CN112085751A - Cloud layer height estimation method based on cloud image shadow matching algorithm - Google Patents

Cloud layer height estimation method based on cloud image shadow matching algorithm Download PDF

Info

Publication number
CN112085751A
CN112085751A CN202010784447.8A CN202010784447A CN112085751A CN 112085751 A CN112085751 A CN 112085751A CN 202010784447 A CN202010784447 A CN 202010784447A CN 112085751 A CN112085751 A CN 112085751A
Authority
CN
China
Prior art keywords
shadow
cloud
image
point
ground
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202010784447.8A
Other languages
Chinese (zh)
Other versions
CN112085751B (en
Inventor
黄洵
戚军
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Zhejiang University of Technology ZJUT
Original Assignee
Zhejiang University of Technology ZJUT
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Zhejiang University of Technology ZJUT filed Critical Zhejiang University of Technology ZJUT
Priority to CN202010784447.8A priority Critical patent/CN112085751B/en
Publication of CN112085751A publication Critical patent/CN112085751A/en
Application granted granted Critical
Publication of CN112085751B publication Critical patent/CN112085751B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/10Segmentation; Edge detection
    • G06T7/13Edge detection
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/30Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/60Analysis of geometric attributes
    • G06T7/66Analysis of geometric attributes of image moments or centre of gravity
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00Image analysis
    • G06T7/70Determining position or orientation of objects or cameras
    • G06T7/73Determining position or orientation of objects or cameras using feature-based methods
    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00Indexing scheme for image analysis or image enhancement
    • G06T2207/30Subject of image; Context of image processing
    • G06T2207/30181Earth observation

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Geometry (AREA)
  • Image Processing (AREA)

Abstract

The invention provides a cloud layer height estimation method based on a cloud image shadow matching algorithm, which comprises the following steps: synchronously shooting cloud pictures and ground shadow pictures and preprocessing the cloud pictures and the ground shadow pictures, selecting cloud cluster matching points and shadow matching points through a cloud cluster shadow matching algorithm, and solving the cloud layer height according to the actual spatial relationship of the matching points. The technical scheme provided by the invention accurately describes the actual spatial relationship between the ground shadow and the sky cloud layer, simplifies and establishes the cloud layer height solving equation, and conveniently and accurately realizes the estimation of the cloud layer height.

