CN114545410B - Crop lodging monitoring method based on synthetic aperture radar dual-polarized data coherence - Google Patents
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Abstract
The invention provides a crop lodging monitoring method based on synthetic aperture radar dual-polarized data coherence, which comprises the following steps: extracting backward scattering coefficients before and after lodging of an operation area; determining a backscattering variation characteristic, a coherence coefficient and an average texture characteristic; the lodging crop monitoring sensitive characteristics in the backscattering change characteristics, the coherence coefficient and the average texture characteristics are obtained through screening; and identifying lodging crops by adopting a partial least square classification method to obtain the spatial distribution information of the lodging crops in the operation area. According to the invention, primary phase images and secondary phase images provided by dual-phase dual-polarized synthetic aperture radar data are subjected to primary difference and phase unwrapping to generate coherence coefficients before and after crop lodging, the morphological changes before and after crop lodging are represented through phase differences, meanwhile, the crop lodging is monitored by fusing second-order probability texture features based on space adjacent statistics, the backward scattering change features and noise interference of the coherence coefficients are suppressed, and the precision of crop lodging monitoring results under the condition of optical data missing is improved.
Description
Technical Field
The invention relates to the technical field of agricultural informatization, in particular to a crop lodging monitoring method based on synthetic aperture radar dual-polarized data coherence.
Background
Crop lodging is a common agricultural disaster phenomenon that severely affects the normal growth and development of crops and threatens grain safety. With the rapid development of remote sensing technology, it is becoming more and more common to apply remote sensing technology to acquiring disaster information of crops. The lodging crop remote sensing monitoring method can be divided into an optical monitoring method and a radar monitoring method according to different remote sensing data sources.
The optical monitoring method mainly constructs a series of sensitive indexes capable of distinguishing lodging and unbent crops based on space-time difference rules of the lodging and unbent crops in visible light images, and the indexes mainly comprise: spectrum, texture, vegetation index and canopy structural features; and screening the characteristics by using a characteristic screening method to obtain the optimal characteristics. And then identifying the lodged crops and the unbent crops in the working area based on the most characteristic combined with a machine learning or linear regression classification method. While many characteristics and methods for lodging crop identification have been developed by existing researchers based on optical data and good results have been obtained, optical data is subject to atmospheric environmental interference, and the application of such methods is often limited; secondly, the existence of the phenomenon of 'homospectral foreign matter' reduces the identification degree of lodging and unbent crops, thereby reducing the identification precision.
The radar monitoring method generally uses a SAR (SYNTHETIC APERTURE RADAR ) technology, which is a technology for actively transmitting microwave signals and receiving the reflected wave signals to produce scattering images of an observation area. SAR remote sensing technology does not depend on solar illumination, reduces atmospheric attenuation, can work under any weather condition, and has all-weather earth observation capability. In addition, SAR remote sensing technology is sensitive to surface roughness and ground geometry, can provide multi-band, multi-polarization and multi-angle scattering information, and provides powerful technical support for monitoring crop lodging. At present, the conventional radar monitoring method is mainly based on the change of different polarized backscattering coefficients (VV, VH, HV, HH) in time sequence, but the following problems exist in the research: (1) Disaster weather is often accompanied by heavy rain, and the backscattering coefficient is sensitive to moisture, so that the scattering value is easily distorted, and the separability of lodged and unbent crops is poor; (2) The canopy structure of vegetation is comparatively complicated, and scattering characteristic's randomness is stronger, and in backscattering in-process soil background, vegetation, multiple factor stack coupling problem such as water are difficult to classify the contribution of lodging to backscattering.
Disclosure of Invention
The invention provides a crop lodging monitoring method and system based on synthetic aperture radar dual-polarized data coherence, which are used for solving the defects in the prior art.
In a first aspect, the invention provides a method for monitoring crop lodging based on synthetic aperture radar dual polarized data coherence, comprising the following steps:
Collecting crop samples with different lodging degrees in an operation area, acquiring dual-polarized SAR remote sensing data before and after lodging, and extracting backward scattering coefficients before and after lodging in the operation area based on the dual-polarized SAR remote sensing data;
Based on the backscattering coefficients, determining backscattering change characteristics, coherence coefficients and average texture characteristics before and after lodging;
screening and obtaining lodging crop monitoring sensitive characteristics in the backscattering change characteristics, the coherence coefficient and the average value texture characteristics based on a random forest-recursive characteristic elimination algorithm;
and identifying lodging crops by adopting a partial least square classification method to obtain the space distribution information of the lodging crops in the operation area.
