CN112949602A - Unmanned aerial vehicle visible light image forest type classification method - Google Patents
Unmanned aerial vehicle visible light image forest type classification method Download PDFInfo
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- CN112949602A CN112949602A CN202110389459.5A CN202110389459A CN112949602A CN 112949602 A CN112949602 A CN 112949602A CN 202110389459 A CN202110389459 A CN 202110389459A CN 112949602 A CN112949602 A CN 112949602A
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Abstract
The invention discloses a classification method for forest types of visible light images of unmanned aerial vehicles, which adopts the remote sensing technology of the unmanned aerial vehicles, has better data processing software and diversified sensors, and provides more effective information support for forest resource monitoring; under ideal conditions, the optimal scheme of using the unmanned aerial vehicle is that the whole process from take-off to data processing and result output is automated, and in the field of forestry, the unmanned aerial vehicle remote sensing can carry out accurate forest resource monitoring to promote the development of forest resource control.
Description
Technical Field
The invention particularly relates to the field of unmanned aerial vehicle visible light image forests, and particularly relates to a method for classifying types of the unmanned aerial vehicle visible light image forests.
Background
Forest spatial information is the basis of forestry management, and can be used for solving various application problems faced by a forestry protection department, an accurate tree species distribution diagram is beneficial to forestry resource management and monitoring, carbon reserve estimation, forest disturbance evaluation and the like, at present, the demand of people on tree species composition and spatial distribution information is increasing day by day, and the remote sensing technology has obvious advantages in the aspect of obtaining spatial information; however, when the high-resolution remote sensing satellite is used for acquiring remote sensing images, the timeliness is poor, the price is high, the interference of external environment factors is easy to occur, and the resolution of the classification space accurate to the tree species level is not high enough at present; therefore, how to acquire accurate tree species spatial distribution information by using a remote sensing technology is the key and difficult point of the current forestry remote sensing.
Disclosure of Invention
Therefore, the invention provides a method for classifying the types of the forest of the visible light images of the unmanned aerial vehicle so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme: unmanned aerial vehicle visible light image forest type classification method is characterized by comprising the following steps:
s1: preparing an unmanned aerial vehicle, and loading a sensor on the unmanned aerial vehicle;
s2: judging the influence of forest high resolution;
s3: image preprocessing, namely sequentially performing digital orthographic imaging and establishing a digital elevation model and a digital surface model;
s4: classifying images through the digital orthoimages;
s5: subtracting the digital elevation model from the digital surface model to obtain a canopy height model;
s6: and acquiring forest data.
Further, in S1, it is preferable that the sensor is at least one of a hyperspectral sensor, a multispectral sensor, a thermal infrared sensor, and a lidar sensor.
Further, preferably, in S4, the image classification includes forest virus classification identification, forest species classification identification, forest habitats species identification, forest region composition identification, and forest region habitats identification.
Further, preferably, the forest zone composition recognition is performed by recognizing the contour, texture, color, structure and phenology of the forest.
Further, preferably, in S5, the canopy height model is constructed by forest factors including forest height, forest breast diameter and land occupation area.
Compared with the prior art, the invention has the beneficial effects that: the method adopts the unmanned aerial vehicle remote sensing technology, has better data processing software and diversified sensors, and provides more effective information support for forest resource monitoring; under ideal conditions, the optimal scheme of using the unmanned aerial vehicle is that the whole process from take-off to data processing and result output is automated, and in the field of forestry, the unmanned aerial vehicle remote sensing can carry out accurate forest resource monitoring to promote the development of forest resource control.
Drawings
Fig. 1 is a schematic flow chart of a method for classifying types of forest by using visible light images of an unmanned aerial vehicle;
FIG. 2 is a schematic diagram illustrating a flow of image classification in a forest type classification method for visible light images of an unmanned aerial vehicle;
fig. 3 is a schematic flow chart of a canopy height model in the unmanned aerial vehicle visible light image forest type classification method.
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 (b): referring to fig. 1-3, the present invention provides a technical solution: an unmanned aerial vehicle visible light image forest type classification method comprises the following steps:
s1: preparing an unmanned aerial vehicle, and loading a sensor on the unmanned aerial vehicle;
s2: judging the influence of forest high resolution;
s3: image preprocessing, namely sequentially performing digital orthographic imaging and establishing a digital elevation model and a digital surface model;
s4: classifying images through the digital orthoimages;
s5: subtracting the digital elevation model from the digital surface model to obtain a canopy height model;
s6: and acquiring forest data.