Description

Cloud layer height estimation method based on cloud image shadow matching algorithm
Technical Field
The invention relates to the field of cloud layer height measurement and calculation, in particular to a cloud layer height estimation method based on a cloud layer shadow matching algorithm.
Background
In recent years, the cloud layer height is increasingly applied in various industries, and various methods for measuring the cloud layer height are developed.
At present, three methods are mainly used for actually measuring the cloud height: the cloud height is measured by a ball at night, the cloud height is measured by a laser cloud measuring instrument and the cloud height is measured by a cloud curtain lamp. The cloud height measured by the cloud curtain ball is measured by the hydrogen balloon, so that the cost is low but the precision is poor; the laser cloud measuring instrument measures through laser pulses, the precision is high, but the instrument is complex and the cost is high; the cloud curtain lamp is used for measuring the cloud height at night, and the space-time application range is small.
The method for measuring the cloud layer height has respective limitations and provides less cloud layer information, so that a convenient, quick and accurate cloud layer height estimation method with rich information is needed.
Disclosure of Invention
The present invention is directed to overcome the above disadvantages of the prior art, and provides a cloud layer height estimation method based on a cloud image shadow matching algorithm, so as to conveniently and accurately obtain a height parameter of a current sky cloud layer in an actual situation.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a cloud layer height estimation method based on a cloud image shadow matching algorithm comprises the following steps:
step 1, determining the longitude and latitude of a cloud picture shooting place T, synchronously shooting a cloud picture and a ground shadow image, and recording the shooting time;
step 2, determining the solar altitude angle beta according to the longitude and latitude of the shooting time and the shooting place2
Step 3, identifying the cloud picture and the ground shadow image projected on the ground, and extracting edge information of the cloud picture and the ground shadow image;
step 4, determining the mapping relation between the cloud cluster on the cloud picture and the shadow on the ground shadow image by adopting a cloud picture shadow matching algorithm, and selecting a cloud cluster matching point C on the cloud picture and a shadow matching point S on the ground shadow image;
step 5, determining a cloud cluster azimuth angle alpha according to the cloud cluster matching point C1And cloud altitude angle alpha2
Step 6, determining an actual shadow point R on the actual ground shadow according to the shadow matching point S, and determining an azimuth angle beta of the actual shadow point R1Distance l between actual shadow point R and cloud picture shooting place TTR
And 7, establishing an equation to estimate the height of the cloud layer, wherein the specific calculation equation is as follows, and H is the height of the cloud layer.
Figure BDA0002621422160000021
Preferably, the step 2 specifically includes:
step 2-1, according to the shooting time t, a calculation formula of a time angle omega is as follows, wherein t is 24-hour system time;
ω=(t-12)×15 (2)
step 2-2, according to the solar declination angle and the local latitude
Figure BDA0002621422160000022
And the hour angle omega, the solar altitude angle beta2The calculation formula is as follows:
Figure BDA0002621422160000023
preferably, the step 3 specifically includes:
3-1, eliminating a shading band and a bracket shading area in the cloud picture, repairing the eliminated area, extracting an effective area of the cloud picture, and finally performing edge extraction on the cloud picture and turning the image by taking the north-south direction as an axis to obtain edge information of the cloud picture;
and 3-2, correcting the ground shadow image according to the shooting angle, binarizing the image, distinguishing a shielded area and a non-shielded area, and finally performing edge extraction to obtain edge information of the ground shadow image.
Preferably, the step 4 specifically includes:
step 4-1, sequentially recording all edge pixel points on the edge image of the cloud picture by using an array Point from left to right and from top to bottom, wherein N pixel points are total, Point (j) represents the jth Point in the array, and j is 1,2, … … and N;
step 4-2, zooming the ground shadow edge image for the ith time, and determining a first shadow edge pixel point as a shadow matching point S under the ith zooming from left to right and from top to bottom after zoomingiI is 1,2, … …, M is the total number of zooms;
step 4-3, with point SiOverlapping the cloud picture and the ground shadow edge image by taking point (j) as the overlapping starting point of the ground shadow edge image;
step 4-4, subtracting the superposed image from the edge image of the original cloud image to obtain an error pixel image, and calculating the number of pixel points on the error pixel image as a superposition error value eij,i=1,2,……,M,j=1,2,……,N;
Step 4-5, judging whether points in the Point array are overlapped as the cloud picture edge image overlapping starting points or not, if so, entering the next step, and if not, returning to the step 4-3 for carrying out;
step 4-6, judging whether all the zooming times are finished, if so, entering the next step, and if not, returning to the step 4-2 for processing;
step 4-7, comparing all the superposition error values eijI is 1,2, … …, M, j is 1,2, … …, N, and the subscripts i and j corresponding to the minimum superposition error value are taken, then point (j) point is recorded as the cloud cluster matching point C, S on the cloud cluster mapiIs recorded as a shadow matching point S on the ground shadow image.
Preferably, the step 5 specifically includes:
step 5-1, establishing a cloud picture image coordinate system by taking the circle center of the turned cloud picture edge image as an origin, the east direction as the positive direction of an X axis and the north direction as the positive direction of a Y axis, and recording the coordinate of a cloud cluster matching point C as (X)2,y2) The cloud azimuth calculation formula is as follows:
Figure BDA0002621422160000031
step 5-2, according to the maximum cloud image radius pixel value r of the cloud image shooting equipment, the maximum zenith angle theta corresponding to the maximum radius and the coordinate (x) of the cloud cluster matching point C2,y2) The cloud cluster height angle calculation formula is as follows:
Figure BDA0002621422160000032
preferably, the step 6 specifically includes:
step 6-1, establishing a shadow image coordinate system by taking the southwest angle of the shadow edge image as an origin, the east direction as the positive direction of an X axis and the north direction as the positive direction of a Y axis, and reading the coordinate of a shadow matching point S as (X, Y); and establishing an actual shadow coordinate system by taking the cloud picture shooting place T as an origin, the east direction as the positive direction of the X axis and the north direction as the positive direction of the Y axis. Let the actual shadow point R coordinate be (x)1,y1)。
Step 6-2, because the coordinates (x, y) of the shadow matching point are the coordinates of the shadow edge image, the coordinates need to be converted into the coordinates under the established actual shadow coordinate system, that is, the coordinates of the actual shadow point R. According to the S coordinate (x, y) of the shadow matching point and the maximum coordinate (x) of the shadow imagemax,ymax) The southwest angular coordinate (a, b) of the ground actual shadow, the length L and the width W of the ground actual shadow image, and the R coordinate (x) of the actual shadow point1,y1) The calculation formula is as follows:
Figure BDA0002621422160000033
step 6-3, according to the R coordinate (x) of the actual shadow point1,y1) Azimuth angle of ground shadow beta1The calculation formula is as follows:
Figure BDA0002621422160000034
6-4, according to the R coordinate (x) of the actual shadow point1,y1) Distance l between ground actual shadow point R and cloud picture shooting deviceTRThe calculation formula of (a) is as follows:
Figure BDA0002621422160000041
the technical scheme of the invention has the following beneficial effects:
