CN114240758A - Mountain tea garden low-altitude image splicing method taking quadrilateral plots as reference objects - Google Patents
Mountain tea garden low-altitude image splicing method taking quadrilateral plots as reference objects Download PDFInfo
- Publication number
- CN114240758A CN114240758A CN202111597281.XA CN202111597281A CN114240758A CN 114240758 A CN114240758 A CN 114240758A CN 202111597281 A CN202111597281 A CN 202111597281A CN 114240758 A CN114240758 A CN 114240758A
- Authority
- CN
- China
- Prior art keywords
- image
- tea garden
- splicing
- images
- reference object
- 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
Links
- 238000000034 method Methods 0.000 title claims abstract description 42
- 240000001238 Gaultheria procumbens Species 0.000 title claims abstract description 33
- 235000007297 Gaultheria procumbens Nutrition 0.000 title claims abstract description 33
- 235000010394 Solidago odora Nutrition 0.000 title claims abstract description 33
- 230000011218 segmentation Effects 0.000 claims abstract description 20
- 241001122767 Theaceae Species 0.000 claims description 23
- 238000012545 processing Methods 0.000 claims description 17
- 238000010586 diagram Methods 0.000 claims description 9
- 238000007781 pre-processing Methods 0.000 claims description 8
- 230000000007 visual effect Effects 0.000 claims description 6
- 238000003708 edge detection Methods 0.000 claims description 3
- 230000009467 reduction Effects 0.000 claims description 3
- 230000008569 process Effects 0.000 abstract description 4
- 239000000284 extract Substances 0.000 abstract description 3
- 244000269722 Thea sinensis Species 0.000 abstract 3
- 238000000605 extraction Methods 0.000 abstract 1
- 230000006872 improvement Effects 0.000 description 7
- 230000009286 beneficial effect Effects 0.000 description 4
- 238000007689 inspection Methods 0.000 description 3
- 235000009024 Ceanothus sanguineus Nutrition 0.000 description 2
- 241000282414 Homo sapiens Species 0.000 description 2
- 240000003553 Leptospermum scoparium Species 0.000 description 2
- 235000015459 Lycium barbarum Nutrition 0.000 description 2
- 244000299461 Theobroma cacao Species 0.000 description 1
- 235000009470 Theobroma cacao Nutrition 0.000 description 1
- 235000013361 beverage Nutrition 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000018109 developmental process Effects 0.000 description 1
- 230000035622 drinking Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 235000020567 organic tea Nutrition 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
- 230000007704 transition Effects 0.000 description 1
Images
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
- G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
-
- 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
- G06T3/40—Scaling of whole images or parts thereof, e.g. expanding or contracting
- G06T3/4038—Image mosaicing, e.g. composing plane images from plane sub-images
-
- 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
- G06T3/60—Rotation of whole images or parts thereof
-
- 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
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a splicing method of low-altitude images of a mountainous area tea garden by taking a quadrangular land block as a reference object, which comprises the following steps: collecting low-altitude images of the tea garden in the mountainous area, carrying out threshold segmentation on the collected images, extracting coordinates of each corner point of a reference object in a binary image, calculating the scaling and the rotation angle of the images, and splicing the low-altitude images of the tea garden; the method takes the starting point of the mountain tea garden operation as a reference object to splice the low-altitude images, carries out binarization segmentation on the images in the splicing process, carries out corner point coordinate extraction on the operation starting point, calculates the scaling ratio and the rotation angle of the secondary image, extracts the region uncovered by the main image in the secondary image and completes splicing with the main image, avoids errors caused by selection errors of feature points, and solves the problem of insufficient image coverage in the traditional mountain tea garden image acquisition by taking the mountain tea garden operation starting point as the starting point of the obtained spliced image, thereby ensuring the relatively large image coverage while maintaining the image quality.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a method for splicing low-altitude images of a mountainous tea garden by taking a quadrangular land as a reference object.
Background
Tea originates from China, has been cultivated for more than three thousand years in history, is one of three major beverages of human beings, has more consumption than coffee and cocoa, is an important economic crop in China, has profound influence on economic happiness of tea areas, market supply and establishment of famous tea brands due to development conditions, is beneficial to human health by drinking tea, is mainly planted in tea gardens, and mountain tea gardens are mountain areas for planting tea leaves, most of the tea gardens in China are located in the mountain areas, especially organic tea gardens, and in order to improve the quality of the tea leaves in the mountain tea gardens, effective sensing and daily inspection on environmental parameters of the tea gardens are needed so as to timely take effective measures to realize efficient production management.
