CN108961150B - Photo control point method of deploying to ensure effective monitoring and control of illegal activities automatically based on unmanned plane image - Google Patents
Photo control point method of deploying to ensure effective monitoring and control of illegal activities automatically based on unmanned plane image Download PDFInfo
- Publication number
- CN108961150B CN108961150B CN201810319807.XA CN201810319807A CN108961150B CN 108961150 B CN108961150 B CN 108961150B CN 201810319807 A CN201810319807 A CN 201810319807A CN 108961150 B CN108961150 B CN 108961150B
- Authority
- CN
- China
- Prior art keywords
- image
- control point
- point
- photo
- photo control
- 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.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 230000000694 effects Effects 0.000 title claims abstract description 22
- 238000012544 monitoring process Methods 0.000 title claims abstract description 22
- 241000282693 Cercopithecidae Species 0.000 claims abstract description 4
- 238000012545 processing Methods 0.000 claims abstract description 4
- 230000009466 transformation Effects 0.000 claims description 14
- PXFBZOLANLWPMH-UHFFFAOYSA-N 16-Epiaffinine Natural products C1C(C2=CC=CC=C2N2)=C2C(=O)CC2C(=CC)CN(C)C1C2CO PXFBZOLANLWPMH-UHFFFAOYSA-N 0.000 claims description 6
- 239000000284 extract Substances 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 5
- 230000008859 change Effects 0.000 claims description 4
- 238000012937 correction Methods 0.000 claims description 4
- 238000003384 imaging method Methods 0.000 claims description 4
- 238000007689 inspection Methods 0.000 claims description 3
- 230000003044 adaptive effect Effects 0.000 claims description 2
- 230000008901 benefit Effects 0.000 claims description 2
- 230000005540 biological transmission Effects 0.000 claims description 2
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 238000000354 decomposition reaction Methods 0.000 claims description 2
- 230000002708 enhancing effect Effects 0.000 claims description 2
- 238000001914 filtration Methods 0.000 claims description 2
- 238000009499 grossing Methods 0.000 claims description 2
- 238000005070 sampling Methods 0.000 claims 1
- 238000004519 manufacturing process Methods 0.000 abstract description 3
- 238000013507 mapping Methods 0.000 description 4
- VMXUWOKSQNHOCA-UKTHLTGXSA-N ranitidine Chemical compound [O-][N+](=O)\C=C(/NC)NCCSCC1=CC=C(CN(C)C)O1 VMXUWOKSQNHOCA-UKTHLTGXSA-N 0.000 description 3
- 238000007726 management method Methods 0.000 description 2
- 238000012952 Resampling Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 235000013399 edible fruits Nutrition 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T3/00—Geometric image transformations in the plane of the image
- G06T3/14—Transformations for image registration, e.g. adjusting or mapping for alignment of images
- G06T3/147—Transformations for image registration, e.g. adjusting or mapping for alignment of images using affine transformations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20048—Transform domain processing
- G06T2207/20064—Wavelet transform [DWT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/80—Geometric correction
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
Abstract
The method of what the invention discloses a kind of based on unmanned plane image deploy to ensure effective monitoring and control of illegal activities automatically photo control point, it is characterized in that passing through Yunnan snub-nosed monkey, Image Matching is carried out on the basis of existing image processing algorithms, it determines most preferably as control region, according to navigation software, select the optimal path reached as control region, the photo control point coordinate most preferably as control region is obtained on map, it sends Measured Coordinates in map, check whether photo control point laying meets the requirements, Measured Coordinates are transferred in image simultaneously, carry out thorn point annotation automatically in image according to photo control point specification.With no paper operation may be implemented in the present invention, and photo control point checks, while reducing manpower interference, improves the cycle management of layout precision, aerial survey production efficiency and photo control point, realizes photo control point of deploying to ensure effective monitoring and control of illegal activities automatically.
Description
Technical field
The present invention relates to the distribution methods of aviation image field photo control point.