the cloud image and the ground shadow image at the same time are processed by the cloud image shadow matching algorithm, so that the height of a cloud layer is conveniently and quickly estimated, and a foundation is laid for the prediction and positioning of the ground shadow. The invention has reliable calculation result and strong real-time performance, and the provided cloud cluster information is rich and the time space application range is wide.
Drawings
FIG. 1 is a diagram of the preprocessing flow and results of a cloud image of the present invention.
Fig. 2a is an inverted cloud image edge image of the present invention, and fig. 2b is a schematic view of a principle calculation of photographing.
FIG. 3 is a schematic diagram of the calculation of the ground shadow edge image of the present invention.
FIG. 4 is a schematic diagram of the actual spatial relationship calculation of the present invention.
FIG. 5 is a simplified cloud height calculation model diagram of the present invention.
Detailed Description
The invention is further described below in conjunction with the detailed examples and figures following the steps of the summary of the invention.
A cloud layer height estimation method based on a cloud image shadow matching algorithm comprises the following steps:
step 1, synchronously shooting a cloud picture and a ground shadow image by taking Hangzhou as a shooting place and taking 14 hours of 6 months, 21 days and 14 days;
step 2, according to the longitude and latitude and the shooting time of Hangzhou, the solar altitude angle beta is calculated2
Step 2-1, calculating the time angle omega according to the shooting time 14 time, wherein the specific calculation process is as follows:
ω=(14-12)×15=30°; (9)
step 2-2, calculating the solar altitude angle beta according to the solar declination at the moment of 23.5 degrees, the local latitude of 30.3 degrees and the time angle of 30 degrees2The specific calculation process is as follows:
β2=arcsin(sin23.5°sin30.3°+cos23.5°cos30.3°cos30°)=62.485° (10)
step 3, identifying the cloud picture and the ground shadow image projected on the ground, and extracting edge information of the cloud picture and the ground shadow image;
and 3-1, eliminating the area on the cloud picture, which is shielded by the shadow generated by the shading band and the bracket, because some cloud picture shooting equipment adopts the shading band and the camera bracket, and repairing the eliminated area of the cloud picture to a certain extent by adopting a repairing algorithm after the elimination is finished. Then extracting a circular effective information area in the cloud picture to reduce unnecessary operation, finally extracting the edge of the image by adopting an edge operator to obtain a cloud picture edge image, and finally performing horizontal turning by taking the north-south direction as an axis to obtain a final cloud cluster edge image because the photographed image and the actual sky image have a mirror image relationship, wherein the processing flow and the final result image are shown in an attached figure 1;
step 3-2, the ground shadow image is shot by a high-definition camera which is erected at a certain angle, so that firstly, image correction is carried out according to the shooting angle to reflect the real ground shadow image, secondly, the corrected image is subjected to binarization processing to obtain clearer shadow regions and non-shadow regions respectively, and finally, the edge of the image is extracted by adopting an edge operator to obtain a ground shadow edge image;
step 4, determining the mapping relation between the cloud cluster on the cloud picture and the shadow on the ground shadow image by adopting a cloud picture shadow matching algorithm, and selecting a cloud cluster matching point C on the cloud picture and a shadow matching point S on the ground shadow image;
step 5, as shown in the attached figure 2, determining the cloud azimuth angle alpha according to the cloud matching point C1And cloud altitude angle alpha2
Step 5-1, establishing a cloud picture image coordinate system by taking the circle center of the turned cloud picture edge image as an origin, the east direction as the positive direction of an X axis and the north direction as the positive direction of a Y axis, and recording the coordinate of a cloud cluster matching point C as (X)2,y2) In this example, assuming that the coordinates of the point C are (75 pixels, -75 pixels), the cloud azimuth calculation formula is as follows:
Figure BDA0002621422160000051
step 5-2, shooting according to the cloud pictureThe pixel value r of the maximum cloud image radius, the maximum zenith angle theta corresponding to the maximum radius and the coordinate (x) of the cloud cluster matching point C2,y2) Calculating the cloud cluster altitude angle alpha2In this example, let r be 500 pixels, θ be 60 degrees, and the cloud altitude α2The specific calculation formula is as follows:
Figure BDA0002621422160000052
step 6, as shown in fig. 3 and fig. 4, determining an actual shadow point R on the actual ground shadow according to the shadow matching point S, and determining an azimuth angle β of the actual shadow point R1Distance l between actual shadow point R and cloud picture shooting equipmentTR
Step 6-1, establishing a shadow image coordinate system by taking the southwest angle of the shadow edge image as an origin, the east direction as the positive direction of an X axis and the north direction as the positive direction of a Y axis, and reading the coordinate of a shadow matching point S as (X, Y); taking a cloud picture shooting place T as an origin, setting the east direction as the positive direction of an X axis and the north direction as the positive direction of a Y axis to establish an actual shadow coordinate system, and recording the coordinates of an actual shadow point R as (X)1,y1) In this example, the coordinate of the shadow matching point S is assumed to be (500 pixels );
step 6-2, because the coordinates (x, y) of the shadow matching point are shadow edge image coordinates, the coordinates need to be converted into the coordinates under the established actual shadow coordinate system, namely the coordinates of the actual shadow point R; according to the S coordinate (x, y) of the shadow matching point and the maximum coordinate (x) of the shadow imagemax,ymax) Calculating the coordinate (x) of an actual shadow point R by the coordinates (a, b) of the southwest angle of the actual shadow on the ground, the length L and the width W of the actual shadow image on the ground1,y1) In this example, it is assumed that the maximum coordinate of the shadow image is (1000 pixels, 500 pixels), the coordinate of the real-ground shadow southwest angle is (500m ), and the length L and the width W of the real-ground shadow image are 200m and 100m, respectively; the specific calculation formula is as follows:
Figure BDA0002621422160000061
step 6-2, according to the R coordinate (600m ) of the actual shadow point and the azimuth angle beta of the ground shadow1The calculation formula is as follows:
Figure BDA0002621422160000062
6-3, according to the coordinates (600m ) of the actual shadow point R, the distance l between the actual shadow point R on the ground and the cloud picture shooting equipmentTRThe calculation formula of (a) is as follows:
Figure BDA0002621422160000063
and 7, establishing an equation about H by utilizing a cosine theorem according to the simplified model diagram shown in the attached figure 5, and solving, wherein the point P represents a point of the cloud cluster matching point C in the actual sky, the point G is a vertical projection point of the point P, and the specific equation is as follows:
Figure BDA0002621422160000064
the solution yields H2498.8 m, i.e. the cloud height under the assumption of this example condition is 2498.8 m.
The technical scheme provided by the invention accurately describes the actual spatial relationship between the ground shadow and the sky cloud layer, simplifies and establishes the cloud layer height solving equation, and conveniently and accurately realizes the estimation of the cloud layer height.
It should be noted that the above-mentioned embodiments are only for illustrating the technical solutions of the present application and not for limiting the scope of protection thereof, and although the present application is described in detail with reference to the above-mentioned embodiments, those skilled in the art should understand that after reading the present application, they can make various changes, modifications or equivalents to the specific embodiments of the application, but these changes, modifications or equivalents are all within the scope of protection of the claims to be filed.