The daily inspection work of mountain tea garden leads to work load because of mountain topography characteristics big, and work efficiency is low, and the introduction many rotors unmanned vehicles technique of carrying out the daily inspection of tea garden becomes the effective way that improves labor efficiency, consequently all builds the quadrangle plot that is used for unmanned vehicles to berth on the mountain tea garden, with regard as unmanned vehicles parking apron, however, in unmanned vehicles actual image acquisition process, in order to keep image quality, the coverage of unmanned vehicles collection image is less relatively, the problem that a plurality of continuous hills can't appear in an image often appears, thereby lead to the image coverage of gathering not enough.
At present, in order to display more regional information in one image, the images need to be spliced, feature points are mostly selected through an image processing algorithm in the existing image splicing research, at this time, matching between different tea tree rows is probably generated due to the similarity of tea tree row information in a tea garden in a mountainous area, and under the existing splicing method, the splicing target mainly highlights the smoothness of the images and the transition nature of pictures, which is obviously not beneficial to the positioning of a subsequent agricultural operation region, and the problem can be solved by taking an apron of a quadrangular land as a reference object.
Disclosure of Invention
In view of the above problems, the present invention provides a method for splicing low-altitude images of a mountain tea garden with a quadrangular land block as a reference, which splices low-altitude images with a starting point of mountain tea garden operation as a reference from the perspective of agricultural operation, performs binary segmentation on the images during the splicing process, extracts corner coordinates of the starting point of the operation, calculates a scaling ratio and a rotation angle of a secondary image, extracts an area uncovered by a main image in the secondary image and splices the area with the main image, and avoids errors caused by selection errors of feature points in the traditional image splicing method.
In order to achieve the purpose of the invention, the invention is realized by the following technical scheme: a method for splicing low-altitude images of a mountainous area tea garden by taking a quadrangular plot as a reference object comprises the following steps:
the method comprises the following steps: firstly, presetting a patrol area of a mountain tea garden on an unmanned aerial vehicle, then starting the unmanned aerial vehicle to carry out daily flight patrol on the mountain tea garden, carrying out low-altitude image acquisition on the mountain tea garden in the patrol area, and then preprocessing the acquired images to obtain original images to be spliced, wherein the original images contain quadrangular plots serving as parking aprons of the unmanned aerial vehicle;
step two: dividing an original image to be spliced into a main image and a secondary image according to the length of a road in the image, then taking the brightness characteristics of the main image and the secondary image and carrying out threshold segmentation to obtain binary images of the main image and the secondary image, and determining a white area of a quadrilateral land block in the binary images of the main image and the secondary image as a quadrilateral reference object;
step three: firstly, determining pixel point coordinates of four corner points of a quadrilateral reference object in a binary image of a main image and a secondary image in an original image, and then sequentially extracting four corner points A of the quadrilateral reference object on the main image according to the determined pixel point coordinates1、B1、C1And D1And four corner points A of the quadrilateral reference object on the sub-graph2、B2、C2And D2；
Step four: firstly, the diagonal A of the quadrilateral reference object on the main graph is respectively obtained according to the four corners on the main graph and the auxiliary graph1C1And B1D1And the diagonal A of the quadrangular reference object on the sub-diagram2C2And B2D2According to the diagonal line A1C1、B1D1、A2C2And B2D2The scaling of the sub-map is calculated, the sub-map is then scaled according to the calculated scaling, and then according to the diagonal A1C1And A2C2And B1D1：B2D2Calculating the rotation angle of the secondary image at an included angle in the original image coordinate system;
step five: firstly, selecting an area which is not covered by the main graph in the auxiliary graph as a part of the auxiliary graph participating in splicing, then rotating the part of the auxiliary graph participating in splicing according to the rotating angle in the fourth step, and splicing with the main graph to finish the splicing of the low-altitude images of the mountainous area tea garden.
The further improvement lies in that: in the first step, the image preprocessing specifically comprises the following steps: firstly, carrying out edge detection processing on the image, then carrying out equalization processing on the image, then carrying out noise reduction processing on the image after the equalization processing, and finally obtaining a low-altitude original image of the mountainous area tea garden after pretreatment.