Background technique
Aerial survey of unmanned aerial vehicle as a kind of novel low altitude remote sensing image acquiring technology, maneuverability, can photography under cloud, at
The features such as this is cheap, it has also become a kind of effective quickly mapping means, in urban planning, emergency disaster relief, geographical national conditions monitoring, intelligence
Intelligent urban construction etc. plays an increasingly important role.Wherein the laying of aviation image field photo control point be carry out stereoplotting and
The important link for making orthography, is also basis and the premise of later data processing, and laying efficiency directly affects subsequent
The progress of work, while course line and net type are even more to influence final mapping and digital orthophoto map (DOM, Digital
Orthophoto Map)。
Traditional photo control point laying, which needs to import image initial p OS data, is used as reference on Google earth, in one
Industry personnel are according to density requirements of layouting, situations such as according to different scale, processing method and surveying traffic and the atural object, landforms in area,
Photo control point is laid on Google earth, the final figure of deploying to ensure effective monitoring and control of illegal activities for obtaining photo control point.In addition, due to Google earth image
It is not clear enough and update not in time etc., it can not need a surveyor on image when making reference an image mark thorn point position
The corresponding position of photo control point is found, and sketches the contours of thorn point range on image, outdoor workers can be according to thorn point range and practically
Situations such as object, landforms, the clear, feature locations that are of moderate size of selection target carried out field data acquisition.Mapping faces main at present
Difficulty is still that photo control point rests on papery working method, checks and manage inconvenience etc.；Image updates not in time, point search
It is difficult；Interior field operation is not carried out institutional operation, increases the task dispatching of measurement.Currently, aerophotogrammetric field work digitlization thorn point is to raising
Precision of layouting and reduce manpower interference, realizing route most it is excellent be still the method difficult point.
Summary of the invention
The method of what the purpose of the present invention is a kind of based on unmanned plane image deploy to ensure effective monitoring and control of illegal activities automatically photo control point, realizes with no paper work
Industry, photo control point check, while reducing manpower interference, improve the cycle management of layout precision, aerial survey production efficiency and photo control point, real
It now deploys to ensure effective monitoring and control of illegal activities automatically photo control point.
The technical scheme is that a kind of method of the photo control point of deploying to ensure effective monitoring and control of illegal activities automatically based on unmanned plane image, it is characterized in that
By Yunnan snub-nosed monkey, on the basis of existing image processing algorithms, determine most preferably as control region, according to navigation software, selection
The optimal path as control region is reached out, and the photo control point coordinate most preferably as control region is obtained on map, Measured Coordinates are sent
It into map, checks whether photo control point laying meets the requirements, while Measured Coordinates being transferred in image, according to photo control point specification
Carry out thorn point annotation automatically in image.
With no paper operation may be implemented in the present invention, and photo control point checks, while reducing manpower interference, improves precision of layouting, boat
The cycle management of production efficiency and photo control point is surveyed, realizes photo control point of deploying to ensure effective monitoring and control of illegal activities automatically.
Detailed description of the invention
Fig. 1 is photo control point method flow diagram of deploying to ensure effective monitoring and control of illegal activities automatically.
Fig. 2 is theory of wavelet transformation figure.
Fig. 3 is wavelet transformation flow chart.
Fig. 4 is Image Matching flow chart.
Fig. 5 is best as control zone concepts figure.
Specific embodiment
Technical solution of the present invention is described in detail below in conjunction with attached drawing.
The photo control point method as shown in Figure 1, the present invention deploys to ensure effective monitoring and control of illegal activities automatically the following steps are included:
1 Yunnan snub-nosed monkey
1.1 image check
Ground station by receive unmanned plane photo system be transmitted back to come image, inspection image whether meet resolution ratio
Greater than the resolution ratio (2448 × 1624) of small image, endlap region is greater than 70%, and side lap area is greater than 40%, image
Than being 1.5, whether two degree or more overlappings, image leaks bat etc., can not carry out to prevent follow-up work for pixel wide and height.
The correction of 1.2 image distortion differences
Because all there is image distortion in unmanned plane image data comprising principal point offset, symmetrical and asymmetrical distortion shape,
Therefore parameter (principal point coordinate X is examined using camera calibration as school0, Y0；Symmetrical distortion parameter K1, K2, K3；Asymmetrical distortion parameter
P1, P2), photogrammetric distortion correction is carried out to raw video.
1.3 image relative positions
Air strips image thumbnail is established using initial p OS data, image is numbered according to course line, until search
Until arrangement is correct.
2 Image Matchings
The denoising of 2.1 images
2.1.1 as shown in figure 5, automatically extracting out the resolution ratio of every image.
2.1.2 " low latitude digital aerial surveying field operation specification ", " industry specification in the digital aerial surveying of low latitude " are wanted
Photo control point is asked to lay far from 150 pixel of raw video edge.Therefore, it is closed according to 150 pixel of edge and raw video pixel ratio
System, deletes the pixel that image width edge is less than 2%-4%, and elevation edge is delete widthwise edges pixel ratio 70%
Pixel eliminates edge and layouts bring error.