Claims (6)

1. A cloud layer height estimation method based on a cloud image shadow matching algorithm comprises the following steps:
step 1, determining the longitude and latitude of a cloud picture shooting place T, synchronously shooting a cloud picture and a ground shadow image, and recording the shooting time;
step 2, determining the solar altitude angle beta according to the longitude and latitude of the shooting time and the shooting place2
Step 3, identifying the cloud picture and the ground shadow image projected on the ground, and extracting edge information of the cloud picture and the ground shadow image;
step 4, determining the mapping relation between the cloud cluster on the cloud picture and the shadow on the ground shadow image by adopting a cloud picture shadow matching algorithm, and selecting a cloud cluster matching point C on the cloud picture and a shadow matching point S on the ground shadow image;
step 5, determining a cloud cluster azimuth angle alpha according to the cloud cluster matching point C1And cloud altitude angle alpha2
Step 6, determining an actual shadow point R on the actual ground shadow according to the shadow matching point S, and determining an azimuth angle beta of the actual shadow point R1Distance l between actual shadow point R and cloud picture shooting place TTR
And 7, establishing an equation to estimate the height of the cloud layer, wherein the specific calculation equation is as follows, and H is the height of the cloud layer.
Figure FDA0002621422150000011
2. The estimation method according to claim 1, characterized in that: the step 2 specifically comprises:
step 2-1, according to the shooting time t, a calculation formula of a time angle omega is as follows, wherein t is 24-hour system time;
ω=(t-12)×15 (2)
step 2-2, according to the solar declination angle and the local latitude
Figure FDA0002621422150000012
And the hour angle omega, the solar altitude angle beta2The calculation formula is as follows:
Figure FDA0002621422150000013
3. the estimation method according to claim 1, characterized in that: the step 3 specifically includes:
3-1, eliminating a shading band and a bracket shading area in the cloud picture, repairing the eliminated area, extracting an effective area of the cloud picture, and finally performing edge extraction on the cloud picture and turning the image by taking the north-south direction as an axis to obtain edge information of the cloud picture;
and 3-2, correcting the ground shadow image according to the shooting angle, binarizing the image, distinguishing a shielded area and a non-shielded area, and finally performing edge extraction to obtain edge information of the ground shadow image.
4. The estimation method according to claim 1, characterized in that: the step 4 specifically includes:
step 4-1, sequentially recording all edge pixel points on the edge image of the cloud picture by using an array Point from left to right and from top to bottom, wherein N pixel points are total, Point (j) represents the jth Point in the array, and j is 1,2, … … and N;
step 4-2, zooming the ground shadow edge image for the ith time, and determining a first shadow edge pixel point as a shadow matching point S under the ith zooming from left to right and from top to bottom after zoomingiI is 1,2, … …, M is the total number of zooms;
step 4-3, with point SiOverlapping the cloud picture and the ground shadow edge image by taking point (j) as the overlapping starting point of the ground shadow edge image;
step 4-4, subtracting the superposed image from the edge image of the original cloud image to obtain an error pixel image, and calculating the number of pixel points on the error pixel image as a superposition error value eij,i=1,2,……,M,j=1,2,……,N;
Step 4-5, judging whether points in the Point array are overlapped as the cloud picture edge image overlapping starting points or not, if so, entering the next step, and if not, returning to the step 4-3 for carrying out;
step 4-6, judging whether all the zooming times are finished, if so, entering the next step, and if not, returning to the step 4-2 for processing;
step 4-7, comparing all the superposition error values eijI is 1,2, … …, M, j is 1,2, … …, N, and the subscripts i and j corresponding to the minimum superposition error value are taken, then point (j) point is recorded as the cloud cluster matching point C, S on the cloud cluster mapiIs recorded as a shadow matching point S on the ground shadow image.
5. The estimation method according to claim 1, characterized in that: the step 5 specifically includes:
step 5-1, establishing a cloud picture image coordinate system by taking the circle center of the turned cloud picture edge image as an origin, the east direction as the positive direction of an X axis and the north direction as the positive direction of a Y axis, and recording the coordinate of a cloud cluster matching point C as (X)2,y2) The cloud azimuth calculation formula is as follows:
Figure FDA0002621422150000021
step 5-2, according to the maximum cloud image radius pixel value r of the cloud image shooting equipment, the maximum zenith angle theta corresponding to the maximum radius and the coordinate (x) of the cloud cluster matching point C2,y2) The cloud cluster height angle calculation formula is as follows:
Figure FDA0002621422150000022
6. the estimation method according to claim 1, characterized in that: the step 6 specifically includes:
step 6-1, coolingEstablishing a shadow image coordinate system by taking the southwest angle of the shadow edge image as an origin, the east direction as the positive direction of an X axis and the north direction as the positive direction of a Y axis, and reading the coordinate of a shadow matching point S as (X, Y); and establishing an actual shadow coordinate system by taking the cloud picture shooting place T as an origin, the east direction as the positive direction of the X axis and the north direction as the positive direction of the Y axis. Let the actual shadow point R coordinate be (x)1,y1);
Step 6-2, because the coordinates (x, y) of the shadow matching point are the coordinates of the shadow edge image, the coordinates need to be converted into the coordinates under the established actual shadow coordinate system, that is, the coordinates of the actual shadow point R. According to the S coordinate (x, y) of the shadow matching point and the maximum coordinate (x) of the shadow imagemax,ymax) The southwest angular coordinate (a, b) of the ground actual shadow, the length L and the width W of the ground actual shadow image, and the R coordinate (x) of the actual shadow point1,y1) The calculation formula is as follows:
Figure FDA0002621422150000031
step 6-3, according to the R coordinate (x) of the actual shadow point1,y1) Azimuth angle of ground shadow beta1The calculation formula is as follows:
Figure FDA0002621422150000032
6-4, according to the R coordinate (x) of the actual shadow point1,y1) Distance l between ground actual shadow point R and cloud picture shooting deviceTRThe calculation formula of (a) is as follows:
Figure 2
CN202010784447.8A 2020-08-06 2020-08-06 Cloud layer height estimation method based on cloud image shadow matching algorithm Active CN112085751B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010784447.8A CN112085751B (en) 2020-08-06 2020-08-06 Cloud layer height estimation method based on cloud image shadow matching algorithm