The further improvement lies in that: in the second step, the specific step of threshold segmentation is to take the R + G + B brightness feature of the original image to be spliced as the segmentation feature, determine the threshold by using the OSTU method, and then segment the original image to be spliced into a binary image by using threshold segmentation.
The further improvement lies in that: in the third step, when extracting the coordinates of the corner points of the quadrilateral reference object on the main graph and the auxiliary graph, the upper left corner of the four corner points of the quadrilateral reference object of the main graph is used as A1Sequentially extracting coordinates A clockwise1、B1、C1And D1The four corner points of the quadrilateral reference object on the secondary graph take the upper left corner as A2Sequentially extracting coordinates A clockwise2、B2、C2And D2。
The further improvement lies in that: in the fourth step, when the scaling of the sub-image is calculated, the diagonal A is calculated respectively1C1、B1D1、A2C2And B2D2Length of (1), and then A1C1：A2C2And B1D1：B2D2The average of (d) is used as a scaling of the sub-graph.
The further improvement lies in that: in the fourth step, when the rotation angle of the secondary graph is calculated, A is used firstly1C1And A2C2The angle in the image coordinate system is theta1Then with B1D1：B2D2The angle in the image coordinate system is theta2Then at theta1And theta2The average value of (a) is used as the rotation angle of the sub-map.
The further improvement lies in that: in the fifth step, if the sub-image view is on the left, the rotated image from the leftmost to A is selected1The point area is used as the part of the sub-image participating in the splicing, if the sub-image visual field is right, the rightmost side of the rotated image is selected to be C1The region of points serves as a part of the sub-map participating in the stitching.
The invention has the beneficial effects that: according to the method, from the perspective of agricultural operation, the starting point of mountain tea garden operation is taken as a reference object to splice the low-altitude images, the images are subjected to binarization segmentation in the splicing process, the angular point coordinates of the operation starting point are extracted, the scaling ratio and the rotation angle of the secondary image are calculated, the region which is not covered by the main image in the secondary image is extracted and spliced with the main image, errors caused by selection errors of feature points are avoided, the obtained spliced image takes the mountain tea garden operation starting point as the starting point, the problem of insufficient image coverage in traditional mountain tea garden image acquisition is solved, the image coverage is relatively large while the image quality is maintained, and compared with a traditional splicing method, the spliced image is more beneficial to determination of subsequent tea garden operation regions.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic flow chart of a method according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram showing a comparison between the main diagram and the sub diagram in the second embodiment of the present invention;
FIG. 3 is a schematic diagram of a binarized image according to a second embodiment of the present invention;
FIG. 4 is a diagram illustrating a main graph and a sub graph after being merged according to a second embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example one
Referring to fig. 1, fig. 2, fig. 3, and fig. 4, the present embodiment provides a method for splicing low-altitude images of a mountainous area tea garden by using a quadrangular plot as a reference, including the following steps:
the method comprises the following steps: the method comprises the following steps of firstly presetting a patrol area of a mountain tea garden on an unmanned aerial vehicle, then starting the unmanned aerial vehicle to carry out daily patrol on the mountain tea garden, carrying out low-altitude image acquisition on the mountain tea garden in the patrol area, then preprocessing the acquired image to obtain an original image to be spliced, wherein the original image contains a quadrilateral land block serving as an unmanned aerial vehicle parking apron, and the preprocessing specific steps of the image are as follows: firstly, carrying out edge detection processing on the image, then carrying out equalization processing on the image, then carrying out noise reduction processing on the image after the equalization processing, and finally obtaining a low-altitude original image of the mountain tea garden after pretreatment;
step two: dividing an original image to be spliced into a main image and a secondary image according to the length of a road in the image, then adopting the brightness characteristics of the main image and the secondary image and carrying out threshold segmentation to obtain a binary image of the main image and the secondary image, determining a white area of a quadrilateral land block in the binary image of the main image and the secondary image as a quadrilateral reference object, and carrying out threshold segmentation by using the R + G + B brightness characteristics of the original image to be spliced as segmentation characteristics, then using an OSTU method to determine a threshold, and then adopting threshold segmentation to segment the original image to be spliced into a binary image;