2.1.3 in wavelet transformation, with the reduction of image resolution, the wavelet transformation value of white noise is gradually reduced, letter
It makes an uproar than improving, conversely, resolution ratio improves, signal-to-noise ratio is caused to reduce.As shown in Figure 2 and Figure 3, image is carried out using wavelet transformation
Wavelet decomposition three times, being divided into the high and low frequency images of different frequencies a series of, (high frequency shows image detail part, and low frequency shows
Image contour), 64 sub-blocks are divided into, wherein LL3 only accounts for 1/64, but energy has concentrated 90% or more, LH3 equally to account for 1/
64, H indicate vertical direction high pass component, are indicated with wavelet coefficient, L represents horizontal direction low-pass component, is indicated with approximation coefficient.
2.1.4 high fdrequency component detail coefficients are increased according to the method for weighting, low frequency component remains unchanged, and increases signal-to-noise ratio, finally
Image using the method reconstructed image of wavelet inverse transformation, after being denoised.
2.2 Imaging enhanced
Grayscale image is converted by chromatic image, enhancing is filtered to gray level image by Wallis filtering.By image
Gray average and variance be mapped to given gray average and variance so that the gray scale minor change information of image is increased
By force, while smoothing operator is introduced, inhibits image noise, enhances fuzzy texture pattern, principle is as follows:
In formula, g (x, y) is the gray value at raw video midpoint (x, y)；G (x, y) is that point (x, y) is filtered by Waliis
Transformed image greyscale value；mg、sgThe image greyscale mean value in certain field of a certain pixel and gray scale side respectively in image
Difference；mf、sfThe respectively target value of the target value of image mean value and image variance；C is that image contrast extends constant, value one
As be [0,1]；B is image brilliance coefficient, and value is also [0,1].
2.2.1 every image is divided into the square type region [2M+1,2N+1] not overlapped, wherein M is the picture of image width
Vegetarian refreshments, N are the pixel of image height, lesser window (being less than [10,10]) and very big window value (greater than [(M+2)/2,
(M+3)/3]) all undesirable, it is larger although also will increase the time of calculating simultaneously because lesser window can reduce error
Window will cause very big error.According to step 1.1 requirement, if M/N=1.5, M value range (10, [M/4]), N value range
For [M/1.5], the target value of image mean value takes the intermediate value of image dynamic range [0,255], i.e., 127, the target value of image variance
Take 87.([M/4], [] in [M/1.5] indicate to be rounded, and take the maximum integer no more than it)
2.2.2 the gray average and variance of each rectangular area are calculated.
It 2.2.3 is the gray value for keeping raw video, unification selection c=[0.75,1), b=[0.5,1) so that image
Gray scale minor change information is enhanced.
2.2.4 the new gray value in region is recalculated.
2.3 image Auto-matchings
Using the matched advantage of superimposed image gray value (gray value of same atural object is identical), in conjunction with geometry constraint conditions
Image space consistency characteristic point matching method, it is possible to reduce search range and improve matching efficiency.
2.3.1 adjacent using first image as reference images as shown in figure 4, according to step 1.3 to the number of image
Image is as registration image, according to image multiplication, so that every image is imitated in two and two or more image overlap areas
Fruit is significant.Overlapping region is greater than 1 multiplied by weight coefficient, and other regions are multiplied by 0, so that every image overlap area is obvious.
2.3.2 Forstner algorithm is utilized, in the square type region [2M+1,2N+1] of step 2.2.1 reference images overlapping,
1 image feature point is at least extracted, guarantee image feature point should be distributed as far as possible on the basis of being greater than 6 characteristic points in this way
Uniformly.
2.3.3 it using reference images rectangle overlapping region as benchmark window, and is mapped in registration image, as registration shadow
The search window of picture, is subject to benchmark window, utilizes correlation coefficient process Pi=| f1(x, y)-f2(x, y) |, f1(x, y) and f2(x,
Y) be respectively benchmark window image feature point and search window match point, calculate related coefficient, related coefficient is the smallest
Match point of the image feature point as search window rejects other error dots.
2.3.4 the corresponding image points of Image Matching is utilized into mean square deviation, i.e.,Wherein PiFor step
The related coefficient of matching characteristic point, deletes the characteristic point selected less than mean square deviation again in rapid 2.3.3, improves matching precision.
2.3.5 affine transformation formula is utilized, the image feature point for deleting choosing is brought into, 6 affine transformation parameters are obtained, is established
Image geometry relationship.
Wherein (x, y) is the point coordinate of benchmark image, and (X, Y) is the point coordinate for matching image.
