CN107527037A - Blue-green algae identification and analysis system based on unmanned aerial vehicle remote sensing data - Google Patents
Blue-green algae identification and analysis system based on unmanned aerial vehicle remote sensing data Download PDFInfo
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Abstract
The present invention discloses a kind of blue-green algae identification based on unmanned aerial vehicle remote sensing data and analysis system, including image capturing unit, Yunnan snub-nosed monkey unit, image joint unit, image interpretation and analytic unit and blue-green alga bloom analytic unit, Yunnan snub-nosed monkey unit includes image distortion and corrects module and image smoothing module, image joint unit includes image feature extraction module, Image Matching module and visual fusion module, image interpretation and analytic unit include Spectrum Analysis module, blue-green algae vegetation index builds module, blue-green algae identification module, training sample acquisition module, parameter optimization module and Optimized model, blue-green alga bloom analytic unit utilizes LIBSVM graders identification water-outlet body and blue-green algae.The blue-green algae identifies utilize unmanned aerial vehicle remote sensing data with analysis system, identifies blue-green algae, then by Optimized model, analyzes blue-green alga bloom phenomenon, and to produce and early warning is made in life, system is easily implemented, and with the progress of unmanned air vehicle technique, it is possible to increase the accuracy that blue-green algae identifies.
Description
Technical field
The present invention relates to blue-green algae identification and analysis system, knows more particularly to a kind of blue-green algae based on unmanned aerial vehicle remote sensing data
Not and analysis system.
Background technology
With the rapid development of economy, the aggravation of mankind's activity, pollutant discharge beyond standards or sewage treatment facility missing etc.
Factor causes substantial amounts of water pollution and eutrophication.The eutrophication of water body can cause the fast-growth of phytoplankton, especially
It is those algae with floating or locomitivity, the excessive multiplication of algae, the wawter bloom of algae is formed in the water surface.Blue-green algae in waters
The quality of life and life and health that breaking out for wawter bloom can not only influence aquatic products industry resource, can also influence the neighbouring people in waters, more can
Welding ecosystem balance, have become one of important environmental problem of mankind's concern.Therefore grasp in time blue in water body
The distribution of algae, bloom prealarming is proposed, it is significant for water environment protection.
For the blue-green alga bloom of anti-water-stop body large area, people need that blue-green alga bloom phenomenon is identified, prevent, monitor
And control.Conventional lake blue algae wawter bloom identification depends on prevention method samples calmodulin binding domain CaM environmental monitoring station on the spot
The experiment and analysis of interior blue algae monitoring equipment, i.e., alga cells number in a certain amount of water is calculated by Multifunctional water quality detection equipment
Mesh, chlorophyll concentration, nitrogen and phosphorus content etc.；Although this method can carry out digital classification to body eutrophication, enhancing is comparative,
But the conventional relatively time-consuming effort of blue algae monitoring method, it is unsuitable for determining the area and developing direction of blue-green alga bloom.In addition, on the spot
Sampling simply detects the blue-green algae density of relative discrete point, can not make correct evaluation to overall blue-green alga bloom distribution situation.This
Kind method is difficult to meet reality monitoring and the needs that vegetation ecology is studied.
The content of the invention
The embodiment of the present invention provides a kind of blue-green algae identification based on unmanned aerial vehicle remote sensing data and analysis system, the system utilize
Unmanned aerial vehicle remote sensing data, blue-green algae is identified, then by Optimized model, analyze blue-green alga bloom phenomenon, to produce and early warning is made in life.
Blue-green algae identification and analysis system based on unmanned aerial vehicle remote sensing data, including：
Image capturing unit, it includes unmanned aerial vehicle remote sensing device and camera arrangement；
Yunnan snub-nosed monkey unit, it includes image distortion and corrects module and image smoothing module；
Image joint unit, it includes image feature extraction module, Image Matching module and visual fusion module；
Image interpretation and analytic unit, it includes Spectrum Analysis module, blue-green algae vegetation index structure module, blue-green algae identification mould
Block, training sample acquisition module, parameter optimization module and Optimized model；
Blue-green alga bloom analytic unit, it utilizes LIBSVM graders identification water-outlet body and blue-green algae.
Preferably, described unmanned aerial vehicle remote sensing device includes flying platform, flight control unit, ground monitoring station, taken off
Recovery system and data acquisition process equipment, described camera arrangement are connected to described data acquisition equipment.
Preferably, described image distortion rectification module is the image distortion correction in described data acquisition process equipment
Module.