step three: firstly, determining pixel point coordinates of four corner points of a quadrilateral reference object in a binary image of a main image and a secondary image in an original image, and then sequentially extracting four corner points A of the quadrilateral reference object on the main image according to the determined pixel point coordinates1、B1、C1And D1And four corner points A of the quadrilateral reference object on the sub-graph2、B2、C2And D2When coordinates of corner points of the quadrilateral reference object on the main graph and the secondary graph are extracted, the upper left corner of the four corner points of the quadrilateral reference object of the main graph is used as A1Sequentially extracting coordinates A clockwise1、B1、C1And D1The four corner points of the quadrilateral reference object on the secondary graph take the upper left corner as A2Sequentially extracting coordinates A clockwise2、B2、C2And D2；
Step four: firstly, the diagonal A of the quadrilateral reference object on the main graph is respectively obtained according to the four corners on the main graph and the auxiliary graph1C1And B1D1And the diagonal A of the quadrangular reference object on the sub-diagram2C2And B2D2According to the diagonal line A1C1、B1D1、A2C2And B2D2The scaling of the sub-map is calculated, the sub-map is then scaled according to the calculated scaling, and then according to the diagonal A1C1And A2C2And B1D1：B2D2In the original image coordinate systemThe angle is used to calculate the rotation angle of the sub-image, and when calculating the scaling of the sub-image, the diagonal A is calculated1C1、B1D1、A2C2And B2D2Length of (1), and then A1C1：A2C2And B1D1：B2D2The average value of (A) is used as the scaling ratio of the sub-map, and when the rotation angle of the sub-map is calculated, A is used first1C1And A2C2The angle in the image coordinate system is theta1Then with B1D1：B2D2The angle in the image coordinate system is theta2Then at theta1And theta2The average value of (a) is used as the rotation angle of the sub-map;
step five: firstly, selecting an area which is not covered by the main image in the auxiliary image as a part of the auxiliary image participating in splicing, and if the visual field of the auxiliary image is on the left, selecting the rotated image from the leftmost side to A1The point area is used as the part of the sub-image participating in the splicing, if the sub-image visual field is right, the rightmost side of the rotated image is selected to be C1And taking the region of the point as a part of the auxiliary image participating in splicing, rotating the part of the auxiliary image participating in splicing according to the rotating angle in the fourth step, and splicing with the main image to finish the splicing of the low-altitude image of the mountainous area tea garden.
Example two
Referring to fig. 1, fig. 2, fig. 3, and fig. 4, the present embodiment provides a method for splicing low-altitude images of a mountainous area tea garden by using a quadrangular plot as a reference, including the following steps:
the method comprises the following steps: the method comprises the following steps of firstly presetting a patrol area of a mountain tea garden on an unmanned aerial vehicle, then starting the unmanned aerial vehicle to carry out daily patrol on the mountain tea garden, carrying out low-altitude image acquisition on the mountain tea garden in the patrol area, then preprocessing the acquired image to obtain an original image to be spliced, wherein the original image contains a quadrilateral land block serving as an unmanned aerial vehicle parking apron, and the preprocessing specific steps of the image are as follows: firstly, performing primary enhancement processing on an image by a spatial domain method, performing secondary enhancement processing on the image by a high-pass filter, then performing equalization processing on the image, and finally obtaining a preprocessed low-altitude original image of the mountain tea garden;
step two: dividing an original image to be spliced into a main image and a secondary image according to the length of a road in the image, then adopting the brightness characteristics of the main image and the secondary image and carrying out threshold segmentation to obtain a binary image of the main image and the secondary image, determining a white area of a quadrilateral land block in the binary image of the main image and the secondary image as a quadrilateral reference object, and carrying out threshold segmentation by using the R + G + B brightness characteristics of the original image to be spliced as segmentation characteristics, then using an OSTU method to determine a threshold, and then adopting threshold segmentation to segment the original image to be spliced into a binary image;
step three: firstly, determining pixel point coordinates of four corner points of a quadrilateral reference object in a binary image of a main image and a secondary image in an original image, and then sequentially extracting four corner points A of the quadrilateral reference object on the main image according to the determined pixel point coordinates1、B1、C1And D1And four corner points A of the quadrilateral reference object on the sub-graph2、B2、C2And D2When coordinates of corner points of the quadrilateral reference object on the main graph and the secondary graph are extracted, the upper left corner of the four corner points of the quadrilateral reference object of the main graph is used as A1Sequentially extracting coordinates A clockwise1、B1、C1And D1The four corner points of the quadrilateral reference object on the secondary graph take the upper left corner as A2Sequentially extracting coordinates A clockwise2、B2、C2And D2；
