CN112016388A - Vegetation information extraction method based on visible light waveband unmanned aerial vehicle remote sensing image - Google Patents
Vegetation information extraction method based on visible light waveband unmanned aerial vehicle remote sensing image Download PDFInfo
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Abstract
The invention discloses a vegetation information extraction method based on a visible light waveband unmanned aerial vehicle remote sensing image, which comprises the following steps: acquiring a remote sensing orthographic image of an unmanned aerial vehicle in a research area; and calculating the vegetation index RGBGNSVI of the remote sensing image of the unmanned aerial vehicle by taking the pixel points as units to obtain a vegetation index raster image, wherein the RGBGNSVI can enhance green light information reflecting vegetation information and compress red and blue light information reflecting non-vegetation information. And then carrying out vegetation/non-vegetation information segmentation on the vegetation index grid map to finish vegetation information extraction. According to the invention, by providing the calculation formula of the vegetation index of the unmanned aerial vehicle visible light band for enhancing green light information reflecting vegetation information and compressing red and blue light information reflecting non-vegetation information, the calculation precision of the vegetation index of the visible light band remote sensing image and the extraction accuracy of the vegetation information are improved.
Description
Technical Field
The invention relates to a vegetation information extraction method of a remote sensing image, in particular to a vegetation information extraction method based on a visible light band unmanned aerial vehicle remote sensing image.
Background
Accurate quantification and inversion of land vegetation information is one of the important contents and hot spots in the research of land ecosystem in recent years. The vegetation index is the most direct and effective, simplest and practical measurement method for the land vegetation condition, and can effectively reflect land surface vegetation information and vitality. Therefore, accurate acquisition of vegetation indexes and vegetation information is a key content and basic parameter in many research fields such as ecology, water and soil conservation, forestry and the like.
There are many Vegetation indexes in the field of remote sensing, such as normalized Vegetation index ndvi (normalized Difference Vegetation index), ratio Vegetation index rvi (ratio Vegetation index), enhanced Vegetation index evi (enhanced Vegetation index), soil-regulating Vegetation index savi (soil-adjusting Vegetation index), modified soil-regulating Vegetation index msaii (modified soil-adjusting Vegetation index), Transformed Vegetation index tvi (Transformed Vegetation index), modified Transformed Vegetation index ctvi (modified Transformed Vegetation index), vertical Vegetation index pvlari (normalized Vegetation index), Difference Vegetation index dvi (Difference Vegetation index), weight Difference Vegetation index wdvi (green Vegetation index), and the like.
The vegetation indexes such as NDVI need to be calculated by using near infrared waveband data, but are limited by factors such as optical sensor cost, unmanned aerial vehicle load and the like, at present, an unmanned aerial vehicle carries a sensor mainly comprising a visible light true color sensor, and a calculation method for vegetation indexes in visible light wavebands is still not mature enough, so that the application of remote sensing images of the unmanned aerial vehicle is limited to a certain extent.
Therefore, it is necessary to find a feasible method for calculating the vegetation index only by using 3 visible light bands of red, green and blue, which is a scientific problem to be solved urgently in the field of the current unmanned aerial vehicle remote sensing technology, and is a technical support for the wide application of the unmanned aerial vehicle remote sensing technology.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a vegetation information extraction method based on a remote sensing image of a visible light waveband unmanned aerial vehicle.
The purpose of the invention is realized by the following technical scheme: a vegetation information extraction method based on a visible light waveband unmanned aerial vehicle remote sensing image comprises the following steps:
acquiring a remote sensing orthographic image of an unmanned aerial vehicle in a research area;
calculating an image vegetation index RGBGNSVI by taking pixel points as units to obtain a vegetation index raster image, wherein the RGBGNSVI has the following calculation formula:
RGBGNSVI＝(G-R)/(G+R)+(G-B)/(G+B)；
r, G, B, which represents the DN (reflectance value) of the red, green, and blue light channels at the pixel point, respectively;
determining an optimal threshold value for vegetation/non-vegetation information segmentation;
and carrying out vegetation/non-vegetation information segmentation on the data in the vegetation index raster map according to the optimal threshold value to finish vegetation information extraction.
According to the method, the calculation precision of the vegetation index of the visible light band remote sensing image and the extraction accuracy of the vegetation information are improved by enhancing green light information reflecting the vegetation information and compressing red and blue light information reflecting non-vegetation information.