Preferably, described image smoothing module uses low pass filtering method, median filtering method or mean filter method
To realize smoothing processing.
Preferably, described image feature extraction module and described Image Matching module carry out feature by SIFT algorithms
Extraction and matching.
Preferably, image information passes through described Spectrum Analysis module, blue-green algae vegetation index structure module and blue-green algae identification
After resume module, identify blue-green algae, described training sample acquisition module, parameter optimization module and Optimized model formed for
The Optimized model that the blue-green algae information identified is compared.
The identification of the blue-green algae based on unmanned aerial vehicle remote sensing data of the present invention and analysis system, it utilizes unmanned aerial vehicle remote sensing data,
Blue-green algae is identified, then by Optimized model, analyzes blue-green alga bloom phenomenon, it is fast due to unmanned plane to produce and early warning is made in life
Hail exhibition so that the system is easily implemented, and with the progress of unmanned air vehicle technique, can further improve the accurate of blue-green algae identification
Property.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the required accompanying drawing used in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
Some embodiments of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can be with
Other accompanying drawings are obtained according to these accompanying drawings.
Fig. 1 is the blue-green algae identification provided in an embodiment of the present invention based on unmanned aerial vehicle remote sensing data and the system stream of analysis system
Cheng Tu.
Embodiment
Below in conjunction with the accompanying drawing in the embodiment of the present invention, the technical scheme in the embodiment of the present invention is carried out clear, complete
Site preparation describes, it is clear that described embodiment is only part of the embodiment of the present invention, rather than whole embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art are obtained every other under the premise of creative work is not made
Embodiment, belong to the scope of protection of the invention.
As shown in figure 1, a kind of blue-green algae identification and analysis system based on unmanned aerial vehicle remote sensing data, including image capturing list
Member, Yunnan snub-nosed monkey unit, image joint unit, image interpretation and analytic unit and blue-green alga bloom analytic unit, image capturing
Unit includes unmanned aerial vehicle remote sensing device and camera arrangement, and Yunnan snub-nosed monkey unit includes image distortion correction module and image is smooth
Processing module, image joint unit include image feature extraction module, Image Matching module and visual fusion module, image interpretation
And analytic unit includes Spectrum Analysis module, blue-green algae vegetation index structure module, blue-green algae identification module, training sample collection mould
Block, parameter optimization module and Optimized model, blue-green alga bloom analytic unit utilize LIBSVM graders identification water-outlet body and blue-green algae.
Unmanned aerial vehicle remote sensing device generally comprises ground monitoring system, recovery system of rising and falling, data acquisition equipment, data processing
System and communication system, and photographing device uses SONY digital cameras in the present embodiment.During due to unmanned plane aerial photography easily by
To the influence of the factors such as wind, shade, after the flight end of job, user exports aviation image and position and posture from airborne equipment
Data (Position Orientation System, POS), need to carry out quality examination.Mainly include：(1) whether POS data
Compareed one by one with image, i.e., whether there is dropping fraction situation to be checked；(2) whether coverage of taking photo by plane is enough.(3) shadow is visually inspected
As whether shade influences later stage splicing and geography information expression.(4) magnified image, the atural object being adapted with resolution requirement is differentiated
It is whether clear and legible.
Because the image that unmanned plane carries digital camera shooting has distortion, distortion can cause image shape that non-perspective occurs
Convert and then make picture point, projection centre, 3 points of object point it is no longer conllinear, corresponding image rays is no longer intersecting, rebuilds the geometrical model of object
Deform.So the data handling system in UAS image information is corrected and smoothing processing.It is therein
Image smoothing processing can be realized using the one or more in low pass filtering method, median filtering method and mean filter method.
Carry out feature extraction and matching followed by SIFT algorithms, SIFT algorithms be widely used in feature extraction a little,
Matching, there are good rotation, yardstick affine-invariant features.Key point is detected based on Harris-Laplace, using SIFT feature
Point is described description, in characteristic matching stage using improved KD tree algorithms and RANSAC algorithm to feature
Point carries out thick matching and essence matching, improves matching precision.It is forthright for the high-resolution of unmanned plane image, extract characteristic point mistake
It is more, SIFT algorithmic match overlong times, the method that the present embodiment is accepted or rejected using Image Segmentation and characteristic point, improve traditional shadow
As matching strategy.It Image Segmentation is different chis to be using the cardinal principle that is matched of SIFT algorithms extraction image feature point
Degree spatially carries out characteristic point lookup, by finding out matching double points to characteristic point similitude comparing calculation.