According to the crop lodging monitoring method based on the synthetic aperture radar dual-polarized data coherence, the crop samples of different lodging degrees of the operation area are collected, dual-polarized synthetic aperture radar SAR remote sensing data before and after lodging are obtained, and the backward scattering coefficient before and after lodging of the operation area is extracted based on the dual-polarized SAR remote sensing data, and the method comprises the following steps:
Determining a pre-lodging crop sample and a post-lodging crop sample, acquiring single-view complex number Sentinel-1 SLC images in the dual-polarized SAR remote sensing data, and preprocessing the Sentinel-1 SLC images to obtain ground distance multi-view Sentinel-1 GRD images;
and respectively acquiring a VV polarized backscattering coefficient before operation area lodging, a VH polarized backscattering coefficient before operation area lodging, a VV polarized backscattering coefficient after operation area lodging and a VH polarized backscattering coefficient after operation area lodging from the Sentinel-1 GRD image.
According to the crop lodging monitoring method based on synthetic aperture radar dual-polarized data coherence, the method for determining the backward scattering change characteristics, the coherence coefficient and the average texture characteristics before and after lodging based on the backward scattering coefficient comprises the following steps:
dividing the VH polarized backscattering coefficient before lodging of the operation area by the VV polarized backscattering coefficient before lodging of the operation area to obtain a lodging front coefficient ratio;
dividing the VH polarized backscattering coefficient after the operation area is lodged by the VV polarized backscattering coefficient after the operation area is lodged to obtain a lodged coefficient ratio;
respectively differentiating the VH polarized backscattering coefficient before the operation area lodging from the VH polarized backscattering coefficient after the operation area lodging, differentiating the VV polarized backscattering coefficient before the operation area lodging from the VV polarized backscattering coefficient after the operation area lodging, and differentiating the ratio of the coefficient before lodging from the ratio of the coefficient after lodging to obtain the backscattering variation characteristic;
Constructing an average texture feature based on second-order probability statistics based on the backscattering variation feature;
and carrying out interference processing on the Sentinel-1 SLC image to obtain a VV polarization coherence coefficient and a VH polarization coherence coefficient, and representing morphological changes before and after lodging of crops based on phase difference information in the VV polarization coherence coefficient and the VH polarization coherence coefficient.
According to the crop lodging monitoring method based on synthetic aperture radar dual-polarized data coherence, the average texture feature based on second-order probability statistics is constructed based on the backscattering variation feature, and the method comprises the following steps:
Determining a two-dimensional matrix of the Sentinel-1 SLC image, and obtaining a normalized SAR image value based on a maximum value and a minimum value in the two-dimensional matrix;
based on the backscattering variation characteristics, counting a gray level co-occurrence matrix of the normalized SAR image value according to a preset direction and a preset displacement;
and calculating the average texture characteristics of the gray level co-occurrence matrix based on the optimized preset neighborhood window.
According to the crop lodging monitoring method based on the synthetic aperture radar dual-polarized data coherence, the optimized preset neighborhood window comprises the following steps:
acquiring average texture features of the pre-lodging crop samples, average texture features of the post-lodging crop samples, covariance matrices of the pre-lodging crop samples and covariance matrices of the post-lodging crop samples;
Determining the optimized preset neighborhood window by adopting a J-M distance based on the average texture feature of the pre-lodging crop sample, the average texture feature of the post-lodging crop sample, the covariance matrix of the pre-lodging crop sample and the covariance matrix of the post-lodging crop sample;
The J-M distance comprises a preset feasible region, and the preset feasible region is divided into a first feasible region, a second feasible region and a third feasible region in sequence based on the degree of the separability of the texture features.