Preferably, the vegetation index RGBGNSVI of the remote sensing image of the unmanned aerial vehicle can be calculated by the following formula:
RGBGNSVI＝(g-r)/(g+r)+(g-b)/(g+b)；
wherein the calculation formulas of r, g and b are respectively as follows:
r＝R/(R+G+B)
g＝G/(R+G+B)
b＝B/(R+G+B)。
preferably, after the remote sensing orthophoto image of the unmanned aerial vehicle is obtained, preprocessing is carried out on the remote sensing orthophoto image, and the preprocessing comprises geometric correction and cutting. Thereby performing more accurate information extraction for a specific region.
Preferably, an Optimal threshold (referred to as OST) for vegetation/non-vegetation information segmentation is obtained by a maximum entropy method or a two-peak histogram method.
Compared with the prior art, the invention has the following advantages and beneficial effects:
the vegetation index RGBGNSVI provided by the invention is suitable for a visible light wave band remote sensing image acquired by an unmanned aerial vehicle, 3 wave band information of red, green and blue are combined, the green light wave band information quantity reflecting vegetation information is enhanced, and the red light wave band information quantity and the blue light wave band information quantity reflecting non-vegetation information are compressed, so that the vegetation and non-vegetation information can be effectively distinguished, and the vegetation information segmentation and extraction precision is improved.
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For a clearer explanation of the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments 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 a person of ordinary skill in the relevant art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a method of an embodiment of the present invention.
Fig. 2 is a remote sensing ortho image of an original unmanned aerial vehicle according to an embodiment of the present invention.
Fig. 3 is the image of fig. 2 after geometric correction.
Fig. 4 is a view of the cropping of fig. 3.
Fig. 5 is a vegetation index raster graph obtained by the method based on the RGBGNSVI calculation formula in the embodiment of the present invention with respect to fig. 4.
Fig. 6 is a grid diagram (white areas indicate vegetation and the rest indicate non-vegetation) of vegetation information extraction obtained by the method of the embodiment of the present invention shown in fig. 5.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
Example 1
Referring to fig. 1, the embodiment provides a Vegetation information extraction method based on a visible light band unmanned aerial vehicle remote sensing image, and the method innovatively provides a Vegetation Index of the unmanned aerial vehicle remote sensing image, namely a Red-Green-Red-Blue Normalized Sum-Value combined Vegetation Index (rgbgnisvi for short), aiming at a visible light true color image shot by an unmanned aerial vehicle, wherein the Index only uses 3 visible light bands of Red, Green and Blue, and can realize accurate extraction of Vegetation information by enhancing Green light information reflecting the Vegetation information and compressing Red and Blue light information reflecting non-Vegetation information. The steps and effects thereof will be described in detail below with reference to the accompanying drawings.
And S1, acquiring the remote sensing ortho-image of the unmanned aerial vehicle in the research area.
The method specifically comprises the following steps:
acquiring basic condition information (terrain, boundary range, building condition and the like) of a project, making a field aerial photography route of the unmanned aerial vehicle, and determining the flight height of the unmanned aerial vehicle;
secondly, arriving at a research area, judging whether the weather conditions meet the requirements of flight safety, reliability and the like, and if the weather conditions meet the requirements, carrying out the next step, further determining and rechecking the flight height of the unmanned aerial vehicle;
checking by the unmanned aerial vehicle, wherein the checking comprises whether a battery, a remote controller and nearby interference sources exist or not, and if no problem exists, performing the next step;
and fourthly, taking off and shooting by the unmanned aerial vehicle, monitoring the flight working state of the unmanned aerial vehicle, and finishing the flight of the unmanned aerial vehicle.
Checking the quality of the pictures shot by the unmanned aerial vehicle on site, if the pictures are missed or blurred, and returning to carry out the interior work after the quality is determined to be not problematic;
sixthly, completing photo splicing of the unmanned aerial vehicle by using software such as Agisoft PhotoSacan and the like, and finally obtaining a project unmanned aerial vehicle remote sensing orthographic image (as shown in figure 2).
And S2, carrying out preprocessing such as geometric correction and cutting on the remote sensing orthographic image of the unmanned aerial vehicle.
Because the unmanned aerial vehicle aerial photograph in-process receives factor influences such as wind, leads to unmanned aerial vehicle state micro-change for unmanned aerial vehicle remote sensing image produces certain degree geometric distortion. In the embodiment, common geographic information processing software (such as mapInfo, Arc/Info, mapGIS, GeoStar and the like) is used for carrying out geometric correction on the remote sensing image of the unmanned aerial vehicle so as to obtain a more accurate result. The result is shown in FIG. 3 (the black dashed box in the figure indicates the region of interest). Unmanned aerial vehicle remote sensing image belongs to the low latitude aerial photograph, and geometric distortion is less relatively, and unmanned aerial vehicle image data difference before and after perhaps the naked eye is difficult to distinguish geometric correction in this embodiment.
According to the range of the actual work or research area, the present embodiment uses common geographic information processing software to cut the unmanned aerial vehicle remote sensing image after geometric correction, and cuts the image into a new image file, with the result shown in fig. 4.
And S3, calculating the vegetation index RGBGNSVI of the unmanned aerial vehicle remote sensing image.
The RGBGNSVI can be calculated by two different methods, both of which have the same inventive concept, that is, green light information reflecting vegetation information is enhanced, and red and blue light information reflecting non-vegetation information is compressed.
The first calculation formula is as follows:
RGBGNSVI＝(G-R)/(G+R)+(G-B)/(G+B)，
wherein R, G, B represents the DN values (reflectance values) for the red, green, and blue channels, respectively. The calculation results are shown in fig. 5.
The second calculation formula is as follows:
RGBGNSVI＝(g-r)/(g+r)+(g-b)/(g+b)；
wherein the calculation formulas of r, g and b are respectively as follows:
r＝R/(R+G+B)
g＝G/(R+G+B)
b＝B/(R+G+B)。
and S4, performing information segmentation on the vegetation index raster graph.
In this embodiment, the optimal threshold for vegetation information segmentation may be determined by a maximum entropy method or a bimodal histogram method. And dividing vegetation/non-vegetation information based on the optimal threshold value to finish vegetation information extraction. The calculation results are shown in fig. 6. After the vegetation information is extracted, the generated vegetation grid map is derived so as to be convenient for a user to store and calculate for use in the next step.
The result shows that the vegetation information extraction method provided by the invention has the vegetation information identification and segmentation accuracy of 93.31 percent, the overall accuracy of vegetation and non-vegetation identification and segmentation of 91.44 percent, and the Kappa coefficient for evaluating the remote sensing image classification extraction accuracy is up to 0.9576.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (4)
1. A vegetation information extraction method based on a visible light waveband unmanned aerial vehicle remote sensing image is characterized by comprising the following steps:
acquiring a remote sensing orthographic image of an unmanned aerial vehicle in a research area;
calculating an image vegetation index RGBGNSVI by taking pixel points as units to obtain a vegetation index raster image, wherein the RGBGNSVI has the following calculation formula:
RGBGNSVI＝(G-R)/(G+R)+(G-B)/(G+B)；
r, G, B, which represents the DN values of the red, green, and blue light channels at the pixel point, respectively;
determining an optimal threshold value for vegetation/non-vegetation information segmentation;
and carrying out vegetation/non-vegetation information segmentation on the data in the vegetation index raster map according to the optimal threshold value to finish vegetation information extraction.
2. The method for extracting vegetation information based on the visible light band unmanned aerial vehicle remote sensing image according to claim 1, wherein the vegetation index RGBGSVI of the unmanned aerial vehicle remote sensing image is calculated by the following formula:
RGBGNSVI＝(g-r)/(g+r)+(g-b)/(g+b)；
wherein the calculation formulas of r, g and b are respectively as follows:
r＝R/(R+G+B)
g＝G/(R+G+B)
b＝B/(R+G+B)。
3. the method for extracting vegetation information based on the remote sensing image of the unmanned aerial vehicle in the visible light waveband according to claim 1, wherein the unmanned aerial vehicle remote sensing ortho image is preprocessed after being acquired, and the preprocessing comprises geometric correction and cutting.
4. The visible light band unmanned aerial vehicle remote sensing image based vegetation information extraction method of claim 1, wherein the optimum threshold for vegetation/non-vegetation information segmentation is obtained by a maximum entropy method or a two-peak histogram method.
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| CN113191745A (en) * | 2021-05-28 | 2021-07-30 | 珠江水利委员会珠江水利科学研究院 | Remote sensing large-range evaluation method and medium for real estate construction project construction progress |
| CN117274844A (en) * | 2023-11-16 | 2023-12-22 | 山东科技大学 | Rapid extraction method for field peanut seedling emergence condition by using unmanned aerial vehicle remote sensing image |
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| CN117274844A (en) * | 2023-11-16 | 2023-12-22 | 山东科技大学 | Rapid extraction method for field peanut seedling emergence condition by using unmanned aerial vehicle remote sensing image |
| CN117274844B (en) * | 2023-11-16 | 2024-02-06 | 山东科技大学 | Rapid extraction method for field peanut seedling emergence condition by using unmanned aerial vehicle remote sensing image |
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