Step four: firstly, the diagonal A of the quadrilateral reference object on the main graph is respectively obtained according to the four corners on the main graph and the auxiliary graph1C1And B1D1And the diagonal A of the quadrangular reference object on the sub-diagram2C2And B2D2According to the diagonal line A1C1、B1D1、A2C2And B2D2The scaling of the sub-map is calculated, the sub-map is then scaled according to the calculated scaling, and then according to the diagonal A1C1And A2C2And B1D1：B2D2Calculating the rotation angle of the sub-image at the included angle in the original image coordinate system, and calculating the scaling of the sub-image by calculating the diagonal A1C1、B1D1、A2C2And B2D2Length of (1), and then A1C1：A2C2And B1D1：B2D2The average value of (A) is used as the scaling ratio of the sub-map, and when the rotation angle of the sub-map is calculated, A is used first1C1And A2C2The angle in the image coordinate system is theta1Then with B1D1：B2D2The angle in the image coordinate system is theta2Then at theta1And theta2The average value of (a) is used as the rotation angle of the sub-map;
step five: firstly, selecting an area which is not covered by the main image in the auxiliary image as a part of the auxiliary image participating in splicing, and if the visual field of the auxiliary image is on the left, selecting the rotated image from the leftmost side to A1The point area is used as the part of the sub-image participating in the splicing, if the sub-image visual field is right, the rightmost side of the rotated image is selected to be C1And taking the region of the point as a part of the auxiliary image participating in splicing, rotating the part of the auxiliary image participating in splicing according to the rotating angle in the fourth step, and splicing with the main image to finish the splicing of the low-altitude image of the mountainous area tea garden.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (7)
1. A method for splicing low-altitude images of a mountainous area tea garden by taking a quadrangular plot as a reference object is characterized by comprising the following steps of:
the method comprises the following steps: firstly, presetting a patrol area of a mountain tea garden on an unmanned aerial vehicle, then starting the unmanned aerial vehicle to carry out daily flight patrol on the mountain tea garden, carrying out low-altitude image acquisition on the mountain tea garden in the patrol area, and then preprocessing the acquired images to obtain original images to be spliced, wherein the original images contain quadrangular plots serving as parking aprons of the unmanned aerial vehicle;
step two: dividing an original image to be spliced into a main image and a secondary image according to the length of a road in the image, then taking the brightness characteristics of the main image and the secondary image and carrying out threshold segmentation to obtain binary images of the main image and the secondary image, and determining a white area of a quadrilateral land block in the binary images of the main image and the secondary image as a quadrilateral reference object;
step three: firstly, determining pixel point coordinates of four corner points of a quadrilateral reference object in a binary image of a main image and a secondary image in an original image, and then sequentially extracting four corner points A of the quadrilateral reference object on the main image according to the determined pixel point coordinates1、B1、C1And D1And four corner points A of the quadrilateral reference object on the sub-graph2、B2、C2And D2；
Step four: firstly, the diagonal A of the quadrilateral reference object on the main graph is respectively obtained according to the four corners on the main graph and the auxiliary graph1C1And B1D1And the diagonal A of the quadrangular reference object on the sub-diagram2C2And B2D2According to the diagonal line A1C1、B1D1、A2C2And B2D2The scaling of the sub-map is calculated, the sub-map is then scaled according to the calculated scaling, and then according to the diagonal A1C1And A2C2And B1D1：B2D2Calculating the rotation angle of the secondary image at an included angle in the original image coordinate system;
step five: firstly, selecting an area which is not covered by the main graph in the auxiliary graph as a part of the auxiliary graph participating in splicing, then rotating the part of the auxiliary graph participating in splicing according to the rotating angle in the fourth step, and splicing with the main graph to finish the splicing of the low-altitude images of the mountainous area tea garden.
2. The method for splicing the low-altitude images of the mountain tea garden by taking the quadrangular land blocks as the reference objects according to claim 1, wherein the method comprises the following steps: in the first step, the image preprocessing specifically comprises the following steps: firstly, carrying out edge detection processing on the image, then carrying out equalization processing on the image, then carrying out noise reduction processing on the image after the equalization processing, and finally obtaining a low-altitude original image of the mountainous area tea garden after pretreatment.
3. The method for splicing the low-altitude images of the mountain tea garden by taking the quadrangular land blocks as the reference objects according to claim 1, wherein the method comprises the following steps: in the second step, the specific step of threshold segmentation is to take the R + G + B brightness feature of the original image to be spliced as the segmentation feature, determine the threshold by using the OSTU method, and then segment the original image to be spliced into a binary image by using threshold segmentation.
