the preprocessed remote sensing image by using the optical characteristics of the vegetation chlorophyll;
and calculating the texture characteristics of the preprocessed remote sensing image by adopting the gray level co-occurrence matrix.
The pedestrian road network solar irradiation index detection method based on the remote sensing image, wherein the step of randomly selecting the road network part position points and collecting the solar irradiance and the corresponding position information comprises the following steps:
randomly selecting position points of a road network part, and acquiring solar irradiance through a solar tester;
and acquiring position information corresponding to the collecting point of the solar irradiance by a built-in sensor of the mobile phone.
The pedestrian road network solar irradiation index detection method based on the remote sensing image, wherein the solar irradiance acquired by the solar tester comprises the following steps:
when the collecting position point is determined, the solar irradiance of the point is collected, meanwhile, the data of the nearby point is collected, and the average value is obtained to obtain the final solar irradiance of the point.
The pedestrian road network solar irradiation index detection method based on the remote sensing image is characterized in that the normalized difference vegetation index and the texture feature corresponding to the irradiance of each position point are obtained through the position information, and a training data set is fused and constructed; the residual data are used as a detection data set for attribute detection based on position information fusion normalization difference vegetation indexes and textural features, and the method comprises the following steps:
data obtained by processing the remote sensing images are all provided with position information, the normalized difference vegetation index and the texture feature corresponding to the closest point of the solar irradiance position are obtained through comparison of GPS data, and a training data set is constructed after fusion; the other position points which are not used for constructing the training set only fuse the normalized difference vegetation fingers and the texture features as a detection data set of the model;
wherein T represents a training data set,
representing the ith normalized differential vegetation index,
representing the ith texture feature, yiRepresenting the ith solar irradiance, n represents the sample size of the training data set.
The pedestrian road network solar irradiation index detection method based on the remote sensing image is characterized in that the multiple linear regression model is as follows:
irrad(x)＝θ1x1+θ2x2+θ0+Rt
wherein irrad (x) is represented bySolar irradiance, theta, obtained from a multiple linear regression model1Representing the coefficient of correlation, theta, of the normalized differential vegetation index with solar irradiance2Representing the correlation coefficient, R, of image texture and solar irradiancetExpressed as a reference value, theta, of solar irradiance for each time period0Are model parameters.
The pedestrian road network solar irradiation index detection method based on the remote sensing image is characterized in that the coefficient of the multiple linear regression model is obtained through the following loss function:
wherein,
j (theta) is a loss function, T represents transposition;
the coefficient theta of the multiple linear regression model is as follows:
θ＝(XTX)-1XTY。
the method for detecting the solar radiation index of the pedestrian road network based on the remote sensing image comprises the following steps of carrying out map matching on the existing pedestrian road network through map matching, endowing the detected attribute information to position points in the pedestrian road network, and obtaining road network basic data with the attribute information, wherein the method comprises the following steps:
based on the existing pedestrian road network OpenStreetMap, the solar irradiance detected by the multiple linear regression model is given to the matched position points in the pedestrian road network according to the position information, so that the basic data of the pedestrian road network has attribute information.
The method for detecting the solar irradiation index of the pedestrian road network based on the remote sensing image comprises the following steps of:
sequentially calculating the distance between a GPS point corresponding to the detected solar irradiance and each position point in the pedestrian network;
and selecting the point pair combination with the shortest distance, and endowing the point solar irradiance to the matched position points in the pedestrian road network, so that the road network basic data with attribute information is obtained.
The pedestrian road network solar irradiation index detection method based on the remote sensing image is characterized in that the distance is as follows:
wherein l and m respectively represent the longitude and latitude of a position point corresponding to the detected solar irradiance, and lOSMAnd mOSMRespectively representing the longitude and latitude of the matched location point in the OpenStreetMap.
Has the advantages that: the solar irradiation indexes of road networks in different areas are automatically detected, the attributes of the pedestrian road network with attribute information are obtained after matching, and a certain data basis is provided for personalized navigation. In the process of planning the travel path of the pedestrian, the sun irradiation index is used as a measurement factor, the travel path with higher comfort can be recommended for the travel of the pedestrian, and the personalized demand is met.
Drawings
FIG. 1 is a technical scheme according to the present invention.
FIG. 2 is a flow chart of a pedestrian road network solar irradiation index detection method based on remote sensing images.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and clearer, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1-2, the present invention provides some embodiments of a method for detecting a pedestrian road network solar radiation index based on a remote sensing image.