In this embodiment, in S1, the sensor is at least one of a hyperspectral sensor, a multispectral sensor, a thermal infrared sensor, and a lidar sensor.
In this embodiment, in S4, the image classification includes forest virus classification identification, forest species classification identification, forest habitats species identification, forest region composition identification, and forest region habitats identification.
In this embodiment, the forest zone composition recognition is performed by recognizing the contour, texture, color, structure, and phenology of the forest.
In this embodiment, in S5, the canopy height model is constructed by forest stand factors, where the forest stand factors include forest height, forest breast diameter, and area occupied.
Specifically, the method adopts the unmanned aerial vehicle remote sensing technology, has better data processing software and diversified sensors, and provides more effective information support for forest resource monitoring; under ideal conditions, the optimal scheme of using the unmanned aerial vehicle is that the whole process from take-off to data processing and result output is automated, and in the field of forestry, the unmanned aerial vehicle remote sensing can carry out accurate forest resource monitoring to promote the development of forest resource control.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. Unmanned aerial vehicle visible light image forest type classification method is characterized by comprising the following steps:
s1: preparing an unmanned aerial vehicle, and loading a sensor on the unmanned aerial vehicle;
s2: judging the influence of forest high resolution;
s3: image preprocessing, namely sequentially performing digital orthographic imaging and establishing a digital elevation model and a digital surface model;
s4: classifying images through the digital orthoimages;
s5: subtracting the digital elevation model from the digital surface model to obtain a canopy height model;
s6: and acquiring forest data.
2. The unmanned aerial vehicle visible light image forest type classification method according to claim 1, characterized in that: in S1, the sensor is at least one of a hyperspectral sensor, a multispectral sensor, a thermal infrared sensor, and a lidar sensor.
3. The unmanned aerial vehicle visible light image forest type classification method according to claim 1, characterized in that: in S4, the image classification includes forest virus classification identification, forest species classification identification, forest habitats species identification, forest region composition identification and forest region habitats identification.
4. The unmanned aerial vehicle visible light image forest type classification method according to claim 3, characterized in that: the forest zone composition identification is carried out by identifying the outline, texture, color, structure and phenology of the forest.
5. The unmanned aerial vehicle visible light image forest type classification method according to claim 1, characterized in that: in S5, the canopy height model is constructed by forest factors including forest height, forest breast diameter and occupied area.
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| Application Number | Priority Date | Filing Date | Title |
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| CN202110389459.5A CN112949602A (en) | 2021-04-12 | 2021-04-12 | Unmanned aerial vehicle visible light image forest type classification method |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114034341A (en) * | 2021-11-09 | 2022-02-11 | 北京林业大学 | Unmanned aerial vehicle forest resource and ecological environment monitoring system |
| CN115131678A (en) * | 2022-07-01 | 2022-09-30 | 杭州万林数链科技服务有限公司 | Block chain architecture construction method and system for digital intelligent forestry |
| CN116029430A (en) * | 2022-12-27 | 2023-04-28 | 江苏师范大学科文学院 | Grassland ecological environment monitoring system based on aerial image |
| CN116205394A (en) * | 2023-05-05 | 2023-06-02 | 浙江茂源林业工程有限公司 | Forest resource investigation and monitoring method and system based on radio navigation |
-
2021
- 2021-04-12 CN CN202110389459.5A patent/CN112949602A/en not_active Withdrawn
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114034341A (en) * | 2021-11-09 | 2022-02-11 | 北京林业大学 | Unmanned aerial vehicle forest resource and ecological environment monitoring system |
| CN115131678A (en) * | 2022-07-01 | 2022-09-30 | 杭州万林数链科技服务有限公司 | Block chain architecture construction method and system for digital intelligent forestry |
| CN116029430A (en) * | 2022-12-27 | 2023-04-28 | 江苏师范大学科文学院 | Grassland ecological environment monitoring system based on aerial image |
| CN116205394A (en) * | 2023-05-05 | 2023-06-02 | 浙江茂源林业工程有限公司 | Forest resource investigation and monitoring method and system based on radio navigation |
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