CN114612794B - Remote sensing identification method for ground cover and planting structure of finely divided agricultural area - Google Patents
Remote sensing identification method for ground cover and planting structure of finely divided agricultural area Download PDFInfo
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Abstract
The invention belongs to the field of agricultural remote sensing, and relates to a remote sensing identification method of a ground covering and planting structure of a finely divided agricultural area. According to the invention, the agricultural area and the non-agricultural area are classified by the support vector machine SVM of the supervision algorithm, and then the crop type is further classified and judged by adopting the decision tree algorithm based on the obtained agricultural area classification raster data. The method does not depend on measured data, greatly reduces classification time and economic cost, and effectively avoids negative influence of the quality of the sample training set of the traditional machine algorithm on classification results. Meanwhile, the method meets the land utilization drawing precision required by agricultural remote sensing application on the premise of ensuring the classification high efficiency.
Description
Technical Field
The invention belongs to the field of agricultural remote sensing, and relates to a remote sensing identification method of a ground covering and planting structure of a finely divided agricultural area.
Background
The method for accurately and timely acquiring the land cover information of the agricultural area has important significance for monitoring agricultural conditions, adjusting agricultural structures, estimating yield, formulating grain policies and the like. Traditional field investigation methods are time-consuming and laborious, and are difficult to obtain large-scale regional land utilization information. The remote sensing technology can overcome the defect of poor space representativeness of single-point observation under complex ground surface conditions, and provides multi-phase, multi-spectrum and multi-angle ground surface information for regional land utilization identification.
At present, land utilization remote sensing inversion is mainly divided into two modes of classification based on multi-source data weathers and polarization characteristics and based on actual measurement sample points. The recognition algorithm based on the sample points can quickly realize land utilization inversion of a large-scale area by only inputting a sample training set and utilizing satellite remote sensing images. The Support Vector Machine (SVM) and random forest algorithms are widely applied to land use remote sensing recognition due to high training efficiency and high accuracy, but the method has high sample accuracy requirements, and a large number of actual measurement samples are needed during application, so that the time and money cost is high. Meanwhile, for crops with similar spectrum and texture characteristics, the type of the algorithm can be mixed and separated during identification. The land utilization inversion can be realized by utilizing the decision tree algorithm based on the multisource weather and polarization characteristic algorithm only by establishing a specific classification rule without actually measuring a sample training set. The method is only aimed at ground object identification with obvious difference of the weather/polarization characteristics, and the original satellite image spectrum data is required to be processed and analyzed, so that the weather/polarization characteristics are calculated and used for classification. In summary, when the existing classification algorithm is applied to satellite remote sensing images, the single classification algorithm has long operation time, high requirement on computer configuration, and needs a large number of actual measurement samples, which is time-consuming and labor-consuming, so that large-scale land utilization identification is difficult to realize.
In practical application, various classifiers such as SVM, random forest, decision tree and the like have advantages and disadvantages, so the method combines the advantages of various algorithms, applies different algorithms to the characteristics of different ground objects of a finely crushed agricultural area, makes up for the shortages, and improves the classification efficiency and precision.
Disclosure of Invention
Aiming at the technical problems, the invention aims to provide a remote sensing identification method for ground coverage and planting structures of finely divided agricultural areas, which is characterized in that the identification of agricultural areas and non-agricultural areas is realized by utilizing a machine learning algorithm through satellite remote sensing image spectrums and texture features and LSWI indexes with good effect of identifying the agricultural areas and the non-agricultural areas. On the basis, a classification rule is established for the physical characteristics of different crops in the agricultural area, the physical indexes obtained by calculating the NDVI time sequence data are used as input data, the further division of the different crops in the agricultural area is realized based on a decision tree algorithm, and finally the high-precision land utilization remote sensing identification of the finely crushed agricultural area is realized.
In order to achieve the above object, the present invention provides the following technical solutions:
A remote sensing identification method for ground coverage and planting structures of finely divided agricultural areas comprises the following steps:
S1, carrying out agricultural region and non-agricultural region identification on an original satellite image by using a support vector machine SVM supervised learning algorithm:
S1.1, resampling each wave band of a plurality of original satellite images with different meteorological conditions in different periods into uniform spatial resolution by adopting a bilinear interpolation method; selecting an original satellite image with vigorous growth of the early-stage August crops, and selecting a visible light wave band capable of better distinguishing ground objects and a red-edge wave band, a near-infrared wave band or a short-wave infrared wave band capable of judging plant growth conditions in the image as classification recognition wave bands; calculating texture values of the classification and identification bands based on a 3×3 window and 64 gray quantization levels by using a gray tone space correlation matrix by using a second-order probability statistical method;
S1.2, obtaining a land water index LSWI time sequence data set by utilizing an original satellite image under a cloudless condition of a multi-period clear sky in a growth period, resampling the land water index LSWI time sequence data set into uniform time resolution by adopting a linear interpolation method, reassigning the land water index LSWI time sequence data in the data set by using a preset threshold value, assigning pixels larger than the preset threshold value to 0, assigning pixels smaller than the preset threshold value to 1, sequentially adding and summing the reassigned land water index LSWI time sequence data to obtain land water index LSWI reclassification data;
wherein: LSWI is land water index; ρ NIR represents the reflectivity in the near infrared band; ρ SWIR represents the reflectivity of the short wave infrared band;
S1.3, selecting a land utilization sample set from Google Earth as training data, utilizing a support vector machine SVM supervised classification algorithm to train and learn the classification recognition wave band and texture value obtained in the step S1.1 and land water index LSWI reclassification data obtained in the step S1.2, and calculating to obtain land utilization classification results of agricultural areas and non-agricultural areas;
S2, carrying out further planting structure division on the agricultural area by applying a decision tree algorithm to obtain final agricultural area high-precision land utilization data:
S2.1, converting land utilization classification results obtained in the step S1 into raster data, reassigning the raster data, assigning an agricultural area to 1, and assigning a non-agricultural area to NoData; converting the reassigned raster data into vector data to obtain shp vector data only containing an agricultural area;
S2.2, cutting an original satellite image under a multi-period clear sky cloudless condition in a growing period based on shp vector data only comprising an agricultural area obtained in the step S2.1 to obtain an original satellite image only comprising the agricultural area, calculating through a formula II to obtain a normalized vegetation index NDVI time sequence data set, and resampling the normalized vegetation index NDVI time sequence data set to uniform time resolution by adopting a linear interpolation method;
Wherein: NDVI is normalized vegetation index; ρ NIR represents the reflectivity in the near infrared band; ρ Red represents the reflectivity of the red band;
S2.3, smoothing the normalized vegetation index NDVI time sequence data set resampled in the step S2.2 by utilizing a Savitzky-Golay filter; establishing a weather indicator judgment standard based on the smoothed NDVI growth period process curve, judging each pixel of the agricultural area, and finally obtaining weather indicator grid data; the weather indicator grid data comprises three weather indicators including a growth period starting time, a growth period ending time and a growth period length;
The establishment process of the weather indicator judgment standard is as follows:
Determining an ascending phase, a descending phase and an NDVI set value of the smoothed NDVI fertility phase process curve, wherein when NDVI=set value, the moment corresponding to the ascending phase is a fertility phase starting time, the moment corresponding to the descending phase is a fertility phase ending time, and the time length in the fertility phase starting time and the fertility phase ending time is a fertility phase length;
S2.4, establishing crop classification rules according to the value range of three weather indexes of different crops; based on crop classification rules, judging each grid in the weather indicator grid data obtained in the step S2.3 by utilizing a decision tree algorithm to obtain an agricultural planting structure classification result, and finally dividing each grid into a certain specific crop type, thereby realizing crop identification of an agricultural area;
and S3, inlaying the non-agricultural area in the land utilization classification result obtained in the step S1 and the agricultural planting structure classification result obtained in the step S2 together to obtain a land utilization and planting structure classification diagram of the agricultural planting area with classification label colors.
In the step S1.2, the preset threshold is 0.2.
In the step S1.3, in the land utilization classification result, the agricultural area is a farmland, and the non-agricultural area comprises a water body, a sand dune, a residential area and a natural land.
In the step S2.3, the set value is 0.3.
The classification result of the agricultural planting structure obtained in the step S2.4 comprises wheat, corn, sunflower and fruits and vegetables.
In the step S2.4, the value range of three physical indexes of different crops is respectively determined based on local actual experience and field investigation.
In the step S3, each pixel in the classification map of land utilization and planting structure in the agricultural planting area has a classification, and different values and colors represent different classifications.
Compared with the prior art, the invention has the beneficial effects that:
1. The invention solves the problems of limited field sample acquisition quantity, higher requirement on the quality of the actually measured sample and difficult acquisition, and ensures the classification precision and stability under the condition of no need of actually measured sample.
2. Compared with the traditional algorithm, the method reduces the time cost and the money cost, has strong operability, and can also realize high-precision land utilization identification by common hardware equipment when being applied to large-area high-precision remote sensing images, so that the automatic acquisition of the land utilization of the agricultural area of the large area is possible.
Drawings
FIG. 1 is a flow chart of a remote sensing identification method of a finely divided agricultural area land cover and planting structure of the present invention;
FIG. 2 is an original satellite remote sensing image (Sentinel-2 historical images are used in this description, other satellite original images are possible) according to an embodiment of the invention;
FIG. 3 is a schematic diagram of a process for calculating texture values of an original satellite remote sensing image according to an embodiment of the present invention;
fig. 4 is a calculation process of SVM classification supported by the supervision algorithm according to the embodiment of the present invention, and a land utilization image obtained after classification;
FIG. 5 is a schematic illustration of a selected weatherometer according to the present invention;
FIG. 6 is a decision tree classification rule for crop classification according to an embodiment of the present invention;
FIG. 7 is an image of an agricultural area after a specific planting structure classification in accordance with an embodiment of the present invention;
FIG. 8 is a classification chart of land utilization and planting structures finally obtained according to an embodiment of the present invention;
fig. 9 is a diagram of each category in the classification result.
Detailed Description
The invention will be further described with reference to the drawings and examples.
As shown in fig. 1, a remote sensing identification method for a ground cover and planting structure of a finely divided agricultural area comprises the following steps:
S1, carrying out agricultural region and non-agricultural region identification on an original satellite image by using a support vector machine SVM supervised learning algorithm:
S1.1, resampling each wave band of a plurality of original satellite images with different meteorological conditions in different periods into uniform spatial resolution by adopting a bilinear interpolation method; selecting an original satellite image with vigorous growth of the early-stage August crops, and selecting a visible light wave band capable of better distinguishing ground objects and a red-edge wave band, a near-infrared wave band or a short-wave infrared wave band capable of judging plant growth conditions in the image as classification recognition wave bands; calculating texture values of the classification and identification bands based on a 3×3 window and 64 gray quantization levels by using a gray tone space correlation matrix by using a second-order probability statistical method;
S1.2, obtaining a land water index LSWI time sequence data set by utilizing an original satellite image under a cloudless condition of a multi-period clear sky in a growth period, resampling the land water index LSWI time sequence data set into uniform time resolution by adopting a linear interpolation method, reassigning the land water index LSWI time sequence data in the data set by using a preset threshold value, assigning pixels larger than the preset threshold value to 0, assigning pixels smaller than the preset threshold value to 1, sequentially adding and summing the reassigned land water index LSWI time sequence data to obtain land water index LSWI reclassification data;
Wherein: LSWI is land water index; ρ NIR represents the reflectivity in the near infrared band; ρ SWIR represents the reflectivity of the short wave infrared band.
The preset threshold is 0.2.
S1.3, selecting a land utilization sample set from Google Earth as training data, utilizing a support vector machine SVM supervised classification algorithm to train and learn the classification recognition wave band and texture value obtained in the step S1.1 and land water index LSWI reclassification data obtained in the step S1.2, and calculating to obtain land utilization classification results of agricultural areas and non-agricultural areas;
in the land utilization classification result, the agricultural area is a farmland, and the non-agricultural area comprises a water body, a sand dune, a residential area and a natural land.
S2, carrying out further planting structure division on the agricultural area by applying a decision tree algorithm to obtain final agricultural area high-precision land utilization data:
S2.1, converting land utilization classification results obtained in the step S1 into raster data, reassigning the raster data, assigning an agricultural area to 1, and assigning a non-agricultural area to NoData; converting the reassigned raster data into vector data to obtain shp vector data only containing an agricultural area;
S2.2, cutting an original satellite image under a multi-period clear sky cloudless condition in a growing period based on shp vector data only comprising an agricultural area obtained in the step S2.1 to obtain an original satellite image only comprising the agricultural area, calculating through a formula II to obtain a normalized vegetation index NDVI time sequence data set, and resampling the normalized vegetation index NDVI time sequence data set to uniform time resolution by adopting a linear interpolation method;
wherein: NDVI is normalized vegetation index; ρ NIR represents the reflectivity in the near infrared band; ρ Red represents the reflectivity of the red band.
S2.3, smoothing the normalized vegetation index NDVI time sequence data set resampled in the step S2.2 by using a Savitzky-Golay (S-G) filter; establishing a weather indicator judgment standard based on the smoothed NDVI growth period process curve, judging each pixel of the agricultural area, and finally obtaining weather indicator grid data; the weather indicator grid data comprises three weather indicators including a growth period starting time, a growth period ending time and a growth period length;
The establishment process of the weather indicator judgment standard is as follows:
And determining an ascending phase, a descending phase and an NDVI set value of the smoothed NDVI fertility process curve, wherein when NDVI=set value, the time corresponding to the ascending phase is the start time of the fertility period, the time corresponding to the descending phase is the end time of the fertility period, and the time length in the start time and the end time of the fertility period is the length of the fertility period.
The set value is 0.3.
S2.4, establishing crop classification rules according to the value range of three weather indexes of different crops; based on crop classification rules, judging each grid in the weather indicator grid data obtained in the step S2.3 by utilizing a decision tree algorithm to obtain an agricultural planting structure classification result, and finally dividing each grid into a specific crop type, thereby realizing crop identification of an agricultural area.
The classification result of the agricultural planting structure obtained in the step S2 comprises wheat, corn, sunflower and fruits and vegetables.
The range of the three physical indicators of different crops is respectively determined based on local actual experience and field investigation.
And S3, inlaying the non-agricultural area in the land utilization classification result obtained in the step S1 and the agricultural planting structure classification result obtained in the step S2 together to obtain a land utilization and planting structure classification diagram of the agricultural planting area with classification label colors.
Each pixel in the classification diagram of the land utilization and planting structure of the agricultural planting area has a classification, and different values and colors represent different classifications.
Examples
S1, carrying out agricultural area and non-agricultural area identification on an original satellite image by using a Support Vector Machine (SVM) supervised learning algorithm;
Resampling each wave band of the Sentinel-2 original satellite image into uniform 10m spatial resolution by adopting a bilinear interpolation method, as shown in FIG. 2; an original satellite image with vigorous growth of the early-stage crops in August is selected, and visible light (red, green and blue) wave bands capable of better distinguishing ground features in the image and red-edge wave bands, near-infrared wave bands and short-wave infrared wave bands capable of judging plant growth conditions are selected as classification and identification wave bands. Calculating texture values of the optimal band based on a3×3 window and 64 gray quantization levels by using a gray tone space correlation matrix by using a second order probability statistical method, and finally obtaining texture values which are sequentially calculated based on eight texture filters (mean, variance, cooperativity, contrast, dissimilarity, information entropy, second order moment and correlation), as shown in fig. 3;
Obtaining a land water index LSWI time sequence data set by utilizing a Sentinel-2 original satellite image under a multi-period clear sky cloudless condition in a growth period through a formula I calculation, resampling the land water index LSWI data set to be uniform 5-day time resolution by adopting a linear interpolation method, reassigning the land water index LSWI data set by taking 0.2 as a threshold value, assigning pixels larger than 0.2 to be 0, assigning pixels smaller than 0.2 to be 1, sequentially adding and summing the reassigned land water index LSWI time sequence data to obtain land water index LSWI reclassification data;
Land utilization sample sets (residential lands, sand dunes, barren lands, water bodies and farmlands) are manually selected from Google Earth and used as training data, a support vector machine SVM supervised classification algorithm is used for training and learning the optimal wave band and texture values and LSWI reclassification data, and land utilization classification results of agricultural areas and non-agricultural areas are calculated and obtained, as shown in figure 4.
S2, performing further planting structure division on the agricultural area by applying a decision tree algorithm to obtain final agricultural area high-precision land utilization data;
converting land utilization classification results into raster data and reassigning the raster data, assigning 1 to an agricultural area, uniformly assigning NoData to a non-agricultural area, converting the reassigned raster data into vector data, and obtaining shp vector data of the agricultural area;
Cutting an original satellite image under a cloudless condition of a multi-period clear sky in a growing period based on the vector data of the agricultural region to obtain an original satellite image only comprising the agricultural region, calculating through a formula II to obtain a normalized vegetation index NDVI time sequence data set, and resampling the time sequence data set to be uniform 5-day time resolution by adopting a linear interpolation method;
The time series data set of the growth period NDVI is smoothed by a Savitzky-Golay (S-G) filter. As shown in fig. 5, based on the smoothed NDVI growth period process curve, determining an ascending stage and a descending stage of the curve, when ndvi=0.3, the time corresponding to the ascending stage and the descending stage are the growth period start time and the growth period end time respectively, the time length in the growth period start time and the growth period end time is the growth period length, and judging each pixel of the agricultural area based on the weather indicator judgment standard, so as to finally obtain weather indicator grid data containing three weather indicators of the growth period start time, the growth period end time and the growth period length for further crop classification.
Based on the local practical experience and the field investigation, the value range ranges of three physical indicators of different crops are respectively determined, and crop classification rules are established, as shown in fig. 6. Based on the classification rule, each grid in the weather indicator grid data is judged by utilizing a decision tree algorithm, and finally each grid is divided into a certain specific crop type, so that crop identification of an agricultural area is realized, and an agricultural area planting structure classification result shown in fig. 7 is obtained.
The non-agricultural area and the planting structure recognition result in the land use result are inlaid together as shown in fig. 8, so that an agricultural area high-precision land use classification result is obtained, an image with classification label colors is obtained as a classification result, the classification result comprises water body, sand dunes, residential areas, land use types of natural land non-agricultural areas and wheat, corn, sunflower and fruit and vegetable planting structure classification results, and a legend of various land features and crops is shown in fig. 9.
Claims (7)
1. The remote sensing identification method for the ground cover and planting structure of the finely divided agricultural area is characterized by comprising the following steps:
S1, carrying out agricultural region and non-agricultural region identification on an original satellite image by using a support vector machine SVM supervised learning algorithm:
S1.1, resampling each wave band of a plurality of original satellite images with different meteorological conditions in different periods into uniform spatial resolution by adopting a bilinear interpolation method; selecting an original satellite image with vigorous growth of the early-stage August crops, and selecting a visible light wave band capable of distinguishing ground features and a red-edge wave band, a near-infrared wave band or a short-wave infrared wave band capable of judging plant growth conditions in the image as classification recognition wave bands; calculating texture values of the classification and identification bands based on a 3×3 window and 64 gray quantization levels by using a gray tone space correlation matrix by using a second-order probability statistical method;
S1.2, obtaining a land water index LSWI time sequence data set by utilizing an original satellite image under a cloudless condition of a multi-period clear sky in a growth period, resampling the land water index LSWI time sequence data set into uniform time resolution by adopting a linear interpolation method, reassigning the land water index LSWI time sequence data in the data set by using a preset threshold value, assigning pixels larger than the preset threshold value to 0, assigning pixels smaller than the preset threshold value to 1, sequentially adding and summing the reassigned land water index LSWI time sequence data to obtain land water index LSWI reclassification data;
wherein: LSWI is land water index; ρ NIR represents the reflectivity in the near infrared band; ρ SWIR represents the reflectivity of the short wave infrared band;
S1.3, selecting a land utilization sample set from Google Earth as training data, utilizing a support vector machine SVM supervised classification algorithm to train and learn the classification recognition wave band and texture value obtained in the step S1.1 and land water index LSWI reclassification data obtained in the step S1.2, and calculating to obtain land utilization classification results of agricultural areas and non-agricultural areas;
S2, carrying out further planting structure division on the agricultural area by applying a decision tree algorithm to obtain final agricultural area high-precision land utilization data:
S2.1, converting land utilization classification results obtained in the step S1 into raster data, reassigning the raster data, assigning an agricultural area to 1, and assigning a non-agricultural area to NoData; converting the reassigned raster data into vector data to obtain shp vector data only containing an agricultural area;
S2.2, cutting an original satellite image under a multi-period clear sky cloudless condition in a growing period based on shp vector data only comprising an agricultural area obtained in the step S2.1 to obtain an original satellite image only comprising the agricultural area, calculating through a formula II to obtain a normalized vegetation index NDVI time sequence data set, and resampling the normalized vegetation index NDVI time sequence data set to uniform time resolution by adopting a linear interpolation method;
Wherein: NDVI is normalized vegetation index; ρ NIR represents the reflectivity in the near infrared band; ρ Red represents the reflectivity of the red band;
S2.3, smoothing the normalized vegetation index NDVI time sequence data set resampled in the step S2.2 by utilizing a Savitzky-Golay filter; establishing a weather indicator judgment standard based on the smoothed NDVI growth period process curve, judging each pixel of the agricultural area, and finally obtaining weather indicator grid data; the weather indicator grid data comprises three weather indicators including a growth period starting time, a growth period ending time and a growth period length;
The establishment process of the weather indicator judgment standard is as follows:
Determining an ascending phase, a descending phase and an NDVI set value of the smoothed NDVI fertility phase process curve, wherein when NDVI=set value, the moment corresponding to the ascending phase is a fertility phase starting time, the moment corresponding to the descending phase is a fertility phase ending time, and the time length in the fertility phase starting time and the fertility phase ending time is a fertility phase length;
S2.4, establishing crop classification rules according to the value range of three weather indexes of different crops; based on crop classification rules, judging each grid in the weather indicator grid data obtained in the step S2.3 by utilizing a decision tree algorithm to obtain an agricultural planting structure classification result, and finally dividing each grid into a certain specific crop type, thereby realizing crop identification of an agricultural area;
and S3, inlaying the non-agricultural area in the land utilization classification result obtained in the step S1 and the agricultural planting structure classification result obtained in the step S2 together to obtain a land utilization and planting structure classification diagram of the agricultural planting area with classification label colors.
2. The method for remotely sensing and identifying land cover and planting structures in finely divided agricultural areas according to claim 1, wherein in the step S1.2, the preset threshold is 0.2.
3. The method for remotely identifying land cover and planting structures in finely divided agricultural areas according to claim 1, wherein in the step S1.3, the agricultural area is a farmland, and the non-agricultural area includes water, sand dunes, residential areas and natural lands.
4. The method for remotely sensing and identifying land cover and planting structures in finely divided agricultural areas according to claim 1, wherein in the step S2.3, the set value is 0.3.
5. The method for remotely identifying land cover and planting structures in finely divided agricultural areas according to claim 1, wherein the classification result of agricultural planting structures obtained in step S2.4 comprises wheat, corn, sunflower and fruits and vegetables.
6. The method for remotely sensing and identifying the ground cover and planting structure of a finely divided agricultural area according to claim 1, wherein in the step S2.4, the value ranges of three physical indexes of different crops are respectively determined based on local actual experience and field investigation.
7. The method for remote sensing identification of finely divided agricultural land cover and planting structures according to claim 1, wherein in said step S3, each pixel in the classification map of agricultural land utilization and planting structures has a classification, and different values and colors represent different classifications.
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