2.3.6 using 6 parameters of affine transformation calculated, by matching image rectification to reference images.
2.3.7 2.3.1-2.3.6 step is recycled, until Image Matching terminates.
3 automatic pictures of laying control region
It is automatic raw according to high-precision POS data, unmanned plane during flying course line and baseline separation parameter, mapping scale requirement
Imaging control region.
3.1 obtain image picture control region
3.1.1, grid is set, is x-axis along course-and-bearing, is y-axis perpendicular to course-and-bearing, most important of which is that knot
Image overlap area is divided into 30 × 30 grid to effectively obtain overlay information by the selection of structure window size, can make precision
Reach 1/10 Pixel-level.
3.1.2 select l × r (l/r > 1, r > 30) a image grid in left image overlap area, at the same estimate it
Right image grid.
3.1.3 two images are formed simultaneously 30 × 30 field of search, while according to P=∑ [f1(x, y)-f2(x, y)], f1
(x, y) and f2(x, y) is respectively the corresponding gray value of left and right image, calculates field of search related coefficient, right when P is less than threshold
The gray value answered is suitble to laying as control region, deletes gray value (the determining majority of threshold that image related coefficient is greater than threshold
Principle method determines that image maximum related coefficient subtracts the smallest related coefficient multiplied by 0.75, adds the smallest phase relation
Number does not make image fault while guaranteeing to delete in this way).
3.1.4 the image picture control region laid automatically is obtained.
3.2 extract most preferably as control region
3.2.1 " low latitude digital aerial surveying field operation specification " requires photo control point to be greater than 5cm apart from course line, therefore, choosing
It lays as control region the position for selecting the image pixel point greater than image course line 3%-5%.
3.2.2 according to multinomial least squares collocation ∑ vv=min, wherein v is indicated as the control matched difference of area grayscale
Value, the picture control region of resampling steps 3.1, obtains coherence factor, selects related coefficient minimum, determine that it is first best picture
Control region.
3.2.3 according to determining best picture control region, to parallel (course is vertical), photo second is determined most using with side
Good picture control region.
3.2.4 3.2.1-3.2.3 is circuited sequentially, determines image all most preferably as control region.
4 extract photo control point
The conversion of 4.1 coordinate systems
Image itself has WGS-84 coordinate, while can call Google earth map, Amap, Baidu
A variety of Map Services such as figure, local area map.It is converted using coordinate system, the image after matching is mapped to the map of calling
In, it realizes the combination of image and map, image coordinate data is avoided to cause confusion in reading and transmission.
4.2 selectively figure photo control points
It 4.2.1 will be most preferably as on control region projection to satellite map.
4.2.2 software optimal path is utilized, surveyor is selected and reaches the best route as control region.
4.2.3 by the fast roaming of map, scaling and adaptive display function, in satellite map regional choice image
Point is controlled, is usually bordering on right angle and the again intersection point of subhorizontal linear ground object and atural object turning, such as road junction, solely
With founding shrub, blocky blank, enclosure wall or the corner point of platform etc., and the photo control point for being conducive to surveyor's arrival is optimal.
4.2.4 it pierces a little, and makes marks in satellite map, and the WGS-84 approximate coordinate of the thorn point target is saved.
4.3 extract image photo control point
4.3.1 RTK is real-time transmitted in image in the data measured, by formula δ=| f1(x, y)-f2(x, y) |,
Wherein f1(x, y) is actual measurement photo control point coordinate, f2(x, y) is map photo control point approximate coordinate, carries out coordinate with map thorn point data
Difference calculates, and real-time perfoming photo control point coordinate checks, so as to meet limit poor for map photo control point coordinate and actual measurement photo control point coordinate, wherein
The precision for the target image coordinate that Satellite Map GIS Software provides can reach 3-5m, fully meet the precision of dot position, use this
Method can cause the not corresponding of the photo control point target selected on the spot and aerial stereo images target to avoid the generation of thorn wrong position.
4.3.2 according to the position of Measured Coordinates automatic Calibration photo control point on image, and relevant parameter information is set.
4.3.3 the photo control point of satellite map calibration and area map are achieved, using as three encryption of interior industry sky check and
Check.
Above embodiments are only that preferred embodiments of the present invention will be described, are not limited the scope of the present invention
Fixed, without departing from the spirit of the design of the present invention, those of ordinary skill in the art make technical solution of the present invention
Various changes and improvements should all be fallen into the protection scope that claims of the present invention determines.