In the existing pedestrian navigation system, route planning only analyzes the shortest-path factor, and a travel environment factor is not considered, so that a travel route with high comfort cannot be recommended for a travel group. Travel groups are usually willing to select cool paths to travel, however, attribute information of solar irradiation indexes is lacked in the current pedestrian network data, so that a navigation system cannot use the solar irradiation indexes as a measurement unit, the paths are optimized and recommended to be comfortable, and only the shortest travel path can be provided. Wherein, the shade degree of the road has a great relationship with the vegetation coverage condition. The vegetation index obtained from the satellite image can only reflect whether the area has vegetation or not, and cannot directly reflect the vegetation coverage condition of the area. Therefore, the vegetation index needs to be analyzed together with the image texture to determine the real vegetation coverage.
As shown in fig. 1, the solar irradiance detection method provided by the present invention mainly includes four aspects. Firstly, acquiring data based on a satellite image and a solar power tester, and simultaneously obtaining position information of each point; secondly, fusing the acquired data based on GPS data, and respectively constructing a training data set and a detection data set by using the fused data, wherein only true value (irradiance) labeling is carried out on the training data set; thirdly, based on a machine learning method, training by using training set data to obtain an applicable regression model, and carrying out irradiance detection on a detection data set; and finally, matching the detected irradiation attribute to an OSM road network based on GPS data so as to obtain pedestrian road network data with attribute information. The detection method provided by the invention can automatically detect the solar irradiation indexes of road networks in different areas, and can obtain pedestrian road network data with attribute information after matching, thereby providing a certain data basis for personalized navigation. In the process of planning the travel path of the pedestrian, the sun irradiation index is used as a measurement factor, the travel path with higher comfort can be recommended for the travel of the pedestrian, and the personalized demand is met.
As shown in fig. 2, the method for detecting the solar irradiation index of the pedestrian road network based on the remote sensing image comprises the following steps:
and S100, obtaining a remote sensing image of the region to which the pedestrian road network belongs, and processing to obtain a normalized difference vegetation index and texture features.
Specifically, the remote sensing image refers to a digital image obtained by carrying a multispectral or hyperspectral imager by using various remote sensing platforms (a satellite, an airplane and an unmanned aerial vehicle) and adopting frame-type or scanning-type imaging. The acquisition channel comprises free downloading, paid ordering or shooting by using own equipment and the like.
Specifically, the step S100 includes:
and step S110, obtaining the remote sensing image and preprocessing the remote sensing image to obtain a preprocessed remote sensing image.
The remote sensing image preprocessing process comprises the following steps:
radiation pretreatment: the method is used for eliminating the influence of atmospheric interference on imaging in the sensor and the imaging process and improving the image quality, and specifically comprises the processes of radiometric calibration, atmospheric correction and the like.
Geometric pretreatment: the method is a process of correcting geometric distortion, geographical position deviation and the like generated in the image imaging process through computer software, inlaying images and forming digital images with accurate geographical positions and no distortion or distortion meeting requirements, and specifically comprises the processes of internal orientation, relative orientation, absolute orientation, matching, splicing and the like.
And S120, calculating the normalized difference vegetation index of the preprocessed remote sensing image by using the optical characteristics of the vegetation chlorophyll.
After the preprocessed remote sensing image is obtained, the normalized vegetation index (NDVI) is calculated from the multispectral image layer using the NDVI button, which is the image analysis function of ArcMap. Specifically, the Normalized Difference Vegetation Index (NDVI) is calculated using the optical properties of Vegetation chlorophyll. The specific calculation principle is that vegetation chlorophyll forms strong reflection in a near infrared band and is strongly absorbed in a red band, and the calculation formula is as follows:
where ρ isNIRIs the reflectance image value, rho, of the near infrared bandRIs the reflectivity image value of the red wave band. Can also be directly used in simple calculationThe gray value of the original image is calculated without using the reflectance image. The calculation method can be implemented in remote sensing (ENVI, ERDAS, PIE, etc.) or geographic information software (ArcMap, etc.).
Because the normalized difference vegetation index of the pixel point only reflects the concentration of chlorophyll and does not directly reflect the type of vegetation, such as grassland, shrub or arbor, that is, the NDVI value of each point cannot intuitively reflect the vegetation coverage of the position, such as grassland or shrub, although the NDVI value is higher, the area is not vegetation covered; while areas of trees are covered by vegetation. Therefore, it is further differentiated by using texture features.
And S130, calculating the texture characteristics of the preprocessed remote sensing image by adopting a gray level co-occurrence matrix.
Texture features are measured using a Gray Level Cooccurrence Matrix (GLCM). The gray level co-occurrence matrix describes the rule that the gray levels of the pixels repeatedly appear at the spatial positions, and describes the joint distribution of the gray levels of the two pixels with a certain spatial position relation. For any pixel, the gray level co-occurrence matrix texture feature calculation process is as follows:
(1) setting a window of w x w by taking the pixel as a center, taking out the sub-images in the range for later use, wherein w is an odd number;
(2) setting gray level symbiotic angles (such as 0 degree, 45 degrees, 90 degrees and 135 degrees) and symbiotic intervals (such as 1, 2 and 3);
(3) setting a gray scale compression level N (e.g., 16, 32, 64, 128, etc.), stretching the image to a specified gray scale range;
(4) initializing a gray level co-occurrence matrix P (matrix dimension N x N), counting gray level frequency on sub-images according to a specified angle and interval, recording the gray level frequency in the gray level co-occurrence matrix, and finally normalizing the co-occurrence matrix;
(5) calculating a texture measurement index (P (i, j) is an element of the ith row and the jth column in the gray level co-occurrence matrix P) by using the gray level co-occurrence matrix, wherein the index is calculated as follows:
contrast (Contrast) reflects the image definition and the depth of texture grooves, and the deeper the texture grooves, the more pixels with large gray scale difference, and the larger this Contrast value.
Dissimilarity (Dissimilary) is similar to contrast but increases linearly. If the local contrast is higher, the dissimilarity is also higher.
Energy (ASM) is a measure of the stability of the gray scale changes of the texture of an image, and a larger value indicates a texture with more stable regular changes.
The Inverse Difference Moment (IDM) reflects the homogeneity of the image texture and measures how much the image texture changes locally. Larger values indicate lack of variation between different regions of the image texture and local uniformity.
The Entropy (Entropy) reflects the uniformity degree of the image gray level co-occurrence matrix value, namely, when the image goldfish is random or the noise value is large, the Entropy is also large.
And S200, randomly selecting position points of the road network part, and collecting solar irradiance and corresponding position information.
Specifically, step S200 includes:
and S210, randomly selecting position points of the road network part, and acquiring solar irradiance through solar energy tester collection.
And S220, acquiring position information corresponding to the solar irradiance through a built-in sensor of the mobile phone.
TES-1333 solar power tester is a precise instrument specially used for on-site measurement of solar radiation power, can measure solar power in different directions and different angles, solar transmittance of heat-insulating materials, solar energy integral measurement and the like, and is suitable for solar panel construction installation measurement, solar research, vegetable and flower greenhouse planting, indoor greenhouse design, and production and hydrological analysis of glass, heat-insulating paper and a solar umbrella. During the use of the tester, the maximum value, the minimum value and the average value can be read according to the selection of the mode, and the readings of the maximum value, the minimum value and the average value are all the current readings. Wherein the average is a moving average of the last 4 occurrences of the current value to smooth out unstable readings.
The solar irradiance tester adopts the solar tester to collect solar irradiance, simultaneously collects data of a collecting point and a nearby point, and calculates an average value to obtain the solar irradiance. In the data acquisition process, the irradiance of an acquisition point cannot be directly reflected as the true value of the point, and a plurality of points are acquired near the point at the same time, and the average value of the plurality of points is used as the true value of the acquisition point. Besides the collection of irradiance of the collection point and the surrounding points, the GPS information of the collection point is recorded through a built-in sensor of the mobile phone.
In addition, weather reasons and different time periods have great influence on the collection of solar irradiance due to various time factors. Therefore, solar irradiance needs to be collected in open spaces for different weather and different time periods. Because the geographic factors and atmospheric conditions of the same regional area are basically similar, and the solar irradiance in the same time period is approximately the same, the irradiance collected in the open environment is used as the reference value of the whole area. The reference value is added to the regression model to represent the control over the time resolution.
Step S300, obtaining the NDVI and the texture characteristics corresponding to the solar irradiance of each position point through the position information, and fusing and constructing a training data set; and the residual data is fused with the NDVI and the texture features based on the position information and is used as a detection data set for attribute detection.
Specifically, data obtained by processing the remote sensing images are provided with position information, so that NDVI and texture values corresponding to the nearest point of the irradiance position are obtained through comparison of GPS data, and a training data set is constructed after fusion; and the other position points which are not used for constructing the training set are only fused with the NDVI and the texture value to be used as detection data of the model. The training data is: the location information, the normalized differential vegetation index, the textural features, and the solar irradiance; the detection data set includes: the location information, the normalized differential vegetation index, and the texture feature. Specifically, the training data set T and the detection data set D are respectively:
wherein,
representing the ith normalized differential vegetation index,
representing the ith texture feature, yiRepresenting the ith solar irradiance, n representing the sample size of the training data set, and m representing the sample size of the test data set.
And S400, constructing a multiple linear regression model suitable for irradiance detection based on the training data set.
After the training data set is obtained, regression analysis is carried out on the data of different solar irradiance by adopting a machine learning algorithm, and a multiple linear regression model suitable for irradiance detection is obtained.
A multiple linear regression model, predicts solar irradiance by establishing linear combinations among features,
the multiple linear regression model is as follows:
irrad(x)＝θ1x1+θ2x2+θ0+Rt
wherein irrad (x) represents a model by multivariate linear regressionResulting solar irradiance, θ1Representing the coefficient of correlation, theta, of the normalized differential vegetation index with solar irradiance2Representing the correlation coefficient, R, of image texture and solar irradiancetExpressed as a reference value, theta, of solar irradiance for each time period0Are model parameters.
The mean square error is taken as the loss function of the multiple linear regression model, and the loss function is solved to obtain a model parameter theta0、θ1And theta2. The mean square error is the sum of the squares of the residuals divided by the sample size n, and the sum of the squares of the residuals is the most commonly used parameter in linear regression.
The loss function is:
wherein,
t denotes transposition.
The coefficient θ of the multiple linear regression model can be obtained by the loss function J (θ):
θ＝(XTX)-1XTY
and after the correlation coefficient of each feature in the model is determined, a multi-linear regression model among the features NDVI, the image texture and the solar irradiance is obtained.
And S500, automatically identifying the irradiance of the detection data set by using a multiple linear regression model.
Specifically, the detection data set is input as feature data of the regression model, and numerical values of solar irradiance of the pedestrian network corresponding to each data in the data set can be obtained. And after the solar irradiation indexes of all position points of the pedestrian road network are obtained through the regression model, the position data of the pedestrian road network are reserved and used for map matching in the next step, and attribute display is carried out.
And S600, performing map matching based on the existing pedestrian network, and endowing the detected attribute information to position points in the pedestrian network, so that the road network basic data with the attribute information is obtained.
Specifically, based on the existing pedestrian road network OpenStreetMap, the solar irradiance detected by the multiple linear regression model is given to the matched position points in the pedestrian road network according to the position information, so that the basic data of the pedestrian road network has attribute information. In order to realize attribute addition to the basic data of the network, attribute matching needs to be performed based on the location information. And downloading the road network data by the OpenStreetMap as basic data, and adding the solar radiation index with the position information into the road network to obtain the road network data with the attribute. To achieve this, attribute matching will be performed based on GPS data. The specific matching process comprises:
(1) and sequentially calculating the distance between the GPS point corresponding to the detected solar irradiance and each position point in the pedestrian network.
The calculation formula of the distance is as follows:
wherein l and m respectively represent longitude and latitude of a position point corresponding to the detected solar irradiance, and lOSMAnd mOSMRespectively representing the longitude and latitude of the matched point in the OpenStreetMap.
(2) And selecting the point pair combination with the shortest distance, and endowing the solar irradiance of the point to the matched position point in the pedestrian road network, so that the road network basic data with the attribute information is obtained.
It is worth to be noted that the method extracts the normalized vegetation index (NDVI) and the image texture based on the remote sensing image to detect the irradiation index of the regional pedestrian road network. And collecting the solar irradiance of the road network by using a solar power tester, and taking the solar irradiance as a true value. And constructing a linear regression model between the NDVI and the image texture and the solar irradiance through a machine learning algorithm. Data is acquired in the same manner and the constructed model is used to detect solar irradiance of a pedestrian network in the area involved by the non-training data. The detected index is added to the data of the road network and is used as attribute information of the basic data of the road network, namely, environmental factors can be considered in path planning, and the service function of personalized navigation is realized.
In summary, the method for detecting the solar irradiation index of the pedestrian road network based on the remote sensing image provided by the invention comprises the following steps: obtaining a remote sensing image of a region to which a pedestrian road network belongs, and processing the remote sensing image to obtain a normalized difference vegetation index and textural features; randomly selecting position points of a road network part, and collecting solar irradiance and corresponding position information; obtaining the NDVI and the texture value corresponding to the solar irradiance of each position point through the position information, and fusing and constructing training data; residual data are fused with the NDVI and the texture value based on the position and serve as detection data for attribute detection; constructing a multiple linear regression model based on the training data set to obtain a regression model suitable for irradiance detection; and automatically detecting the irradiance of the detection data by using the obtained regression model, performing map matching based on the existing pedestrian road network, and giving the detected attribute information to position points in the road network to obtain the road network basic data with the attribute information. The solar irradiation indexes of road networks in different areas are automatically detected, the attributes of the pedestrian road network with attribute information are obtained after matching, and a certain data basis is provided for personalized navigation. In the process of planning the travel path of the pedestrian, the sun irradiation index is used as a measurement factor, the travel path with higher comfort can be recommended for the travel of the pedestrian, and the personalized demand is met.
It is to be understood that the invention is not limited to the examples described above, but that modifications and variations may be effected thereto by those of ordinary skill in the art in light of the foregoing description, and that all such modifications and variations are intended to be within the scope of the invention as defined by the appended claims.
Claims (10)
1. A pedestrian road network solar irradiation index detection method based on remote sensing images is characterized by comprising the following steps:
obtaining a remote sensing image of a region to which a pedestrian road network belongs, and processing the remote sensing image to obtain a normalized difference vegetation index and textural features;
randomly selecting position points of a road network part, and collecting solar irradiance and corresponding position information;
acquiring a normalized difference vegetation index and texture characteristics corresponding to the solar irradiance of each position point through the position information, and fusing and constructing a training data set; the residual data are used as a detection data set for attribute detection based on position information fusion normalization difference vegetation indexes and texture features;
constructing a multiple linear regression model suitable for irradiance detection based on the training dataset;
automatically identifying irradiance of the detection data set by using a multiple linear regression model;
map matching is carried out based on the existing pedestrian road network, and the detected attribute information is given to position points in the pedestrian road network, so that the road network basic data with the attribute information is obtained.
2. The method for detecting the solar irradiation index of the pedestrian road network based on the remote sensing image according to claim 1, wherein the step of obtaining the remote sensing image of the region to which the pedestrian road network belongs and processing the remote sensing image to obtain the normalized difference vegetation index and the texture feature comprises the following steps:
acquiring a remote sensing image and preprocessing the remote sensing image to obtain a preprocessed remote sensing image;
calculating the normalized difference vegetation index of the preprocessed remote sensing image by using the optical characteristics of the vegetation chlorophyll;
and calculating the texture characteristics of the preprocessed remote sensing image by adopting the gray level co-occurrence matrix.
3. The method for detecting the solar irradiation index of the pedestrian road network based on the remote sensing image according to claim 1, wherein the randomly selecting the position points of the road network part and collecting the solar irradiance and the corresponding position information comprises the following steps:
randomly selecting position points of a road network part, and acquiring solar irradiance through a solar tester;
and acquiring position information corresponding to the collecting point of the solar irradiance by a built-in sensor of the mobile phone.
4. The method for detecting the solar irradiation index of the pedestrian road network based on the remote sensing image as claimed in claim 3, wherein the acquiring of the solar irradiance through the solar tester comprises:
when the collecting position point is determined, the solar irradiance of the point is collected, meanwhile, the data of the nearby point is collected, and the average value is obtained to obtain the final solar irradiance of the point.
5. The method for detecting the solar irradiation index of the pedestrian road network based on the remote sensing image according to claim 1, wherein the normalized difference vegetation index and the textural features corresponding to the irradiance of each position point are obtained through the position information, and a training dataset is fused and constructed; the residual data are used as a detection data set for attribute detection based on position information fusion normalization difference vegetation indexes and textural features, and the method comprises the following steps:
data obtained by processing the remote sensing images are all provided with position information, the normalized difference vegetation index and the texture feature corresponding to the closest point of the solar irradiance position are obtained through comparison of GPS data, and a training data set is constructed after fusion; the other position points which are not used for constructing the training set only fuse the normalized difference vegetation fingers and the texture features as a detection data set of the model;
wherein T represents a training data set,
representing the ith normalized differential vegetation index,
representing the ith texture feature, yiRepresenting the ith solar irradiance, n represents the sample size of the training data set.