According to the crop lodging monitoring method based on synthetic aperture radar dual-polarized data coherence, interference processing is carried out on the Sentinel-1 SLC image to obtain a VV polarization coherence coefficient and a VH polarization coherence coefficient, and the method comprises the following steps:
the phase information provided by the Sentinel-1 SLC images before and after lodging is used as a main phase and an auxiliary phase of the coherent coefficient calculation respectively;
Calculating the coordinate mapping relation between the main phase and the same-name point of the auxiliary phase, sampling the auxiliary phase image into a pixel grid of the main phase image according to the coordinate mapping relation, and enabling the same-name point of the main phase image and the auxiliary phase image to correspond to the same resolution unit on the ground;
marking the main phase value and the auxiliary phase value of the same space position in the VV polarization as a pixel pair, performing complex conjugate multiplication on the pixel pair by pixel pair to obtain an interference complex value, and calculating a primary differential interference phase pixel by pixel based on the real part and the imaginary part of the interference complex value;
Carrying out phase unwrapping on the primary differential interference phase of the VV polarization, and recovering the blurred whole-cycle phase 2k pi in the phase main value to obtain the VV polarization coherence coefficient;
marking a main phase value and a sub phase value of the same space position in the VH polarization as a pixel pair, performing complex conjugate multiplication on the pixel pair by pixel pair to obtain an interference complex value, and calculating a primary differential interference phase pixel by pixel based on a real part and an imaginary part of the interference complex value;
and carrying out phase unwrapping on the primary differential interference phase of the VH polarization, and recovering the blurred whole-cycle phase 2k pi in the phase main value to obtain the VH polarization coherence coefficient.
According to the crop lodging monitoring method based on synthetic aperture radar dual-polarized data coherence, the lodging crop monitoring sensitive characteristics in the backscattering variation characteristics, the coherence coefficient and the average texture characteristics are obtained by screening based on a random forest-recursive characteristic elimination algorithm, and the method comprises the following steps:
superposing the pre-lodging crop sample and the post-lodging crop sample to a Google Earth remote sensing image, and obtaining multi-sample data through visual interpretation;
extracting characteristic values from the multiple sample data based on the backscattering variation characteristics, the coherence coefficient and the average texture characteristics to obtain a sample point characteristic data set;
And inputting the sample point characteristic data set and the corresponding sample label into a random forest-recursive characteristic elimination filter to obtain the lodging crop monitoring sensitive characteristic.
According to the method for monitoring the lodging of the crops based on the coherence of dual-polarized data of the synthetic aperture radar, which is provided by the invention, the lodging crops are identified by adopting a partial least square classification method, and the space distribution information of the lodging crops in the operation area is obtained, and the method comprises the following steps:
performing centering treatment on the matrix of the lodging crop monitoring sensitive characteristic and a corresponding sample tag set matrix to respectively obtain a sensitive characteristic principal component scoring matrix, a sensitive characteristic principal component decomposition load matrix and a sensitive characteristic principal component error matrix, and a sample tag set principal component scoring matrix, a sample tag set principal component decomposition load matrix and a sample tag set principal component error matrix;
Performing multiple linear regression on the sensitive feature principal component score matrix and the sample label set principal component score matrix to obtain an unknown pixel sensitive feature score matrix, and obtaining an unknown pixel label based on the unknown pixel sensitive feature score matrix;
Dividing the matrix of the lodging crop monitoring sensitive characteristic and the corresponding sample label set matrix into a training set and a testing set according to a preset proportion, inputting the training set and the testing set into a partial least square regression classification model, and performing model tuning to obtain lodging crop distribution information of the operation area.
In a second aspect, the present invention also provides a crop lodging monitoring system based on synthetic aperture radar dual polarized data coherence, comprising:
The first processing module is used for collecting crop samples with different lodging degrees in an operation area, acquiring dual-polarized SAR remote sensing data before and after lodging, and extracting backward scattering coefficients before and after lodging in the operation area based on the dual-polarized SAR remote sensing data;
the second processing module is used for determining backward scattering change characteristics, coherence coefficients and average texture characteristics before and after lodging based on the backward scattering coefficients;
The third processing module is used for screening and obtaining lodging crop monitoring sensitive characteristics in the backscattering change characteristics, the coherence coefficient and the average value texture characteristics based on a random forest-recursion characteristic elimination algorithm;
and the fourth processing module is used for identifying lodging crops by adopting a partial least square classification method to obtain the space distribution information of the lodging crops in the operation area.
In a third aspect, the present invention also provides an electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any one of the synthetic aperture radar dual polarized data coherence based crop lodging monitoring methods described above when the program is executed.
In a fourth aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of a method for crop lodging monitoring based on synthetic aperture radar dual polarized data coherence as described in any of the above.
In a fifth aspect, the present invention also provides a computer program product comprising a computer program which when executed by a processor implements the steps of a method for crop lodging monitoring based on synthetic aperture radar dual polarized data coherence as defined in any one of the above.