Claims (10)
1. a kind of method of the photo control point of deploying to ensure effective monitoring and control of illegal activities automatically based on unmanned plane image, it is characterized in that by Yunnan snub-nosed monkey, existing
Image Matching is carried out on the basis of image processing algorithms, obtains image picture control region, is extracted most preferably as control region, it is soft according to navigating
Part selects the optimal path reached most preferably as control region, and the photo control point coordinate most preferably as control region is obtained on map, will be real
It surveys coordinate to be sent in map, checks whether photo control point laying meets the requirements, while Measured Coordinates being transferred in image, according to
Photo control point specification carries out thorn point annotation automatically in image；
Wherein obtain image picture control region the following steps are included:
1.1.1, grid is set, is x-axis along course-and-bearing, is y-axis perpendicular to course-and-bearing, image overlap area is divided into 30
× 30 grid can make precision reach 1/10 Pixel-level；
1.1.2 L × r image grid is selected in left image overlap area, while estimates its image lattice at right
Net, wherein L/r > 1, r > 30；
1.1.3 two images are formed simultaneously 30 × 30 field of search, while according to P=∑ [f1(x,y)- f2(x, y)], f1
(x, y) and f2(x, y) is respectively the corresponding gray value of left and right image, calculates field of search related coefficient, right when P is less than threshold
The gray value answered is suitble to lay as control region, deletes the gray value that image related coefficient is greater than threshold；
1.1.4 the image picture control region laid automatically is obtained；
Wherein obtain most preferably as control region the following steps are included:
1.2.1 the position of image pixel point of the selection greater than image course line 3%-5% is laid as control region；
1.2.2 according to multinomial least squares collocation ∑ vv=min, wherein v is indicated as the control matched difference of area grayscale, weight
The picture of sampling step 1.1.4 controls region, obtains related coefficient, selects related coefficient minimum, determines that it is first best picture control
Region；
1.2.3 according to determining best as control region, to parallel or vertical with course, photo second is determined most using with side
Good picture control region；
1.2.4 1.2.1-1.2.3 is circuited sequentially, determines image all most preferably as control region.
2. the method for the photo control point of deploying to ensure effective monitoring and control of illegal activities automatically based on unmanned plane image as described in claim 1, characterized in that image is located in advance
Reason the following steps are included:
1.1 image check
Ground station is transmitted back to the image come by receiving unmanned plane photo system, and whether inspection image meets resolution ratio and is greater than
2448 × 1624 resolution ratio, endlap region be greater than 70%, side lap area be greater than 40%, image pixel width and
Than being 1.5, whether two degree or more overlappings, image leaks bat, can not carry out to prevent follow-up work height；
The correction of 1.2 image distortion differences
Photogrammetric distortion correction is carried out to raw video using camera calibration as school inspection parameter；
1.3 image relative positions
Air strips image thumbnail is established using initial p OS data, image is numbered according to course line, until search arrangement
Until correct.
3. the method for the photo control point of deploying to ensure effective monitoring and control of illegal activities automatically based on unmanned plane image as claimed in claim 2, characterized in that Image Matching
The following steps are included:
The denoising of 2.1 images；
2.2 Imaging enhanced；
Grayscale image is converted by chromatic image, enhancing is filtered to gray level image by Wallis filtering, by the ash of image
Degree mean value and variance are mapped to given gray average and variance, so that the gray scale minor change information of image is enhanced, together
When introduce smoothing operator, inhibit image noise, fuzzy texture pattern is enhanced；
2.3 image Auto-matchings
Using the matched advantage of superimposed image gray value, in conjunction with the characteristic point of the image space consistency of geometry constraint conditions
Method of completing the square, it is possible to reduce search range and raising matching efficiency.
4. the method for the photo control point of deploying to ensure effective monitoring and control of illegal activities automatically based on unmanned plane image as described in claim 1, characterized in that on map
Obtain most preferably as control region photo control point coordinate the following steps are included:
The conversion of 4.1 coordinate systems
It is converted using coordinate system, the image after matching is mapped in the map of calling, realized the combination of image and map, avoid
Image coordinate data cause confusion in reading and transmission；
4.2 selectively figure photo control points；
4.3 extract image photo control point.