4. The method for splicing the low-altitude images of the mountain tea garden by taking the quadrangular land blocks as the reference objects according to claim 1, wherein the method comprises the following steps: in the third step, when extracting the coordinates of the corner points of the quadrilateral reference object on the main graph and the auxiliary graph, the upper left corner of the four corner points of the quadrilateral reference object of the main graph is used as A1Sequentially extracting coordinates A clockwise1、B1、C1And D1The four corner points of the quadrilateral reference object on the secondary graph take the upper left corner as A2Sequentially extracting coordinates A clockwise2、B2、C2And D2。
5. The method for splicing the low-altitude images of the mountain tea garden by taking the quadrangular land blocks as the reference objects according to claim 1, wherein the method comprises the following steps: in the fourth step, when the scaling of the sub-image is calculated, the diagonal A is calculated respectively1C1、B1D1、A2C2And B2D2Length of (1), and then A1C1：A2C2And B1D1：B2D2The average of (d) is used as a scaling of the sub-graph.
6. The mountain area with quadrangular land as reference object according to claim 1The low-altitude image splicing method for the tea garden is characterized by comprising the following steps: in the fourth step, when the rotation angle of the secondary graph is calculated, A is used firstly1C1And A2C2The angle in the image coordinate system is theta1Then with B1D1：B2D2The angle in the image coordinate system is theta2Then at theta1And theta2The average value of (a) is used as the rotation angle of the sub-map.
7. The method for splicing the low-altitude images of the mountain tea garden by taking the quadrangular land blocks as the reference objects according to claim 1, wherein the method comprises the following steps: in the fifth step, if the sub-image view is on the left, the rotated image from the leftmost to A is selected1The point area is used as the part of the sub-image participating in the splicing, if the sub-image visual field is right, the rightmost side of the rotated image is selected to be C1The region of points serves as a part of the sub-map participating in the stitching.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111597281.XA CN114240758B (en) | 2021-12-24 | 2021-12-24 | Mountain tea garden low-altitude image splicing method taking quadrilateral plots as reference objects |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111597281.XA CN114240758B (en) | 2021-12-24 | 2021-12-24 | Mountain tea garden low-altitude image splicing method taking quadrilateral plots as reference objects |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114240758A true CN114240758A (en) | 2022-03-25 |
| CN114240758B CN114240758B (en) | 2022-08-05 |
Family
ID=80762514
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111597281.XA Active CN114240758B (en) | 2021-12-24 | 2021-12-24 | Mountain tea garden low-altitude image splicing method taking quadrilateral plots as reference objects |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114240758B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115294482A (en) * | 2022-09-26 | 2022-11-04 | 山东常生源生物科技股份有限公司 | Edible fungus yield estimation method based on unmanned aerial vehicle remote sensing image |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040057633A1 (en) * | 2002-09-19 | 2004-03-25 | Mai Tuy Vu | System for mosaicing digital ortho-images |
| US20130208997A1 (en) * | 2010-11-02 | 2013-08-15 | Zte Corporation | Method and Apparatus for Combining Panoramic Image |
| CN106485655A (en) * | 2015-09-01 | 2017-03-08 | 张长隆 | A kind of taken photo by plane map generation system and method based on quadrotor |
| CN107580175A (en) * | 2017-07-26 | 2018-01-12 | 济南中维世纪科技有限公司 | A kind of method of single-lens panoramic mosaic |
| CN110225264A (en) * | 2019-05-30 | 2019-09-10 | 石河子大学 | Unmanned plane near-earth is taken photo by plane the method for detecting farmland incomplete film |
| KR20200106680A (en) * | 2019-03-05 | 2020-09-15 | 경북대학교 산학협력단 | Orthophoto building methods using aerial photographs |
| CN113340272A (en) * | 2021-06-29 | 2021-09-03 | 上海良相智能化工程有限公司 | Ground target real-time positioning method based on micro-group of unmanned aerial vehicle |
| US20210358106A1 (en) * | 2020-05-18 | 2021-11-18 | Zhejiang University | Crop yield prediction method and system based on low-altitude remote sensing information from unmanned aerial vehicle |