Image information passes through described Spectrum Analysis module, blue-green algae vegetation index structure module and the processing of blue-green algae identification module
Afterwards, blue-green algae is identified, described training sample acquisition module, parameter optimization module and Optimized model are formed for identifying
The Optimized model that blue-green algae information is compared.The detection to water body changing features in conventional art is analogous to, it is general using as follows
Step, remote sensing image radiant correction, remotely sensing image geometric correction, remote sensing image smoothing processing, remote sensing image normalization and remote sensing
Image spectrum analysis.
Finally, identify that water-outlet body and blue-green algae, processing step are using LIBSVM graders：(1) data are processed into
LIBSVM graders require form；(2) operation is normalized to sample data and prediction data；(3) according to research object
Property, select suitable kernel function；(4) Linear Network search is utilized, with reference to cross validation, finds the optimal punishment for classification
Parameter C and nuclear parameter r；(5) support vector cassification model is established by the optimal parameter C that above-mentioned steps obtain and r；(6) it is sharp
Test and evaluation is carried out with the SVMs of foundation；Determine the type of ground objects only contained in image：Water body and blue-green algae.
Low latitude experiment of UAV remote sensing system can obtain high-resolution remote sensing image, have stronger flexibility and actuality,
It is fast into figure speed, it can be widely applied for the investigation in various fields.To landform complex region, manually it is difficult to reach region, and
Weather more than cloud and mist, satellite are difficult the region covered, can utilize the investigation of unmanned aerial vehicle remote sensing fast accurate.The skill of the application
Art scheme is exactly these characteristics using unmanned plane so that blue-green algae identification is stronger with Application of analysis system.
The blue-green algae identifies utilizes unmanned aerial vehicle remote sensing data with analysis system, identifies blue-green algae, then blue by Optimized model, analysis
Algae wawter bloom phenomenon, to produce and early warning is made in life, due to the fast development of unmanned plane so that the system is easily implemented, and with
The progress of unmanned air vehicle technique, it can further improve the accuracy of blue-green algae identification.
Obviously, those skilled in the art can carry out the essence of various changes and modification without departing from the present invention to the present invention
God and scope.So, if these modifications and variations of the present invention belong to the scope of the claims in the present invention and its equivalent technologies
Within, then the present invention is also intended to comprising including these changes and modification.
Claims (6)
1. a kind of blue-green algae identification and analysis system based on unmanned aerial vehicle remote sensing data, it is characterised in that including：
Image capturing unit, it includes unmanned aerial vehicle remote sensing device and camera arrangement；
Yunnan snub-nosed monkey unit, it includes image distortion and corrects module and image smoothing module；
Image joint unit, it includes image feature extraction module, Image Matching module and visual fusion module；
Image interpretation and analytic unit, it include Spectrum Analysis module, blue-green algae vegetation index structure module, blue-green algae identification module,
Training sample acquisition module, parameter optimization module and Optimized model；
Blue-green alga bloom analytic unit, it utilizes LIBSVM graders identification water-outlet body and blue-green algae.
2. the blue-green algae identification based on unmanned aerial vehicle remote sensing data and analysis system as claimed in claim 1, it is characterised in that described
Unmanned aerial vehicle remote sensing device include flying platform, flight control unit, ground monitoring station, recovery system of taking off and data acquisition at
Equipment is managed, described camera arrangement is connected to described data acquisition equipment.
3. the blue-green algae identification based on unmanned aerial vehicle remote sensing data and analysis system as claimed in claim 2, it is characterised in that described
Image distortion rectification module be described data acquisition process equipment in image distortion correct module.
4. the blue-green algae identification based on unmanned aerial vehicle remote sensing data and analysis system as claimed in claim 1, it is characterised in that described
Image smoothing module smoothing processing is realized using low pass filtering method, median filtering method or mean filter method.
5. the blue-green algae identification based on unmanned aerial vehicle remote sensing data and analysis system as claimed in claim 1, it is characterised in that described
Image feature extraction module and described Image Matching module pass through SIFT algorithms carry out feature extraction and matching.