5. the method for the photo control point of deploying to ensure effective monitoring and control of illegal activities automatically based on unmanned plane image as claimed in claim 3, characterized in that step 2.1
Image denoising the following steps are included:
2.1.1 the resolution ratio of every image is automatically extracted out；
2.1.2 according to 150 pixel of edge and raw video pixel ratio relationship, the picture that image width edge is less than 2%-4% is deleted
Vegetarian refreshments, elevation edge are 70% pixel for deleting widthwise edges pixel ratio, eliminate edge and layout bring error；
2.1.3 image is subjected to wavelet decomposition three times using wavelet transformation, is divided into a series of high and low frequency shadow of different frequencies
Picture, high frequency show image detail part, and low frequency shows image contour, is divided into 64 sub-blocks, and wherein LL3 only accounts for 1/64, but
Energy, which has concentrated 90% or more, LH3 equally to account for 1/64, H, indicates vertical direction high pass component, is indicated with wavelet coefficient, L is represented
Horizontal direction low-pass component, is indicated with approximation coefficient；
2.1.4 high fdrequency component detail coefficients are increased according to the method for weighting, low frequency component remains unchanged, and increases signal-to-noise ratio, finally utilizes
The method reconstructed image of wavelet inverse transformation, the image after being denoised.
6. the method for the photo control point of deploying to ensure effective monitoring and control of illegal activities automatically based on unmanned plane image as claimed in claim 3, characterized in that step 2.2
Imaging enhanced the following steps are included:
2.2.1 every image is divided into the square type region [2M+1,2N+1] not overlapped, wherein being less than the window of [10,10]
It is all undesirable with the window value greater than [(m+2)/2, (m+3)/3]；M value range (10, [m/4]), N value range be (10,
[1.5n/6]), the target value of image mean value takes the intermediate value of image dynamic range [0,255], i.e., 127, the target value of image variance
Take 87；If m/n=1.5, m are image width, n is image height；
2.2.2 the gray average and variance of each rectangular area are calculated；
It 2.2.3 is the gray value for keeping raw video, unified selection c=[0.75,1), b=[0.5,1) so that the gray scale of image
Minor change information is enhanced；Wherein c is that image contrast extends constant, and b is image brilliance coefficient；
2.2.4 the new gray value in region is recalculated.
7. the method for the photo control point of deploying to ensure effective monitoring and control of illegal activities automatically based on unmanned plane image as claimed in claim 6, characterized in that step 2.3
Image Auto-matching the following steps are included:
2.3.1 according to step 1.3 to the number of image, using first image as reference images, adjacent image is as registration shadow
Picture, according to image multiplication, so that image overlap area significant effect of the every image at two and two or more, overlapping region
It is greater than 1 multiplied by weight coefficient, other regions are multiplied by 0, so that every image overlap area is obvious；
2.3.2 Forstner algorithm is utilized, in the square type region [2M+1,2N+1] of reference images overlapping, at least extracts 1
Image feature point, in this way guarantee image feature point should be evenly distributed as far as possible on the basis of being greater than 6 characteristic points；
2.3.3 it using reference images rectangle overlapping region as benchmark window, and is mapped in registration image, as registration image
Search window, be subject to benchmark window, utilize correlation coefficient processP i =|f3(x,y)- f4(x, y) |, f3(x, y) and f4(x,y)
The respectively match point of the image feature point and search window of benchmark window calculates related coefficient, by the smallest shadow of related coefficient
Match point as characteristic point as search window rejects other error dots；
2.3.4 the corresponding image points of Image Matching is utilized into mean square deviation, i.e. σ=((∑n i=1(P i )2)/n)1/2, whereinP i For step
2.3.3 the related coefficient of middle matching characteristic point deletes the characteristic point selected less than mean square deviation again, improves matching precision；
2.3.5 affine transformation formula is utilized, the image feature point for deleting choosing is brought into, 6 affine transformation parameters is obtained, establishes shadow
As geometrical relationship；
x=a0+a1X+a2Y
y=b0+b1X+b2Y
Wherein (x, y) is the point coordinate of benchmark image, and (X, Y) is the point coordinate for matching image；
2.3.6 using 6 parameters of affine transformation calculated, by matching image rectification to reference images；
2.3.7 2.3.1-2.3.6 step is recycled, until Image Matching terminates.
8. the method for the photo control point of deploying to ensure effective monitoring and control of illegal activities automatically based on unmanned plane image as described in claim 1, characterized in that step
1.1.3 the determination of threshold is determined with majority principle method, the maximum related coefficient of image subtract the smallest related coefficient multiplied by
0.75, the smallest related coefficient is added, while guaranteeing to delete in this way, does not make image fault.
