CN117456378A - Water conservancy digital twin base element realization method and system based on satellite remote sensing - Google Patents
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Abstract
The invention relates to a method and a system for realizing a water conservancy digital twin base element based on satellite remote sensing, and belongs to the technical field of remote sensing. The invention utilizes the super computing capability of a GEE platform to directly find a needed remote sensing image on the platform and perform preprocessing operation, reconstructs a vegetation index characteristic value capable of improving the accuracy of feature classification, then organically fuses the vegetation index characteristic value with a multi-scale segmentation method by using the engineering software, performs feature classification on the remote sensing image, performs precision inspection on the classified result, and then performs discrimination on water conservancy elements based on the feature classification result, thereby providing a method for discriminating whether a water body is a river, a reservoir or a lake by utilizing the aspect ratio and the area index of the water body form. The invention greatly improves the efficiency of distinguishing the water conservancy factors and provides powerful support for the construction of the data base plate in the digital twin base.
Description
Technical Field
The invention relates to a method and a system for realizing a water conservancy digital twin base element based on satellite remote sensing, and belongs to the technical field of remote sensing.
Background
Digital twinning, the english name Digital Twin (Digital Twin), is also called Digital mapping, digital mirroring. The method fully utilizes data such as a physical model, sensor update, operation history and the like, integrates simulation processes of multiple disciplines, multiple physical quantities, multiple scales and multiple probabilities, and completes mapping in a virtual space, thereby reflecting the full life cycle process of corresponding entity equipment. It can be seen that the digital twin technology is applied to the field of water conservancy management, and based on the digital version clone bodies of rivers and lakes, the latest states of the rivers and the lakes can be mastered, so that real-time monitoring and scientific management can be further carried out. With the continuous development of satellite remote sensing technology, the remote sensing technology becomes a 'thousand-in-eye' in the water conservancy industry and plays a very important role in digital twin construction. The remote sensing technology can provide multidimensional, multi-time space resolution and high timeliness data, the data can be used for constructing a data base plate platform of a digital twin base, the dynamic monitoring of geographical entities such as rivers, lakes, hydraulic engineering and the like is included, particularly, the remote sensing technology can be used for monitoring and distinguishing the hydraulic elements on a remote sensing image in real time, and comprehensive position information and change trend of the hydraulic elements can be provided.
The traditional water conservancy element monitoring method comprises the steps of downloading satellite images, extracting ground object types, and distinguishing water body elements and hydraulic engineering elements according to the extracted ground object types. The method not only needs to download the satellite images to the local, consumes a great deal of time and energy and is low in efficiency, but also occupies the storage space of the equipment. And then, classifying the classified ground objects into water categories, wherein the process also needs a large amount of manual participation, and the classified ground objects are inspected one by one, so that the labor cost is high.
Disclosure of Invention
The invention aims to overcome the defects and provide a method for realizing the water conservancy digital twin base element based on satellite remote sensing, which greatly improves the efficiency of remote sensing image processing.
The technical scheme adopted by the invention is as follows:
the method for realizing the water conservancy digital twin base element based on satellite remote sensing comprises the following steps:
s1, acquiring a remote sensing image of a required research area on a GEE platform and preprocessing the remote sensing image;
s2, constructing a vegetation index characteristic value which comprises a normalized vegetation index NDVI, a soil conditioning vegetation index SAVI and a normalized difference water body index NDWI;
s3, organically fusing the vegetation index characteristic values with a multi-scale segmentation method by using the ecognition software, and classifying the ground objects of the remote sensing images;
s4, performing precision inspection on the land feature classification result fused with the vegetation characteristic index, and performing the next step with good classification result;
and S5, distinguishing water conservancy elements including hydraulic buildings, rivers, reservoirs and lakes based on the ground object classification result, and distinguishing the water body as the river, the reservoir or the lake by utilizing the aspect ratio and the area index of the water body form.
In the method, the preprocessing in the step S1 is to perform orthographic correction and atmospheric correction on the image, then perform shadow removal and cloud mask operation, and acquire an annual synthetic image by using a median filtering method, so that all remote sensing images of the study year are synthesized into one, and the remote sensing image capable of clearly and completely displaying the earth surface coverage information of the study area is obtained.
