CN111382724A - NDVI time sequence complex hurst-based low-temperature-resistant plant identification method - Google Patents
NDVI time sequence complex hurst-based low-temperature-resistant plant identification method Download PDFInfo
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Abstract
The invention discloses a low-temperature-resistant plant identification method based on NDVI time sequence complex hurst, which fully utilizes the characteristic phenological characteristics of low-temperature-resistant plants different from other crops, and remarkably improves the difference between the low-temperature-resistant plants and other ground objects through the complex hurst of the NDVI time sequence. According to the method, a high-spatial-resolution NDVI time sequence is constructed by adopting a single data source, the NDVI time sequence covers the whole growth period of the low-temperature-resistant plant, the dispersion of the NDVI time sequence of the low-temperature-resistant plant is reduced by establishing a complex Hurst model, the automatic acquisition of a threshold value is completed by utilizing sample data, and the improvement of the identification precision of the low-temperature-resistant plant is realized.
Description
Technical Field
The invention relates to a low-temperature-resistant plant remote sensing identification technology, in particular to a low-temperature-resistant plant identification method based on NDVI time sequence complex hurst.
Background
Low-temperature plants are one of the most important healthy nutritional food crops in the world, and the planting range is the widest in the world. Low-temperature-resistant plants are taken as main parts in the north all over the world, and timely and accurate extraction of the planting area of the low-temperature-resistant plants is the basis for developing yield prediction and is an important factor related to national food safety and social stability.
The traditional method for acquiring the area of the low-temperature-resistant plant based on field observation cannot meet the requirement of timely and accurately acquiring the area of the low-temperature-resistant plant in a large area. With the rapid development of remote sensing technology, remote sensing images are widely applied in the field of low temperature resistant plant monitoring. The early stage mainly adopts single time phase remote sensing image data to carry out the identification of low temperature resistant plants, and because the crop types are complicated and various, and obvious spectral overlap exists between different crops, the phenomenon of 'wrong division, missing division' is easy to appear when the single time phase remote sensing image data is used for carrying out the crop classification, and the ideal classification precision is difficult to achieve. With the continuous abundance of remote sensing data sources, the spectrum separability of different crops can be enhanced by multi-temporal remote sensing data and even time series remote sensing data in consideration of the difference of different crops along with seasonal changes, and the current remote sensing data time series, especially normalized vegetation index (NDVI) time series, has become a hotspot of crop identification research.
NDVI is the most common index for extracting crop information by using a remote sensing technology and is widely applied to crop classification and growth condition evaluation. The NDVI time sequence data can accurately reflect vegetation phenological information (emergence, flowering, pollination and maturity), effectively weaken the phenomena of 'same-species different spectrum and same-spectrum foreign matter', play an important role in crop classification research, and can be applied to identification of low-temperature-resistant plants. The current popular method is based on NDVI time sequence data of MODIS and NOAA, but because the image spatial resolution is low and crop planting types around the world are complex and various, land blocks are broken, only few pixels are composed of single land features, and the low-temperature resistant plant identification precision is limited. The GVG agricultural condition system of ground camera shooting provides an effective data source for the construction of high spatial resolution NDVI time sequences.
In the field of NDVI time series identification of crops, the current well-established method is decision tree classification, which only uses a few characteristic bands in the time series, but does not comprehensively consider the entire sequence. Therefore, the invention provides a low-temperature-resistant plant identification method based on NDVI time sequence complex hurst, fully considers the whole NDVI time sequence, and has simple and practical operation flow.
Disclosure of Invention
The invention aims to provide a low-temperature-resistant plant identification method based on NDVI time sequence complex hurst, which fully utilizes the characteristic phenological characteristics of low-temperature-resistant plants different from other crops, obviously improves the difference between the low-temperature-resistant plants and other ground objects through the complex hurst of the NDVI time sequence, and realizes high-precision identification of the low-temperature-resistant plants.
In order to achieve the purpose, the invention is realized by the following technical scheme:
a low-temperature-resistant plant identification method based on NDVI time series complex hurst comprises the following steps:
the method comprises the following steps: acquiring a GVG agricultural condition image sequence in a growth cycle of a low-temperature-resistant plant, and constructing an NDVI time sequence to form an NDVI time sequence curve with time as a horizontal coordinate and NDVI as a vertical coordinate;
step two: acquiring low-temperature-resistant plant sample data through field investigation or historical data;
step three: based on a low-temperature-resistant plant sample, acquiring an NDVI time sequence curve of a corresponding pixel, and performing quasi-symmetric windowing sliding average on the NDVI time sequences of all sample pixels to form a reference curve of the low-temperature-resistant plant NDVI time sequence;
step four: performing complex hurst on the NDVI time sequence curve by taking the NDVI time sequence reference curve of the low-temperature-resistant plant as a reference, namely subtracting the reference curve from the NDVI time sequence curve corresponding to all pixels in a test area, and calculating according to a formula to obtain an NDVI time sequence conversion curve of each pixel;
step five: on the basis of an NDVI time series conversion curve, respectively calculating a complex hurst model value and an angle of a conversion curve corresponding to a sample pixel by using low-temperature-resistant plant sample data, and then obtaining the maximum and minimum values of the model value and the angle as a threshold value of wheat identification through statistical analysis to realize automatic determination of the threshold value;
step six: and D, respectively calculating the complex Hurst model value and the complex Hurst model angle of the conversion curve corresponding to each pixel element in the test area by using the conversion curve obtained in the fourth step, judging by using the threshold obtained in the fifth step, judging the pixel element to be a low-temperature-resistant plant when the difference between the model value and the angle is in the threshold range, traversing the whole test area, and finally forming a low-temperature-resistant plant distribution graph.
Further, in the first step, the growth cycle of the low-temperature resistant plant is 10 months to 6 months in the next year, it is ensured as much as possible that GVG agricultural condition data exist in each month, before the NDVI time sequence is constructed, the data needs to be subjected to radiation calibration, atmospheric correction and geometric correction, then the NDVI is obtained by calculation using a red light band and a near infrared band, and finally the NDVI time sequence is formed.
Further, in the fourth step, the complex hurst is performed by taking the NDVI reference curve of the low-temperature resistant plant as a reference, the NDVI time sequence of the low-temperature resistant plant can be controlled to be close to the value of 0, the dispersion of the NDVI time sequence of the low-temperature resistant plant is reduced, and the dispersion of the model value and the angle or the NDVI time sequence of other ground objects is improved, so that the identification precision of the low-temperature resistant plant is improved.
Further, in the fourth step, the NDVI time series of the picture elements is represented as Vi ═ (v1, i, v2, i, … v8, i)
NDVI time series reference curve for cold tolerant plants is denoted Vref ═ Vref (v1, ref, v2, ref, … v8, ref)
According to the formula
And calculating Ti-Vi-Vref (v1, i-v1, ref, v2, i-v2, ref, … v8, i-v8, ref) to obtain the NDVI time series conversion curve of each pixel.
Furthermore, in the fifth step, the sample data of the low temperature resistant plant is representative, that is, the dynamic range of the complex hurst model value and the angle of the conversion curve obtained by using the sample pixels can represent the dynamic range of the complex hurst model value and the angle of the time series conversion curve of the low temperature resistant plant NDVI in the whole test area, and the threshold range of the complex hurst model value and the angle is used as the standard for identifying the low temperature resistant plant.
Compared with the prior art, the invention has the following advantages:
according to the method, a high-spatial-resolution NDVI time sequence is constructed by adopting a single data source, the NDVI time sequence covers the whole growth period of the low-temperature-resistant plant, the dispersion of the NDVI time sequence of the low-temperature-resistant plant is reduced by establishing a complex Hurst model, the automatic acquisition of a threshold value is completed by utilizing sample data, and the improvement of the identification precision of the low-temperature-resistant plant is realized.
Drawings
Fig. 1 is a flow chart of a method for identifying a low-temperature resistant plant based on NDVI time series complex hurst.
FIG. 2 is a graph of the NDVI of a typical object.
Figure 3 is a graph of NDVI time series after a complex hurst.
FIG. 4 is a diagram of a distribution of cold-resistant plants.
Detailed Description
The invention is further illustrated with reference to the following figures and detailed description.
Example (b): low-temperature-resistant vegetable lettuce identification method
The implementation flow of the present invention is shown in fig. 1, and the details of each part are as follows.
The method comprises the following steps: acquiring GVG agricultural condition data in a growth cycle of the low-temperature-resistant plant, and constructing an NDVI time sequence; the method selects places with longitude and latitude of (32 degrees 12 '19', 118 degrees 42 '31') in Pukou area of Nanjing city of Jiangsu province for experiment, takes a sample plot with an experimental area of 1.86m X1.5 m as an experimental sample plot, and has the acquisition time of 2018 for 10 months and the germination time of No. 10 months and No. 3. The lettuce growth process is collected once per day on average. The collection time is generally 12 am, and the temperature is kept at about 20 ℃. The collection is carried out from the germination period, and each collected picture is basically larger than 10. The height during collection is 2 meters, the camera shoots downwards, and the speed of capturing one picture every 3 seconds is kept for collection. GVG agricultural condition data (shown in table 1) of a low-temperature-resistant plant growth cycle are subjected to radiometric calibration, atmospheric correction, geometric correction and the like, NDVI is extracted, and an NDVI time sequence is constructed, so that continuous observation of a crop growth and development critical period is realized. The NDVI is obtained by calculating the red light band and the near infrared band of the GVG agricultural condition data through a formula (1).
In the formula: NIR is the reflection value of the near infrared band, and R is the reflection value of the red light band.
TABLE 1 GVG agricultural condition image
The lettuce planting interval in the test area is 9 horizontal rows and 12 vertical rows, sprouting is carried out 3 days after sowing, sampling is carried out from the sprouting period, the sampling time interval is every day, the time is fixed at 12 noon, and each sampling is positioned by combining manpower and a GPS. The sampling interval during sampling is snapshot every 3 seconds, and the information of each picture is recorded. The NDVI profile of a typical object is shown in fig. 2.
Step two: the sample data of the plant with low temperature resistance for 50 days is obtained in the test area through field investigation, and the sample data is distributed as uniformly as possible in the test area.
Step three: and constructing a reference curve based on the sample data of the low-temperature-resistant plant. In the present case, the NDVI time series contains a first half and a second half. Due to the influences of sowing time, water and fertilizer conditions and the like on the lettuce at different positions, the NDVI time series curves of the lettuce at different positions are different, so that quasi-symmetric windowing sliding average is carried out on the NDVI time series curves of all sample pixels (see formula (2)), and a low-temperature-resistant plant NDVI time series reference curve is formed.
Step four: NDVI time series complex hurst. And (3) performing complex hurst on the NDVI time series curve by taking the reference curve as a reference, namely subtracting the reference curve (see formula (3)) from the NDVI time series curve corresponding to all the pixels in the test area to obtain the NDVI time series conversion curve of each pixel.
Wherein:
m is the last point on the X-axis of the graph and μ is the mean of ξ.
The NDVI time series curve after the complex hurst is shown in figure 3, and it can be seen from the figure that the NDVI time series conversion curve of the low-temperature resistant plant is closer to 0 in mean value and smaller in dispersion compared with other ground objects.
Step five: and (4) determining a low-temperature-resistant plant identification threshold value. Assuming that the sample data of the low-temperature-resistant plant is representative, namely the dynamic range of the modulus and the angle of the conversion curve of the sample pixel can represent the dynamic range of the modulus and the angle of the conversion curve of the low-temperature-resistant plant in the whole test area; the threshold value of the low-temperature-resistant plant identification comprises the module value and the angle of a low-temperature-resistant plant conversion curve, based on the NDVI time sequence conversion curve, the module value and the angle of the conversion curve corresponding to the sample pixel are respectively calculated by using the low-temperature-resistant plant sample data, and the module value and the angle of all the sample pixels are subjected to statistical analysis to obtain the maximum value and the minimum value which are used as the threshold value of the low-temperature-resistant plant identification.
Argz＝argz+2nπ (5)
Step six: identification of low temperature resistant plants. Based on the NDVI time sequence conversion curve, calculating the module value and the angle of each pixel in the test area according to the formulas (4) and (5), judging whether the pixel is a low-temperature-resistant plant according to the formula (6), traversing the whole test area, and finally forming a low-temperature-resistant plant distribution diagram (figure 4). The sampling identification precision of the low-temperature resistant plants in the test is 95%.
(amplitude < amplitude threshold) · (angle < angle threshold) ═ low temperature or not (6)
The above description is only an example of the present invention, and is not intended to limit the present invention. The infrared image processing algorithms described above can also be used to process similar medical fluoroscopic images. All equivalents which come within the spirit of the invention are therefore intended to be embraced therein. Details not described herein are well within the skill of those in the art.
Claims (5)
1. A low-temperature-resistant plant identification method based on NDVI time sequence complex hurst is characterized in that: the method comprises the following steps:
the method comprises the following steps: acquiring a GVG agricultural condition image sequence in a growth cycle of a low-temperature-resistant plant, and constructing an NDVI time sequence to form an NDVI time sequence curve with time as a horizontal coordinate and NDVI as a vertical coordinate;
step two: acquiring low-temperature-resistant plant sample data through field investigation or historical data;
step three: based on a low-temperature-resistant plant sample, acquiring an NDVI time sequence curve of a corresponding pixel, and performing quasi-symmetric windowing sliding average on the NDVI time sequences of all sample pixels to form a reference curve of the low-temperature-resistant plant NDVI time sequence;
step four: performing complex hurst on the NDVI time sequence curve by taking the NDVI time sequence reference curve of the low-temperature-resistant plant as a reference, subtracting the reference curve from the NDVI time sequence curve corresponding to all pixels in the test area, and calculating according to a formula to obtain an NDVI time sequence conversion curve of each pixel;
step five: on the basis of an NDVI time series conversion curve, respectively calculating a complex hurst model value and an angle of a conversion curve corresponding to a sample pixel by using low-temperature-resistant plant sample data, and then obtaining the maximum and minimum values of the model value and the angle as a threshold value of wheat identification through statistical analysis to realize automatic determination of the threshold value;
step six: and D, respectively calculating the complex Hurst model value and the complex Hurst model angle of the conversion curve corresponding to each pixel element in the test area by using the conversion curve obtained in the fourth step, judging by using the threshold obtained in the fifth step, judging the pixel element to be a low-temperature-resistant plant when the difference between the model value and the angle is in the threshold range, traversing the whole test area, and finally forming a low-temperature-resistant plant distribution graph.
2. The method of claim 1, wherein the NDVI time series complex hurst-based low temperature resistant plant identification method comprises: in the first step, before the NDVI time sequence is constructed, data needs to be subjected to radiometric calibration, atmospheric correction and geometric correction, then the NDVI is obtained by utilizing the red light wave band and the near infrared wave band, and finally the NDVI time sequence is formed.
3. The method of claim 1, wherein the NDVI time series complex hurst-based low temperature resistant plant identification method comprises: in the fourth step, the complex hurst is carried out by taking the NDVI reference curve of the low-temperature resistant plant as a reference, the NDVI time sequence of the low-temperature resistant plant is controlled to be close to a value of 0, the dispersion of the NDVI time sequence of the low-temperature resistant plant is reduced, and the dispersion of the model value and the angle or the NDVI time sequence of other surface features is improved, so that the identification precision of the low-temperature resistant plant is improved.
4. The method for identifying cold-tolerant plants based on NDVI time series complex hurst as claimed in claim 1 or 3, wherein: in the fourth step, the NDVI time sequence of the image elements is expressed as
Vi＝(v1,i,v2,i,…v8,i)
NDVI time series reference curves for low temperature resistant plants are shown
Vref＝(v1,ref,v2,ref,…v8,ref)
According to the formula
And calculating Ti-Vi-Vref (v1, i-v1, ref, v2, i-v2, ref, … v8, i-v8, ref) to obtain the NDVI time series conversion curve of each pixel.
5. The method of claim 1, wherein the NDVI time series complex hurst-based low temperature resistant plant identification method comprises: in the fifth step, the sample data of the low temperature resistant plants is representative, that is, the dynamic range of the complex hurst model value and the angle of the conversion curve obtained by using the sample pixels can represent the dynamic range of the complex hurst model value and the angle of the time series conversion curve of the low temperature resistant plants NDVI in the whole test area, and the threshold range of the complex hurst model value and the angle is used as the standard of the low temperature resistant plant identification.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104915660A (en) * | 2015-07-01 | 2015-09-16 | 中国科学院遥感与数字地球研究所 | Winter wheat automatic recognition method based on GF-1/WFV NDVI time sequence |
| JP2015188333A (en) * | 2014-03-27 | 2015-11-02 | 株式会社日立製作所 | Vegetation growth analyzing system and method |
| CN105404873A (en) * | 2015-11-30 | 2016-03-16 | 中国科学院遥感与数字地球研究所 | Winter wheat recognition method based on NDVI time sequence coordinate conversion |
| JP2016123369A (en) * | 2015-01-06 | 2016-07-11 | 株式会社日立製作所 | Plant growth analysis system and plant growth analysis method |
| CN110119717A (en) * | 2019-05-15 | 2019-08-13 | 中国科学院遥感与数字地球研究所 | A kind of Crop classification method based on multi-temporal NDVI and LST |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2015188333A (en) * | 2014-03-27 | 2015-11-02 | 株式会社日立製作所 | Vegetation growth analyzing system and method |
| JP2016123369A (en) * | 2015-01-06 | 2016-07-11 | 株式会社日立製作所 | Plant growth analysis system and plant growth analysis method |
| CN104915660A (en) * | 2015-07-01 | 2015-09-16 | 中国科学院遥感与数字地球研究所 | Winter wheat automatic recognition method based on GF-1/WFV NDVI time sequence |
| CN105404873A (en) * | 2015-11-30 | 2016-03-16 | 中国科学院遥感与数字地球研究所 | Winter wheat recognition method based on NDVI time sequence coordinate conversion |
| CN110119717A (en) * | 2019-05-15 | 2019-08-13 | 中国科学院遥感与数字地球研究所 | A kind of Crop classification method based on multi-temporal NDVI and LST |
Non-Patent Citations (1)
| Title |
|---|
| 王新明等: "应用R/S方法分析NDVI时间序列" * |
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