-
2021
- 2021-12-24 CN CN202111597281.XA patent/CN114240758B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040057633A1 (en) * | 2002-09-19 | 2004-03-25 | Mai Tuy Vu | System for mosaicing digital ortho-images |
| US20130208997A1 (en) * | 2010-11-02 | 2013-08-15 | Zte Corporation | Method and Apparatus for Combining Panoramic Image |
| CN106485655A (en) * | 2015-09-01 | 2017-03-08 | 张长隆 | A kind of taken photo by plane map generation system and method based on quadrotor |
| CN107580175A (en) * | 2017-07-26 | 2018-01-12 | 济南中维世纪科技有限公司 | A kind of method of single-lens panoramic mosaic |
| KR20200106680A (en) * | 2019-03-05 | 2020-09-15 | 경북대학교 산학협력단 | Orthophoto building methods using aerial photographs |
| CN110225264A (en) * | 2019-05-30 | 2019-09-10 | 石河子大学 | Unmanned plane near-earth is taken photo by plane the method for detecting farmland incomplete film |
| US20210358106A1 (en) * | 2020-05-18 | 2021-11-18 | Zhejiang University | Crop yield prediction method and system based on low-altitude remote sensing information from unmanned aerial vehicle |
| CN113340272A (en) * | 2021-06-29 | 2021-09-03 | 上海良相智能化工程有限公司 | Ground target real-time positioning method based on micro-group of unmanned aerial vehicle |
Non-Patent Citations (4)
| Title |
|---|
| 唐如枫: "无人机航测在土地复垦调查中的应用研究", 《上海国土资源》 * |
| 张帆等: "低空林地航拍图像拼接的改进缝合线算法", 《北京林业大学学报》 * |
| 罗辉兰等: "图像拼接技术在茶树病虫害防治研究中的应用", 《安徽农业科学》 * |
| 许乙山: "茶园低空图像拼接技术及茶树目标提取研究", 《中国学位论文全文数据库》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115294482A (en) * | 2022-09-26 | 2022-11-04 | 山东常生源生物科技股份有限公司 | Edible fungus yield estimation method based on unmanned aerial vehicle remote sensing image |
| CN115294482B (en) * | 2022-09-26 | 2022-12-20 | 山东常生源生物科技股份有限公司 | Edible fungus yield estimation method based on unmanned aerial vehicle remote sensing image |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114240758B (en) | 2022-08-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107463918B (en) | Lane line extraction method based on fusion of laser point cloud and image data | |
| Zhang et al. | Object-oriented shadow detection and removal from urban high-resolution remote sensing images | |
| CN107895376A (en) | Based on the solar panel recognition methods for improving Canny operators and contour area threshold value | |
| WO2016172827A1 (en) | Stepwise-refinement pavement crack detection method | |
| CN109584281B (en) | Overlapping particle layering counting method based on color image and depth image | |
| CN108710840B (en) | Visual navigation path identification method for farmland pesticide spraying robot | |
| CN109345554A (en) | A kind of adhesion mushroom vision in-situ measuring method based on RGB-D camera | |
| CN102663760A (en) | Location and segmentation method for windshield area of vehicle in images | |
| CN106326822A (en) | Method and device for detecting lane line | |
| CN105424709A (en) | Fruit surface defect detection method based on image marking | |
| CN103839069A (en) | Lawn miss cutting recognition method based on image analysis | |
| CN102999916A (en) | Edge extraction method of color image | |
| CN103295018A (en) | Method for precisely recognizing fruits covered by branches and leaves | |
| CN110175556B (en) | Remote sensing image cloud detection method based on Sobel operator | |
| CN105405148B (en) | A kind of remote sensing image Chinese white poplar recognition methods of combination trees shadow character | |
| CN113450402B (en) | Navigation center line extraction method for vegetable greenhouse inspection robot | |
| CN103914836A (en) | Farmland machine leading line extraction algorithm based on machine vision | |
| CN102073872B (en) | Image-based method for identifying shape of parasite egg | |
| CN105719275A (en) | Parallel combination image defect segmentation method | |
| CN114708208A (en) | Famous tea tender shoot identification and picking point positioning method based on machine vision | |
| CN114240758B (en) | Mountain tea garden low-altitude image splicing method taking quadrilateral plots as reference objects | |
| CN109522904B (en) | Rule farmland extraction method based on remote sensing data | |
| CN103500451B (en) | A kind of independent floating ice extracting method for satellite data | |
| CN107264570A (en) | steel rail light band distribution detecting device and method | |
| CN115731257A (en) | Leaf form information extraction method based on 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 |