The calculating method of the vegetation index eigenvalue in the step S2 specifically comprises the following steps:
，
，
，
wherein P is NIR : the reflectivity of the near-infrared band,
P RED : the reflectivity of the infrared band of wavelengths,
P GREEN : the reflectivity of the green light band,
l: soil adjustment coefficient.
In the step S3, the multi-scale segmentation method mainly divides pixels in the image into cultivated lands, woodlands, grasslands, buildings, water bodies and other lands; the method comprises the steps of (1) distinguishing a forest land, a water body and a building in an important way by setting a threshold value of a characteristic value, wherein the NDVI threshold value is set to be 0.46, distinguishing the forest land from a non-forest land, and the NDVI is larger than 0.46 and is expressed as the forest land; setting NDWI to 0.2, extracting water body, wherein the water body is represented by the NDWI being greater than 0.2, and comprises urban water body and river shallow water area; the SAVI is set to 0.2, and the buildings and the non-buildings are distinguished, wherein SAVI less than 0.2 is expressed as the buildings.
In step S4, using the confusion matrix accuracy checking method, the accuracy of the classification result is verified using the overall accuracy OA and Kappa coefficient, wherein:
，
，
，
TP: the prediction is positive, the actual is positive,
FN: the prediction is negative, actually positive,
FP: the prediction is positive, actually negative,
TN: the predicted time, predicted as negative, actually negative,
AA: the average accuracy rate of the data obtained by the method,
a Kappa coefficient greater than 0.6 after accuracy verification indicates good classification results.
In step S5, the method for discriminating a hydraulic building includes: extracting a building in the ground object classification, and if the building is regular rectangle and is at the side of the reservoir, indicating that the building is a reservoir dam; the construction with obvious water isolation in the middle of the river is denoted as a river barrage. When the water body is judged, the minimum bounding rectangle of the irregularly-shaped water body is taken as the boundary of the water body, the length and the width of the rectangle are regarded as the length and the width of the water body, and when the length-width ratio K1 of the water body is more than 44, the water body is represented as a river; when the aspect ratio K2 of the water body is more than 2.5 and the area S2 is more than 0.37 hectare, the water body is a reservoir, wherein the water body is represented by an engineering dam building around the water body; when the aspect ratio K3 of the water body is more than 2 and the area S3 is more than 10.37 hectares, the water body is a lake without engineering dam buildings around the water body.
The system for realizing the water conservancy digital twin base elements based on the satellite remote sensing comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the method for realizing the water conservancy digital twin base elements based on the satellite remote sensing when executing the program.
The beneficial effects of the invention are as follows:
the invention utilizes the strong super computing capability of the GEE platform to directly carry out batch processing calculation on the data on the platform, thoroughly changes the traditional mode of downloading the remote sensing data to the equipment and then carrying out processing and analysis, and greatly improves the efficiency of remote sensing image processing. In addition, the invention researches a novel distinguishing method for the river, the lake and the reservoir in the water conservancy element, and judges whether the water body is a river, a reservoir or a lake by calculating the aspect ratio and the area of the water body form. The method can realize automatic calculation, accurate river and lake element information can be obtained only by manually screening after calculation, the efficiency of distinguishing water conservancy elements is greatly improved, and powerful support is provided for the construction of a data base plate in a digital twin base.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a remote sensing image of a river basin obtained according to an embodiment of the present invention;
FIG. 3 is a schematic view of aspect ratios of rivers, reservoirs and lakes in accordance with an embodiment of the present invention; (a) is a river, (b) is a reservoir, and (c) is a lake;
FIG. 4 shows the discrimination results of the river, lake and reservoir according to the embodiment of the invention.
Detailed Description
The invention will be further illustrated with reference to specific examples.