6. the blue-green algae identification based on unmanned aerial vehicle remote sensing data and analysis system as claimed in claim 1, it is characterised in that image
Information identifies indigo plant after described Spectrum Analysis module, blue-green algae vegetation index structure module and the processing of blue-green algae identification module
Algae, described training sample acquisition module, parameter optimization module and Optimized model are formed for entering with the blue-green algae information identified
The Optimized model that row compares.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109187534A (en) * | 2018-08-01 | 2019-01-11 | 江苏凯纳水处理技术有限公司 | Water quality detection method and its water sample pattern recognition device |
| CN109472294A (en) * | 2018-10-15 | 2019-03-15 | 广州地理研究所 | A kind of recognition methods of urban water-body, device, storage medium and equipment |
| CN109977788A (en) * | 2019-03-03 | 2019-07-05 | 湖北无垠智探科技发展有限公司 | A kind of unmanned plane aerial photography image integrated treatment platform |
| CN110132370A (en) * | 2019-05-13 | 2019-08-16 | 苏州嘉奕晟中小企业科技咨询有限公司 | A kind of water project management range delimitation data collection system |
| CN110163139A (en) * | 2019-05-14 | 2019-08-23 | 苏州嘉奕晟中小企业科技咨询有限公司 | Three-dimensional digital information acquisition in city updates scanning system |
| CN110940314A (en) * | 2019-11-06 | 2020-03-31 | 同济大学 | Unmanned aerial vehicle-based cyanobacterial bloom hyperspectral monitoring and medicament spraying system |
| CN112710798A (en) * | 2020-12-03 | 2021-04-27 | 苏州工业园区测绘地理信息有限公司 | Water body blue algae identification system and method |
| CN114018338A (en) * | 2021-11-17 | 2022-02-08 | 天津市水利科学研究院 | Water body identification system based on spectral index model |
| CN115115940A (en) * | 2022-08-30 | 2022-09-27 | 中水三立数据技术股份有限公司 | Blue algae bloom monitoring method and monitoring and early warning system thereof |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150071528A1 (en) * | 2013-09-11 | 2015-03-12 | Digitalglobe, Inc. | Classification of land based on analysis of remotely-sensed earth images |
| CN106875636A (en) * | 2017-04-05 | 2017-06-20 | 南京理工大学 | Blue algae monitoring method for early warning and system based on unmanned plane |
-
2017
- 2017-08-31 CN CN201710767727.6A patent/CN107527037A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20150071528A1 (en) * | 2013-09-11 | 2015-03-12 | Digitalglobe, Inc. | Classification of land based on analysis of remotely-sensed earth images |
| CN106875636A (en) * | 2017-04-05 | 2017-06-20 | 南京理工大学 | Blue algae monitoring method for early warning and system based on unmanned plane |
Non-Patent Citations (3)
| Title |
|---|
| 唐晏: "基于无人机采集图像的植被识别方法研究", pages 33 - 41 * |
| 李鑫 等: "基于小型无人机可见光遥感的蓝藻识别研究", vol. 40, no. 40, pages 154 - 156 * |
| 林春生 等: "舰艇防控反导技术", 兵器工业出版社, pages: 70 * |
Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109187534A (en) * | 2018-08-01 | 2019-01-11 | 江苏凯纳水处理技术有限公司 | Water quality detection method and its water sample pattern recognition device |
| CN109472294A (en) * | 2018-10-15 | 2019-03-15 | 广州地理研究所 | A kind of recognition methods of urban water-body, device, storage medium and equipment |
| CN109977788A (en) * | 2019-03-03 | 2019-07-05 | 湖北无垠智探科技发展有限公司 | A kind of unmanned plane aerial photography image integrated treatment platform |
| CN110132370A (en) * | 2019-05-13 | 2019-08-16 | 苏州嘉奕晟中小企业科技咨询有限公司 | A kind of water project management range delimitation data collection system |
| CN110163139A (en) * | 2019-05-14 | 2019-08-23 | 苏州嘉奕晟中小企业科技咨询有限公司 | Three-dimensional digital information acquisition in city updates scanning system |
| CN110940314A (en) * | 2019-11-06 | 2020-03-31 | 同济大学 | Unmanned aerial vehicle-based cyanobacterial bloom hyperspectral monitoring and medicament spraying system |
| CN112710798A (en) * | 2020-12-03 | 2021-04-27 | 苏州工业园区测绘地理信息有限公司 | Water body blue algae identification system and method |
| CN112710798B (en) * | 2020-12-03 | 2022-11-08 | 苏州工业园区测绘地理信息有限公司 | Water body blue algae identification system and method |
| CN114018338A (en) * | 2021-11-17 | 2022-02-08 | 天津市水利科学研究院 | Water body identification system based on spectral index model |
| CN114018338B (en) * | 2021-11-17 | 2023-08-22 | 天津市水利科学研究院 | Water body identification system based on spectrum index model |
| CN115115940A (en) * | 2022-08-30 | 2022-09-27 | 中水三立数据技术股份有限公司 | Blue algae bloom monitoring method and monitoring and early warning system thereof |
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