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010784447.8A CN112085751B (en) 2020-08-06 2020-08-06 Cloud layer height estimation method based on cloud image shadow matching algorithm

Publications (2)

Publication Number Publication Date
CN112085751A true CN112085751A (en) 2020-12-15
CN112085751B CN112085751B (en) 2024-03-26

Family

ID=73735384

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010784447.8A Active CN112085751B (en) 2020-08-06 2020-08-06 Cloud layer height estimation method based on cloud image shadow matching algorithm

Country Status (1)

Country Link
CN (1) CN112085751B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115423758A (en) * 2022-08-15 2022-12-02 山东电力建设第三工程有限公司 Full-field refined DNI prediction method

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101566692A (en) * 2009-05-26 2009-10-28 吉林大学 Method for detecting cloud height by utilizing cloud shadow information in satellite remote sensing data
CN105469391A (en) * 2015-11-17 2016-04-06 中国科学院遥感与数字地球研究所 Cloud shadow detection method and cloud shadow detection system
CN105783861A (en) * 2014-12-22 2016-07-20 国家电网公司 Cloud cluster height measuring method based on dual foundation cloud atlas
CN107917880A (en) * 2017-11-06 2018-04-17 中国科学院寒区旱区环境与工程研究所 A kind of height of cloud base inversion method based on ground cloud atlas
CN111402312A (en) * 2020-03-09 2020-07-10 华北电力大学 Cloud cluster height estimation method and system using sky image

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101566692A (en) * 2009-05-26 2009-10-28 吉林大学 Method for detecting cloud height by utilizing cloud shadow information in satellite remote sensing data
CN105783861A (en) * 2014-12-22 2016-07-20 国家电网公司 Cloud cluster height measuring method based on dual foundation cloud atlas
CN105469391A (en) * 2015-11-17 2016-04-06 中国科学院遥感与数字地球研究所 Cloud shadow detection method and cloud shadow detection system
CN107917880A (en) * 2017-11-06 2018-04-17 中国科学院寒区旱区环境与工程研究所 A kind of height of cloud base inversion method based on ground cloud atlas
CN111402312A (en) * 2020-03-09 2020-07-10 华北电力大学 Cloud cluster height estimation method and system using sky image

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115423758A (en) * 2022-08-15 2022-12-02 山东电力建设第三工程有限公司 Full-field refined DNI prediction method
CN115423758B (en) * 2022-08-15 2023-07-11 山东电力建设第三工程有限公司 Full-field refined DNI prediction method

Also Published As

Publication number Publication date
CN112085751B (en) 2024-03-26

Similar Documents

Publication Publication Date Title
CN106295512B (en) Vision data base construction method and indoor orientation method in more correction lines room based on mark
US20100061593A1 (en) Extrapolation system for solar access determination
CN109446973B (en) Vehicle positioning method based on deep neural network image recognition
CN104200086A (en) Wide-baseline visible light camera pose estimation method
CN107192376B (en) Unmanned plane multiple image target positioning correction method based on interframe continuity
CN110569797B (en) Method, system and storage medium for detecting mountain fire of geostationary orbit satellite image
CN109520500A (en) One kind is based on the matched accurate positioning of terminal shooting image and streetscape library acquisition method
CN107644416A (en) A kind of real-time dynamic cloud amount inversion method based on ground cloud atlas
CN113031041B (en) Urban canyon integrated navigation and positioning method based on skyline matching
CN112857356B (en) Unmanned aerial vehicle water body environment investigation and air route generation method
CN106643670B (en) Unmanned aerial vehicle aerial photography site coordinate solving device and method
CN113947638B (en) Method for correcting orthographic image of fish-eye camera
CN115187798A (en) Multi-unmanned aerial vehicle high-precision matching positioning method
CN114372992A (en) Edge corner point detection four-eye vision algorithm based on moving platform
CN111307140A (en) Atmospheric polarized light orientation method used under cloudy weather condition
CN112085751A (en) Cloud layer height estimation method based on cloud image shadow matching algorithm
CN117115243B (en) Building group outer facade window positioning method and device based on street view picture
CN110411449B (en) Aviation reconnaissance load target positioning method and system and terminal equipment
CN116824079A (en) Three-dimensional entity model construction method and device based on full-information photogrammetry
CN114894197B (en) Underwater polarization autonomous course calculation method based on zenith real-time tracking
CN116594419A (en) Routing inspection route planning method and device, electronic equipment and storage medium
CN110887477A (en) Autonomous positioning method based on north polarization pole and polarized sun vector
CN112053402B (en) Method for obtaining course angle by using polarized geographic information database
Bailey et al. Determining large scale sandbar behaviour
JP2003141507A (en) Precise geometric correction method for landsat tm image and precise geometric correction method for satellite image

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant