CN113516177A - Wheat lodging region identification method based on spectral texture features and support vector machine - Google Patents
Wheat lodging region identification method based on spectral texture features and support vector machine Download PDFInfo
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Abstract
The invention relates to a wheat lodging region identification method based on spectral texture characteristics and a support vector machine, wherein a multispectral sensor is assembled on an unmanned aerial vehicle platform to monitor a farmland; superposing single-channel remote sensing images of several wave bands based on the geographic coordinate information of each pixel according to the unmanned aerial vehicle image to generate a plurality of multispectral remote sensing images containing a plurality of channels; generating an image of only a lodging area or only a non-lodging area in a view field as an original data set according to lodging and non-lodging types in a visual estimation mode; calculating texture characteristics of the first two principal component components of all images in the original data set and the spectral reflectivity of each pixel in each wave band based on a principal component analysis method; constructing a support vector machine model, training, presetting the size of a window, and calling the support vector machine model to judge the type of each window; and counting the number of pixels of all the lodging regions, and further solving the area of the lodging regions.
Description
Technical Field
The invention relates to the field of agricultural remote sensing and the field of agricultural disaster assessment, in particular to a wheat lodging region identification method based on spectral texture characteristics and a support vector machine.
Background
Technical scheme of prior art I
The method for acquiring geographical information of lodging areas by related departments at present is as follows: dispatching workers to the disaster area; determining the boundary of the lodging area by using a nylon rope or chalk lime and the like, outlining the boundary of the lodging area, determining the geographic coordinates of the angular points by using tools such as a total station or a GPS (global positioning system) positioner and the like, and measuring information such as the length of the outline by using tools such as a tape measure and the like.
Disadvantages of the first prior art
The method is time-consuming and labor-consuming, is influenced by artificial subjective factors, and has disputed measuring results. Moreover, the measurement method is contact in nature, needs to manually search disaster areas in farmlands one by one, and can omit or even cause secondary damage.
Technical scheme of prior art II
There are currently some scholars and local units that use the satellite to do so to gather lodging area information. The method mainly downloads the high-resolution remote sensing image returned by the satellite and directly performs image analysis.
The second prior art has the defects
However, the method is limited by low resolution, no preset access time, easy influence of weather factors and the like, and the technology for extracting the crop lodging areas by using space remote sensing is very immature at present. The precision of the existing technology is not ideal.
Unmanned aerial vehicle remote sensing is extensively should with agricultural monitoring field, more and more scholars use unmanned aerial vehicle remote sensing monitoring aassessment crop biomass, detection environmental pressure. There is also an increasing research on using drones to extract lodging areas or estimate lodging severity.
Based on the above, the invention aims to provide a wheat lodging region identification method based on spectral texture features and a support vector machine, which is used for carrying out high-precision detection on a wheat lodging region.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a wheat lodging region identification method based on spectral texture characteristics and a support vector machine, which is used for carrying out high-precision monitoring on a wheat lodging region.
In order to achieve the above purposes, the technical scheme adopted by the invention is as follows:
a wheat lodging region identification method based on spectral texture features and a support vector machine comprises the following steps:
step 1: assembling a multispectral sensor on an unmanned aerial vehicle platform;
step 2: monitoring a farmland through an unmanned aerial vehicle platform assembled with a multispectral sensor to obtain an unmanned aerial vehicle image;
and step 3: matching, fusing and splicing unmanned aerial vehicle images of different wave bands and different groups corresponding to the same plot based on geographic coordinate information of each pixel according to each unmanned aerial vehicle image obtained by monitoring an unmanned aerial vehicle platform to generate a plurality of multi-channel multi-spectral remote sensing images;
and 4, step 4: cutting the multispectral remote sensing image generated in the step 3 in a visual estimation mode according to lodging and non-lodging, and generating an image with only lodging areas or only non-lodging areas in a view field as an original data set;
and 5: calculating texture characteristics of the first two principal component components of all images in the original data set and spectral reflectivity of each pixel of all images in each wave band based on a principal component analysis method, and taking the calculation result as a new data set;
step 6: dividing the new data set obtained in the step 5 into two parts: 80% is sample set, 20% is test set;
and 7: constructing a support vector machine model, inputting a sample set into the support vector machine model for training, verifying the precision of the model by using a test set, and judging that the training is successful when the precision reaches more than 90%;
and 8: presetting the size of a window, calling a successfully trained support vector machine model, and judging the type of each window; and counting the number of pixels of all the lodging regions, and further solving the area of the lodging regions.
On the basis of the above scheme, step 5 specifically includes:
step 51: and performing principal component analysis on each image in the original data set based on a principal component analysis method, thereby reducing data redundancy.
Step 52: calculating texture features of the first two principal component components of each image;
step 53: and counting the spectral reflectance values of each pixel of each remote sensing image in each wave band.
On the basis of the above scheme, the texture features described in step 52 are calculated by using a gray level co-occurrence matrix method.
On the basis of the above scheme, the method using the gray level co-occurrence matrix specifically includes: on an N × N image f (x, y), assuming that the gray value of a pixel point at a position (x, y) is i, and taking the point as a center, the probability P that a pixel point with a gray value j at a fixed distance (the distance between two pixel points is δ, and the direction is θ) appears is referred to as a gray co-occurrence matrix. The distance between the two pixels is delta, the delta is (dx2+ dy2) ^1/2, and the probability that the pixel with the gray level of j appears at the same time is represented as P (i, j, delta, theta); using the mathematical expression as:
P(i,j,δ,θ)＝{[(x,y),(x+dx,y+dy)]|f(x,y)＝i,f(x+dx,y+dy)＝j} (1)
on the basis of the above scheme, step 8 specifically includes:
step 81: taking the size of the picture with the minimum image size in the new data set obtained in the step 5 as the size of a window, translating the window on a target picture of the lodging region to be extracted, and dividing the target picture into a plurality of grids;
step 82: and calling a successfully trained support vector machine model to classify each grid into a lodging region and a non-lodging region, and representing the lodging region and the non-lodging region by different colors.
Step 83: counting the total number of pixels in the lodging region, and calculating to obtain the corresponding area S of each pixel according to a formula (2):
in the formula: n represents the resolution of the image; s represents the size area of the sensor; f represents the focal length of the sensor when acquiring the image; u represents the object distance at which the image was acquired.
The invention has the beneficial effects that:
through a multispectral sensor carried by an unmanned aerial vehicle platform, the number of pixels of all lodging regions can be effectively counted in real time based on a spectrum texture characteristic and a wheat lodging region identification method of a support vector machine, and the area of the lodging regions can be further calculated.
Drawings
The invention has the following drawings:
FIG. 1 is a flow chart of a wheat lodging region identification method based on spectral texture features and a support vector machine provided by the invention;
FIG. 2 is a structural diagram of a wheat lodging region identification method based on spectral texture features and a support vector machine provided by the invention;
fig. 3 is an RGB image effect diagram of the wheat lodging region identification method based on the spectral texture feature and the support vector machine provided by the present invention.
FIG. 4 is a diagram of the effect of SVM extraction results of the wheat lodging region identification method based on the spectral texture characteristics and the support vector machine.
Detailed Description
The invention is described in further detail below with reference to figures 1-4.
A wheat lodging region identification method based on spectral texture features and a support vector machine comprises the following steps:
step 101: assembling a multispectral sensor on an unmanned aerial vehicle platform;
step 102: monitoring a farmland through an unmanned aerial vehicle platform assembled with a multispectral sensor to obtain an unmanned aerial vehicle image;
step 103: according to each unmanned aerial vehicle image obtained by monitoring of the unmanned aerial vehicle platform, single-channel remote sensing images of several wave bands are overlapped based on geographic coordinate information of each pixel, and a plurality of multispectral remote sensing images containing a plurality of channels are generated.
Step 104: and (3) cutting the multispectral remote sensing image generated in the step 103 according to two categories of lodging and non-lodging by a visual estimation method to generate an image only containing a lodging region or a non-lodging region in a view field as an original data set.
Step 105: calculating texture characteristics of the first two principal component components of all multispectral remote sensing images in the original data set based on a principal component analysis method; meanwhile, the spectral reflectivity of each pixel in each multi-spectral remote sensing image in each wave band is counted; these data are packaged to generate a new data set.
Step 106: dividing the new data set into two parts, wherein 80% of the new data set is used as a sample set, and 20% of the new data set is used as a test set;
step 107: and constructing a support vector machine model, inputting the sample set into the support vector machine model for training, verifying the precision of the model by using the test set, and judging that the training is successful when the precision reaches more than 90%.
Step 108: presetting a window size according to the minimum image size in the data set; and calling a successfully trained support vector machine model, classifying each window, counting the total number of pixels of the lodging region, and converting to obtain the total area of the lodging region.
Wherein, step 105 specifically comprises:
step 1051: and carrying out principal component analysis on each remote sensing image in the original data set based on a principal component analysis method, thereby reducing data redundancy.
Principal component analysis, which is a dimension reduction method often used in image processing, is to search a similar image in a database of tens of thousands or millions or even larger when processing problems related to digital image processing, such as the query problem of commonly used images. In this case, a common method is to extract response features, such as color, texture, sift, surf, vlad, etc., from the pictures in the image library, store them, build a response data index, extract corresponding features from the image to be queried, compare the features with the image features in the database, and find the picture closest to the features.
Step 1052: calculating texture characteristics of the first two principal component components of each remote sensing image;
the texture feature is a value calculated from an image, quantifies the feature of gray level change in the region, is not based on the feature of a pixel point, needs to be statistically calculated in the region containing a plurality of pixel points, has rotation invariance and has strong resistance to noise; the method is suitable for searching texture images with large differences in thickness, density and the like.
And (3) calculating texture characteristics by adopting a gray level co-occurrence matrix method:
the gray level co-occurrence matrix is to count the probability P (i, j, delta, theta) that the pixel with the distance delta (dx2+ dy2) lambda 1/2 and the gray level j appears at the same time from the pixel with the gray level i of the image f (x, y) of N multiplied by N. Using the mathematical expression as:
P(i,j,δ,θ)＝{[(x,y),(x+dx,y+dy)]|f(x,y)＝i,f(x+dx,y+dy)＝j} (1)
step 1053: and counting the spectral reflectance values of each pixel of each remote sensing image in each wave band.
Step 108 specifically includes:
step 1081: and taking the size of the picture with the minimum image size in the data set as a window, translating the window on the image to be processed, and dividing the image to be processed into a plurality of grids.
Step 1082: and calling a trained support vector machine model to classify each grid, wherein the lodging areas and the non-lodging areas are represented by different colors.
Step 1083: and counting the total number of pixels in the lodging region, and then calculating the area S corresponding to each pixel according to a formula:
in the formula: n represents the resolution of the image; s represents the size area of the sensor; f represents the focal length of the sensor when acquiring the image; and u represents the object distance when the image is acquired, namely the height of the multispectral sensor from the ground.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The principle and the implementation manner of the present invention are explained by applying specific examples, the above description of the embodiments is only used to help understanding the method of the present invention and the core idea thereof, the described embodiments are only a part of the embodiments of the present invention, rather than all embodiments, and all other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the present invention are within the protection scope of the present invention without making creative efforts. Those not described in detail in this specification are within the skill of the art.
Claims (4)
1. A wheat lodging region identification method based on spectral texture features and a support vector machine is characterized by comprising the following steps:
step 1: assembling a multispectral sensor on an unmanned aerial vehicle platform;
step 2: monitoring a farmland through an unmanned aerial vehicle platform assembled with a multispectral sensor to obtain an unmanned aerial vehicle image;
and step 3: matching, fusing and splicing unmanned aerial vehicle images of different wave bands and different groups corresponding to the same plot based on geographic coordinate information of each pixel according to each unmanned aerial vehicle image obtained by monitoring an unmanned aerial vehicle platform to generate a plurality of multi-channel multi-spectral remote sensing images;
and 4, step 4: cutting the multispectral remote sensing image generated in the step 3 in a visual estimation mode according to lodging and non-lodging, and generating an image with only lodging areas or only non-lodging areas in a view field as an original data set;
and 5: calculating texture characteristics of the first two principal component components of all images in the original data set and spectral reflectivity of each pixel of all images in each wave band based on a principal component analysis method, and taking the calculation result as a new data set;
step 6: dividing the new data set obtained in the step 5 into two parts: 80% is sample set, 20% is test set;
and 7: constructing a support vector machine model, inputting a sample set into the support vector machine model for training, verifying the precision of the model by using a test set, and judging that the training is successful when the precision reaches more than 90%;
and 8: presetting the size of a window, calling a successfully trained support vector machine model, and judging the type of each window; and counting the number of pixels of all the lodging regions, and further solving the area of the lodging regions.
2. The wheat lodging region identification method based on spectral texture features and a support vector machine as claimed in claim 1, wherein step 5 specifically comprises:
step 51: based on a principal component analysis method, principal component analysis is carried out on each image in the original data set, so that data redundancy is reduced;
step 52: calculating texture features of the first two principal component components of each image;
step 53: and counting the spectral reflectance values of each pixel of each remote sensing image in each wave band.
3. The wheat lodging region identification method based on spectral texture features and a support vector machine as claimed in claim 2, wherein the texture features of step 52 are calculated by a gray level co-occurrence matrix method.
4. The wheat lodging region identification method based on spectral texture characteristics and a support vector machine as claimed in claim 1, wherein the step 8 specifically comprises the following steps:
step 81: taking the size of the smallest image size picture in the new data set obtained in the step 5 as the size of a window, translating the window on a target picture of the lodging region to be extracted, and dividing the target picture into a plurality of grids;
step 82: calling a successfully trained support vector machine model to classify each grid, dividing the grid into a lodging area and a non-lodging area, and representing the lodging area and the non-lodging area by different colors;
step 83: counting the total number of pixels in the lodging region, and calculating to obtain the corresponding area S of each pixel according to a formula (2):
in the formula: n represents the resolution of the image; s represents the size area of the sensor; f represents the focal length of the sensor when acquiring the image; u represents the object distance at which the image was acquired.
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| CN112597855A (en) * | 2020-12-15 | 2021-04-02 | 中国农业大学 | Crop lodging degree identification method and device |
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Cited By (3)
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| CN115830442A (en) * | 2022-11-11 | 2023-03-21 | 中国科学院空天信息创新研究院 | Machine learning-based remote sensing estimation method and system for wheat tiller density |
| CN115830442B (en) * | 2022-11-11 | 2023-08-04 | 中国科学院空天信息创新研究院 | Remote sensing estimation method and system for wheat stem tiller density based on machine learning |
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