9. the method for the photo control point of deploying to ensure effective monitoring and control of illegal activities automatically based on unmanned plane image as claimed in claim 4, characterized in that step 4.2
Selectively figure photo control point the following steps are included:
It 4.2.1 will be most preferably as on control region projection to satellite map；
4.2.2 software optimal path is utilized, surveyor is selected and reaches the best route as control region；
4.2.3 pass through the fast roaming of map, scaling and adaptive display function, in satellite map regional choice image control
Point；
4.2.4 it pierces a little, and makes marks in satellite map, and the WGS-84 approximate coordinate of the thorn point target is saved.
10. the method for the photo control point of deploying to ensure effective monitoring and control of illegal activities automatically based on unmanned plane image as claimed in claim 4, characterized in that step 4.3
Extract image photo control point the following steps are included:
4.3.1 RTK is real-time transmitted in image in tested point data measured, by formula δ=| f5(x,y)-f6(x, y) |,
Wherein f5(x, y) is actual measurement photo control point coordinate, f6(x, y) is map photo control point approximate coordinate, carries out coordinate with map thorn point data
Difference calculates, and real-time perfoming photo control point coordinate checks, so as to meet limit poor for map photo control point coordinate and actual measurement photo control point coordinate, wherein
The precision for the target image coordinate that Satellite Map GIS Software provides can reach 3-5m, fully meet the precision of dot position, use this
Method can cause the not corresponding of the photo control point target selected on the spot and aerial stereo images target to avoid the generation of thorn wrong position；
4.3.2 according to the position of Measured Coordinates automatic Calibration photo control point on image, and relevant parameter information is set；
4.3.3 the photo control point of satellite map calibration and area map are achieved, using as three encryption of interior industry sky check and it is multiple
It looks into.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810319807.XA CN108961150B (en) | 2018-04-11 | 2018-04-11 | Photo control point method of deploying to ensure effective monitoring and control of illegal activities automatically based on unmanned plane image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810319807.XA CN108961150B (en) | 2018-04-11 | 2018-04-11 | Photo control point method of deploying to ensure effective monitoring and control of illegal activities automatically based on unmanned plane image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108961150A CN108961150A (en) | 2018-12-07 |
| CN108961150B true CN108961150B (en) | 2019-05-03 |
Family
ID=64498724
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810319807.XA Expired - Fee Related CN108961150B (en) | 2018-04-11 | 2018-04-11 | Photo control point method of deploying to ensure effective monitoring and control of illegal activities automatically based on unmanned plane image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108961150B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110542424B (en) * | 2019-09-03 | 2022-07-08 | 江苏艾佳家居用品有限公司 | Automatic navigation method and system for household space area |
| CN110487253A (en) * | 2019-09-18 | 2019-11-22 | 机械工业勘察设计研究院有限公司 | One kind being based on multi-rotor unmanned aerial vehicle high-precision real estate measurement method |
| CN111426302B (en) * | 2020-04-14 | 2022-03-25 | 西安航空职业技术学院 | Unmanned aerial vehicle high accuracy oblique photography measurement system |
| CN112750135B (en) * | 2020-12-31 | 2022-06-03 | 成都信息工程大学 | Unmanned aerial vehicle oblique photography measurement image control point optimization method and system |
| CN114399541B (en) * | 2021-12-29 | 2022-10-21 | 北京师范大学 | Regional coordinate conversion method and device |
| CN116164711B (en) * | 2023-03-09 | 2024-03-29 | 广东精益空间信息技术股份有限公司 | Unmanned aerial vehicle mapping method, unmanned aerial vehicle mapping system, unmanned aerial vehicle mapping medium and unmanned aerial vehicle mapping computer |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105865427A (en) * | 2016-05-18 | 2016-08-17 | 三峡大学 | Individual geological disaster emergency investigation method based on remote sensing of small unmanned aerial vehicle |
| CN106651848A (en) * | 2016-12-22 | 2017-05-10 | 上海华测导航技术股份有限公司 | Site leveling method and system |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20120114229A1 (en) * | 2010-01-21 | 2012-05-10 | Guoqing Zhou | Orthorectification and mosaic of video flow |
| GB201008104D0 (en) * | 2010-05-14 | 2010-06-30 | Selex Galileo Ltd | System and method for image registration |