Embodiment 1 implementation method of a water conservancy digital twin base element based on satellite remote sensing comprises the following steps (see fig. 1):
s1, acquiring a remote sensing image of a required research area on a GEE platform and preprocessing:
screening out high-resolution No. 1 satellite images meeting the requirements on a Google Earth Engine (GEE) platform, and preprocessing remote sensing images of a research area, wherein the method comprises the following specific steps: after the GEE platform (https:// development. Log. Cn/earth-engine /) selects satellite remote sensing data of a corresponding time phase, orthographic correction and atmospheric correction are carried out on the satellite remote sensing data, shadow removal and cloud mask operation are carried out by using a clout mask tool, and an annual synthetic image is obtained by using a median filtering method, so that all remote sensing images of a research year are synthesized into one remote sensing image which can clearly and completely display earth surface coverage information of a research area.
In this embodiment, a remote sensing image of a river basin in Shandong province is obtained, as shown in fig. 2.
S2, constructing a vegetation index characteristic value which comprises a normalized vegetation index NDVI, a soil conditioning vegetation index SAVI and a normalized difference water body index NDWI:
the vegetation index is used as a characteristic value to be applied to an object-oriented classification algorithm, so that the defect of the spectrum value of the remote sensing image can be effectively overcome, the characteristic value can be amplified, and the ground feature can be accurately extracted. In the invention, three characteristic values of a normalized vegetation index (NDVI), a soil-conditioned vegetation index (SAVI) and a Normalized Difference Water Index (NDWI) are mainly added to improve the accuracy of classification of the ground objects. The method comprises the following steps:
，
，
，
P NIR : the reflectivity of the near-infrared band,
P RED : the reflectivity of the infrared band of wavelengths,
P GREEN : the reflectivity of the green light band,
l: the soil adjustment coefficient is set to 0.6 in the present invention.
S3, organically fusing the vegetation index characteristic values with a multi-scale segmentation method by using the ecognition software, and classifying the ground objects of the remote sensing image:
the ecognition software (Yi Kang Ruanjian) is an object-oriented classification system and is image processing software based on machine learning and deep learning algorithms. The multi-scale segmentation method is a bottom-up segmentation method, and on the premise of minimum average heterogeneity among targets and maximum uniformity among internal pixels, the image segmentation based on the region fusion technology is realized by combining adjacent pixels or small segmentation objects. The method comprises the following specific steps: image data is imported, a segmentation Process is written in a Process Tree dialog box, a Process New is inserted, the Process New is named as segmentation, a multi-Scale segmentation multiresolution segmentation is adopted as a selection algorithm, a segmentation parameter Scale parameter is set to be 0.4, a shape factor parameter (shape) is set to be 0.2, a compactness parameter (compact) is set to be 0.5, and then an operation is executed by clicking the Process.
The accuracy of land feature classification is improved by integrating the vegetation index characteristic values in the multi-scale segmentation method, and forest lands, water bodies and buildings are distinguished in an important way by setting the threshold value of the characteristic values. Wherein, the NDVI threshold is set to be 0.46, the woodland and the non-woodland can be obviously distinguished, and the NDVI is more than 0.46 and is expressed as the woodland; the NDWI is set to be 0.2, so that the water body can be extracted better, and the NDWI is more than 0.2 and is expressed as the water body, including urban water bodies and river shallow water areas; setting the SAVI to 0.2 can distinguish between buildings and non-buildings, where SAVI less than 0.2 is expressed as a building. The method comprises the following specific steps: the method comprises the steps of inserting a right key into a dialog box of a Process Tree, modifying the name into a fused vegetation index characteristic value, then pressing the dialog box of the Process Tree, inserting a right key into the dialog box of the Process Tree, selecting the dialog box of the Process Tree, and selecting the category to be configured from the dialog box of the Active class of the right table, namely selecting the category to be classified as cultivated land, woodland, grassland, building, water body and other land. Then selecting the ' layers value ' under the ' objects Features ' from the ' Features ', double clicking the ' means ' and the ' Standard deviation ', selecting the ' Create new ' Arithmetic Feature ' under the ' custom ' and inputting the algorithm formula of the NDVI, and clicking for execution. Repeating the steps on the executed result until the input algorithm formula is modified into SAVI and NDWI formulas, and outputting a final result which is the ground feature classification result fused with the vegetation characteristic index. And then, carrying out accuracy inspection on the classification result.
S4, carrying out precision inspection on the land feature classification result fused with the vegetation characteristic index, and carrying out the following steps:
the precision test of the land feature classification result fused with the vegetation characteristic index is an essential step for proving the reliability of the classification result, the invention randomly and uniformly selects 100 sample points of various land utilization types in a research area, using a confusion matrix Accuracy checking method, the Accuracy of the classification result is verified using an Overall Accuracy (OA) and Kappa coefficients, wherein:
，
，
，
TP: the prediction is positive, the actual is positive,
FN: the prediction is negative, actually positive,
FP: the prediction is positive, actually negative,
TN: the predicted time, predicted as negative, actually negative,
AA: average accuracy (average of accuracy per class),
the Kappa coefficient after accuracy verification is greater than 0.6 indicates that the classification result is good, and the ground feature classification result fused with the vegetation characteristic index can be used for carrying out next water conservancy element discrimination.
S5, distinguishing water conservancy elements including hydraulic buildings, rivers, reservoirs and lakes based on the ground object classification result, and distinguishing the water body as the river, the reservoir or the lake by utilizing the aspect ratio and the area index of the water body form:
and analyzing the water body morphology according to the land feature classification result fused with the vegetation characteristic index to judge the water conservancy elements, wherein the water conservancy element classification method mainly comprises hydraulic buildings, rivers, reservoirs and lakes. The distinguishing method for the hydraulic building comprises the following steps: extracting a building in the ground object classification, and if the building is regular rectangle and is at the side of the reservoir, indicating that the building is a reservoir dam; the construction with obvious water isolation in the middle of the river is denoted as a river barrage. When the water body is judged, the minimum bounding rectangle of the irregularly-shaped water body is taken as the boundary of the water body, the length and the width of the rectangle are regarded as the length and the width of the water body, and when the length-width ratio K1 of the water body is more than 44, the water body is represented as a river; when the aspect ratio K2 of the water body is more than 2.5 and the area S2 is more than 0.37 hectare, the water body is a reservoir, wherein the water body is represented by an engineering dam building around the water body; when the aspect ratio K3 of the water body is more than 2 and the area S3 is more than 10.37 hectares, the water body is a lake without engineering dam buildings around the water body. In particular to
K1=L1/B1 ，
K2=L2/B2 ，
K3=L3/B3 ，
S2=L2B2 ，
S3=L3B3 ，
Wherein, K1: the aspect ratio of the river,
k2: the aspect ratio of the reservoir,
k3: the aspect ratio of the lake,
s2: area of reservoir (10) 4 m 2 ），
S3: area of lake (10) 4 m 2 ），
L1: the length (m) of the river,
l2: length of reservoir (m),
l3: length of lake (m),
b1: the width (m) of the river,
b2: width (m) of the reservoir,
b3: width (m) of lake.
The aspect ratio of the river, reservoir and lake of the embodiment is schematically shown in fig. 3, wherein l1=594m, b1=11m and k1=54; l2=106 m, b2=39 m, k2=2.71, s2=0.4110 4 m 2 ；L3=732m，B3=149m，K3=4.9，S3=10.910 4 m 2 . The result of distinguishing the water conservancy elements on the ground object classification result is shown in fig. 4.
Example 2: the system for realizing the water conservancy digital twin base elements based on the satellite remote sensing comprises a memory, a processor and a computer program which is stored in the memory and can run on the processor, wherein the processor realizes the method for realizing the water conservancy digital twin base elements based on the satellite remote sensing as described in the embodiment 1 when executing the program.
The invention is further described above in connection with embodiments, to which the scope of protection of the invention is not limited.
Claims (8)
1. The method for realizing the water conservancy digital twin base element based on satellite remote sensing is characterized by comprising the following steps:
s1, acquiring a remote sensing image of a required research area on a GEE platform and preprocessing the remote sensing image;
s2, constructing a vegetation index characteristic value which comprises a normalized vegetation index NDVI, a soil conditioning vegetation index SAVI and a normalized difference water body index NDWI;
s3, organically fusing the vegetation index characteristic values with a multi-scale segmentation method by using the ecognition software, and classifying the ground objects of the remote sensing images;
s4, performing precision inspection on the land feature classification result fused with the vegetation characteristic index, and performing the next step with good classification result;
and S5, distinguishing water conservancy elements including hydraulic buildings, rivers, reservoirs and lakes based on the ground object classification result, and distinguishing the water body as the river, the reservoir or the lake by utilizing the aspect ratio and the area index of the water body form.
2. The method for realizing the satellite remote sensing-based water conservancy digital twin base elements according to claim 1, wherein the preprocessing in the step S1 is to perform orthographic correction and atmospheric correction on images, then perform shadow removal and cloud mask operation, and acquire an annual synthetic image by using a median filtering method, so that all remote sensing images of a research year are synthesized into one, and a remote sensing image capable of clearly and completely displaying earth surface coverage information of a research area is obtained.
3. The method for realizing the water conservancy digital twin base element based on satellite remote sensing according to claim 1, wherein the calculating method of the vegetation index eigenvalue in the step S2 is specifically as follows:
，
，
，
wherein P is NIR : the reflectivity of the near-infrared band,
P RED : the reflectivity of the infrared band of wavelengths,
P GREEN : the reflectivity of the green light band,
l: soil adjustment coefficient.
4. The method for realizing the water conservancy digital twin base element based on satellite remote sensing according to claim 1, wherein in the step S3, the multi-scale segmentation method mainly divides pixels in the image into cultivated land, woodland, grassland, buildings, water body and other land; the method comprises the steps of (1) distinguishing a forest land, a water body and a building in an important way by setting a threshold value of a characteristic value, wherein the NDVI threshold value is set to be 0.46, distinguishing the forest land from a non-forest land, and the NDVI is larger than 0.46 and is expressed as the forest land; setting NDWI to 0.2, extracting water body, wherein the water body is represented by the NDWI being greater than 0.2, and comprises urban water body and river shallow water area; the SAVI is set to 0.2, and the buildings and the non-buildings are distinguished, wherein SAVI less than 0.2 is expressed as the buildings.
5. The method for implementing a satellite remote sensing-based hydrodigital twinning base element according to claim 1, wherein in step S4, the accuracy of the classification result is verified by using the overall accuracy OA and Kappa coefficients by using a confusion matrix accuracy verification method, wherein:
，
，
，
TP: the prediction is positive, the actual is positive,
FN: the prediction is negative, actually positive,
FP: the prediction is positive, actually negative,
TN: the predicted time, predicted as negative, actually negative,
AA: the average accuracy rate of the data obtained by the method,
a Kappa coefficient greater than 0.6 after accuracy verification indicates good classification results.
6. The method for realizing the hydrodigital twin base element based on the satellite remote sensing according to claim 1, wherein in the step S5, the method for distinguishing the hydraulic building is as follows: extracting a building in the ground object classification, and if the building is regular rectangle and is at the side of the reservoir, indicating that the building is a reservoir dam; the construction with obvious water isolation in the middle of the river is denoted as a river barrage.
7. The method for realizing the water conservancy digital twin base element based on satellite remote sensing according to claim 1, wherein in the step S5, a minimum bounding rectangle of an irregularly-shaped water body is taken as a boundary of the water body when the water body is judged, and the length and the width of the rectangle are taken as the length and the width of the water body, wherein when the length-width ratio K1 of the water body is more than 44, the water body is represented as a river; when the aspect ratio K2 of the water body is more than 2.5 and the area S2 is more than 0.37 hectare, the water body is a reservoir, wherein the water body is represented by an engineering dam building around the water body; when the aspect ratio K3 of the water body is more than 2 and the area S3 is more than 10.37 hectares, the water body is a lake without engineering dam buildings around the water body.
8. A satellite remote sensing-based hydraulic digital twin base element implementation system, comprising a memory, a processor and a computer program stored on the memory and operable on the processor, wherein the processor implements the satellite remote sensing-based hydraulic digital twin base element implementation method according to any one of claims 1-7 when executing the program.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105303184A (en) * | 2015-11-25 | 2016-02-03 | 中国矿业大学(北京) | Method for accurately identifying ground features in satellite remote-sensing image |
| CN107609526A (en) * | 2017-09-21 | 2018-01-19 | 吉林大学 | Rule-based fine dimension city impervious surface rapid extracting method |
| CN112051222A (en) * | 2020-08-30 | 2020-12-08 | 山东锋士信息技术有限公司 | River and lake water quality monitoring method based on high-resolution satellite image |
| CN112990657A (en) * | 2021-02-06 | 2021-06-18 | 首都师范大学 | Method for evaluating wetland degradation through long-time satellite remote sensing |
| CN113724389A (en) * | 2021-09-06 | 2021-11-30 | 中国科学院东北地理与农业生态研究所 | Wetland mapping method based on object-oriented hierarchical decision tree |
| CN114359243A (en) * | 2022-01-10 | 2022-04-15 | 首都师范大学 | Seasonal small micro-wetland dynamic monitoring method |
| CN114724049A (en) * | 2022-04-11 | 2022-07-08 | 中国科学院南京地理与湖泊研究所 | Inland culture pond water surface identification method based on high-resolution remote sensing image data |
| CN115205688A (en) * | 2022-09-07 | 2022-10-18 | 浙江甲骨文超级码科技股份有限公司 | Tea tree planting area extraction method and system |
| CN116030352A (en) * | 2023-03-29 | 2023-04-28 | 山东锋士信息技术有限公司 | Long-time-sequence land utilization classification method integrating multi-scale segmentation and super-pixel segmentation |
| CN116129276A (en) * | 2023-03-16 | 2023-05-16 | 重庆市气象科学研究所(重庆市生态气象和卫星遥感中心、重庆市农业气象中心) | Remote sensing fine classification method for main grain crops in terrain complex region |
| CN117114371A (en) * | 2023-10-24 | 2023-11-24 | 山东锋士信息技术有限公司 | Modern water network flood prevention monitoring and scheduling method and system based on satellite remote sensing |
-
2023
- 2023-12-20 CN CN202311753557.8A patent/CN117456378A/en active Pending
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105303184A (en) * | 2015-11-25 | 2016-02-03 | 中国矿业大学(北京) | Method for accurately identifying ground features in satellite remote-sensing image |
| CN107609526A (en) * | 2017-09-21 | 2018-01-19 | 吉林大学 | Rule-based fine dimension city impervious surface rapid extracting method |
| CN112051222A (en) * | 2020-08-30 | 2020-12-08 | 山东锋士信息技术有限公司 | River and lake water quality monitoring method based on high-resolution satellite image |
| CN112990657A (en) * | 2021-02-06 | 2021-06-18 | 首都师范大学 | Method for evaluating wetland degradation through long-time satellite remote sensing |
| CN113724389A (en) * | 2021-09-06 | 2021-11-30 | 中国科学院东北地理与农业生态研究所 | Wetland mapping method based on object-oriented hierarchical decision tree |
| CN114359243A (en) * | 2022-01-10 | 2022-04-15 | 首都师范大学 | Seasonal small micro-wetland dynamic monitoring method |
| CN114724049A (en) * | 2022-04-11 | 2022-07-08 | 中国科学院南京地理与湖泊研究所 | Inland culture pond water surface identification method based on high-resolution remote sensing image data |
| CN115205688A (en) * | 2022-09-07 | 2022-10-18 | 浙江甲骨文超级码科技股份有限公司 | Tea tree planting area extraction method and system |
| CN116129276A (en) * | 2023-03-16 | 2023-05-16 | 重庆市气象科学研究所(重庆市生态气象和卫星遥感中心、重庆市农业气象中心) | Remote sensing fine classification method for main grain crops in terrain complex region |
| CN116030352A (en) * | 2023-03-29 | 2023-04-28 | 山东锋士信息技术有限公司 | Long-time-sequence land utilization classification method integrating multi-scale segmentation and super-pixel segmentation |
| CN117114371A (en) * | 2023-10-24 | 2023-11-24 | 山东锋士信息技术有限公司 | Modern water network flood prevention monitoring and scheduling method and system based on satellite remote sensing |
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