6. The method for detecting the solar radiation index of the pedestrian road network based on the remote sensing image as claimed in claim 5, wherein the multiple linear regression model is as follows:
irrad(x)＝θ1x1+θ2x2+θ0+Rt
wherein irrad (x) represents solar irradiance, θ, obtained by a multiple linear regression model1Representing the coefficient of correlation, theta, of the normalized differential vegetation index with solar irradiance2Representing the correlation coefficient, R, of image texture and solar irradiancetExpressed as a reference value, theta, of solar irradiance for each time period0Are model parameters.
7. The method for detecting the solar radiation index of the pedestrian road network based on the remote sensing image as claimed in claim 6, wherein the coefficient θ of the multiple linear regression model is obtained by the following loss function:
wherein,
j (theta) is a loss function, T represents transposition;
the coefficient theta of the multiple linear regression model is as follows:
θ＝(XTX)-1XTY。
8. the method for detecting the solar radiation index of the pedestrian road network based on the remote sensing image as claimed in claim 1, wherein the map matching is performed based on the existing pedestrian road network, and the attribute information obtained by the detection is given to the position points in the pedestrian road network, so that the road network basic data with the attribute information is obtained, and the method comprises the following steps:
based on the existing pedestrian road network OpenStreetMap, the solar irradiance detected by the multiple linear regression model is given to the matched position points in the pedestrian road network according to the position information, so that the basic data of the pedestrian road network has attribute information.
9. The method for detecting the solar irradiance index of the pedestrian road network based on the remote sensing image as claimed in claim 8, wherein the step of giving the solar irradiance obtained by the multivariate linear regression model detection to the matched position points in the pedestrian road network according to the position information comprises the steps of:
sequentially calculating the distance between a GPS point corresponding to the detected solar irradiance and each position point in the pedestrian network;
and selecting the point pair combination with the shortest distance, and endowing the solar irradiance of the point to the matched position point in the pedestrian road network, so that the road network basic data with the attribute information is obtained.
10. The method for detecting the solar radiation index of the pedestrian road network based on the remote sensing image according to claim 9, wherein the distance is as follows:
wherein l and m respectively represent the longitude and latitude of a position point corresponding to the detected solar irradiance, and lOSMAnd mOSMRespectively representing the longitude and latitude of the matched location point in the OpenStreetMap.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102288954A (en) * | 2011-08-01 | 2011-12-21 | 高吉喜 | Remote sensing estimation method for vegetation coverage of grassland |
| CN102708307A (en) * | 2012-06-26 | 2012-10-03 | 上海大学 | Vegetation index construction method applied to city |
| CN110186820A (en) * | 2018-12-19 | 2019-08-30 | 河北中科遥感信息技术有限公司 | Multisource data fusion and environomental pollution source and pollutant distribution analysis method |
| CN110852381A (en) * | 2019-11-11 | 2020-02-28 | 四川航天神坤科技有限公司 | Forest fire burned area extraction method and system |
-
2020
- 2020-04-24 CN CN202010332218.2A patent/CN111652056B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102288954A (en) * | 2011-08-01 | 2011-12-21 | 高吉喜 | Remote sensing estimation method for vegetation coverage of grassland |
| CN102708307A (en) * | 2012-06-26 | 2012-10-03 | 上海大学 | Vegetation index construction method applied to city |
| CN110186820A (en) * | 2018-12-19 | 2019-08-30 | 河北中科遥感信息技术有限公司 | Multisource data fusion and environomental pollution source and pollutant distribution analysis method |
| CN110852381A (en) * | 2019-11-11 | 2020-02-28 | 四川航天神坤科技有限公司 | Forest fire burned area extraction method and system |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115060231A (en) * | 2022-06-10 | 2022-09-16 | 中国矿业大学 | Soil erosion gully depth measuring and calculating method based on unmanned aerial vehicle image |
| CN115060231B (en) * | 2022-06-10 | 2023-11-07 | 中国矿业大学 | Unmanned aerial vehicle image-based soil erosion trench depth measuring and calculating method |
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