| US9592912B1 (en) * | 2016-03-08 | 2017-03-14 | Unmanned Innovation, Inc. | Ground control point assignment and determination system |
| CN106960174B (en) * | 2017-02-06 | 2021-06-08 | 中国测绘科学研究院 | Height control point extraction and auxiliary positioning method for high resolution image laser radar |
| CN107270877B (en) * | 2017-06-22 | 2019-06-07 | 中铁大桥勘测设计院集团有限公司 | A kind of band-like survey area low altitude photogrammetry photo control point method of layout survey |
-
2018
- 2018-04-11 CN CN201810319807.XA patent/CN108961150B/en not_active Expired - Fee Related
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105865427A (en) * | 2016-05-18 | 2016-08-17 | 三峡大学 | Individual geological disaster emergency investigation method based on remote sensing of small unmanned aerial vehicle |
| CN106651848A (en) * | 2016-12-22 | 2017-05-10 | 上海华测导航技术股份有限公司 | Site leveling method and system |
Non-Patent Citations (8)
| Title |
|---|
| GPS手持机+Google Earth联合进行像控点选刺的探索与应用;曾庆伟等;《铁道勘察》;20091231(第4期);第57-58页 * |
| The Development of an UAV Borne Direct Georeferenced Photogrammetric Platform for Ground Control Point Free Applications;Kai-Wei Chiang等;《Sensors》;20120704;全文 * |
| Wallis滤波在影像匹配中的应用;张力等;《武汉测绘科技大学学报》;19990331;第24卷(第1期);第24-27页 * |
| 基于困难地区的无人机影像像控点布设研究;胡荣明等;《测绘与空间地理信息》;20170430;第40卷(第4期);全文 * |
| 无人机低空遥感数字影像自动拼接与快速定位技术研究;王利勇;《中国优秀硕士学位论文全文数据库 信息科技辑》;20120715(第7期);第I140-1153页 * |
| 无人机影像像控点自动布设方案研究;董平;《价值工程》;20171231;第183-185页 * |
| 海岛(礁)无人机影像快速几何处理技术研究;孙磊;《中国优秀硕士学位论文全文数据库 基础科学辑》;20160715(第7期);第A008-42页 * |
| 董平.无人机影像像控点自动布设方案研究.《价值工程》.2017,第183-185页. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108961150A (en) | 2018-12-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108961150B (en) | Photo control point method of deploying to ensure effective monitoring and control of illegal activities automatically based on unmanned plane image | |
| JP6674822B2 (en) | Photographing method of point cloud data generation image and point cloud data generation method using the image | |
| US20140362082A1 (en) | Automated Overpass Extraction from Aerial Imagery | |
| CN104123730A (en) | Method and system for remote-sensing image and laser point cloud registration based on road features | |
| CN106485751B (en) | Unmanned aerial vehicle photographic imaging and data processing method and system applied to foundation pile detection | |
| CN108548525A (en) | A method of carrying out field mapping using unmanned plane aeroplane photography | |
| KR20190051703A (en) | Stereo drone and method and system for calculating earth volume in non-control points using the same | |
| US20120275698A1 (en) | Method of Orthoimage Color Correction Using Multiple Aerial Images | |
| Hanaizumi et al. | An automated method for registration of satellite remote sensing images | |
| JP4825836B2 (en) | Road map data creation system | |
| JP6347061B2 (en) | Measurement result consistency determination method and measurement result consistency determination apparatus | |
| CN103700063B (en) | Topography integration quick mapping method based on high definition satellite image | |
| CN114926739A (en) | Unmanned collaborative acquisition and processing method for underwater and overwater geographic spatial information of inland waterway | |
| CN106969753B (en) | Unmanned plane data processing method based on Electric Design application | |
| JP6146731B2 (en) | Coordinate correction apparatus, coordinate correction program, and coordinate correction method | |
| JP4984261B2 (en) | Target detection system | |
| JP3380457B2 (en) | Method for removing shadow component on geographic image, geographic image processing apparatus, and recording medium | |
| CN106408964B (en) | The acquisition methods of the preferential accurate lane grade control area of air-ground coordination bus signals | |
| Lehrbass et al. | Urban tree cover mapping with relief-corrected aerial imagery and LiDAR | |
| Zhang et al. | Matching of Ikonos stereo and multitemporal GEO images for DSM generation | |
| CN114882169A (en) | Three-dimensional data-based power grid engineering big data intelligent analysis system and method | |
| CN110021041B (en) | Unmanned scene incremental gridding structure reconstruction method based on binocular camera | |
| CN107843240B (en) | Method for rapidly extracting same-name point information of unmanned aerial vehicle image in coastal zone | |
| Angelats et al. | High resolution orthophotos and a digital surface model of the Roman city of Pollentia (Mallorca, Spain) using RPAS imagery, aerial images, and open data archives | |
| Hughes et al. | Investigation of joint visibility between SAR and optical images of urban environments |
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 | ||
| CF01 | Termination of patent right due to non-payment of annual fee | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20190503 Termination date: 20200411 |