According to the crop lodging monitoring method and system based on multi-synthetic aperture radar dual-polarization data coherence, the coherence coefficient provided by the dual-time-phase multi-polarization SAR single-view complex data is applied to crop lodging monitoring, morphological change characteristics before and after crop lodging are fully considered, second-order probability texture characteristics based on space adjacent statistics are fused, the backward scattering change characteristics and noise interference of the coherence coefficient are suppressed to a certain extent, and the precision of crop lodging monitoring results is further improved.
Drawings
In order to more clearly illustrate the invention or the technical solutions of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow diagram of a method for monitoring crop lodging based on synthetic aperture radar dual polarized data coherence;
fig. 2 is a schematic structural diagram of a crop lodging monitoring system based on synthetic aperture radar dual polarized data coherence provided by the invention;
fig. 3 is a schematic structural diagram of an electronic device provided by the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Aiming at various limitations existing in the remote sensing monitoring of lodging crops in the prior art, the invention adopts dual-polarized coherence analysis to solve the bottleneck problem of low precision of classification results caused by interference of backscattering coefficients on moisture sensitivity, strong randomness of scattering characteristics and the like in the remote sensing monitoring of lodging crops based on SAR interference principle.
Fig. 1 is a flow chart of a method for monitoring crop lodging based on synthetic aperture radar dual polarized data coherence, as shown in fig. 1, including:
Step S1, acquiring crop samples with different lodging degrees in an operation area, acquiring dual-polarized SAR remote sensing data before and after lodging, and extracting backward scattering coefficients before and after lodging in the operation area based on the dual-polarized SAR remote sensing data;
Firstly, collecting crop samples with different lodging degrees in an operation area, acquiring Sentinel-1 dual-polarized SAR remote sensing data before and after lodging in the operation area, and extracting a backward scattering coefficient thereof;
The Sentinel-1 (Sentinel No. 1) satellite is an earth observation satellite in the European space agency Gobey project (GMES), and consists of two polar orbit satellites A and B, wherein the sensors carried by the two satellites are SAR, and the satellite belongs to an active microwave remote sensing satellite. The sensor is mounted in the C-band.
Step S2, determining backward scattering change characteristics, coherence coefficients and average texture characteristics before and after lodging based on the backward scattering coefficients;
And then constructing a lodging monitoring index with strong robustness based on lodging forward and backward scattering change characteristics and coherence coefficients, wherein the lodging monitoring index comprises backward scattering change characteristics, coherence coefficients and average texture characteristics.
Step S3, screening and obtaining lodging crop monitoring sensitive characteristics in the backscattering change characteristics, the coherence coefficient and the average texture characteristics based on a random forest-recursive characteristic elimination algorithm;
And (3) inputting the backscattering change characteristics, the coherence coefficient and the texture characteristics obtained in the step (S2) into a random forest-recursive characteristic elimination screening device, and screening out lodging crop monitoring feel characteristics.
And S4, identifying lodging crops by adopting a partial least square classification method, and obtaining the space distribution information of the lodging crops in the operation area.
And finally, using a partial least square classification method to realize lodging area identification so as to obtain lodging crop distribution information of the whole operation area.
According to the invention, the coherence coefficient provided by the dual-phase dual-polarized SAR single-view complex data is applied to crop lodging monitoring, the morphological change characteristics before and after crop lodging are fully considered, the second-order probability texture characteristics based on space adjacent statistics are fused, the backward scattering change characteristics and the noise interference of the coherence coefficient are suppressed to a certain extent, and the precision of crop lodging monitoring results is further improved.
Based on the above embodiment, the method step S1 includes:
Determining a pre-lodging crop sample and a post-lodging crop sample, acquiring single-view complex number Sentinel-1 SLC images in the dual-polarized SAR remote sensing data, and preprocessing the Sentinel-1 SLC images to obtain ground distance multi-view Sentinel-1 GRD images;
and respectively acquiring a VV polarized backscattering coefficient before operation area lodging, a VH polarized backscattering coefficient before operation area lodging, a VV polarized backscattering coefficient after operation area lodging and a VH polarized backscattering coefficient after operation area lodging from the Sentinel-1 GRD image.
Specifically, after the image of Sentinel-1 SLC (Sentinel-1 Single Look Complex, sentinel number 1 single view plural number) is preprocessed, an image of Sentinel-1 GRD (Sentinel-1 Ground Range Detected, sentinel number 1 multiple view) is obtained, so that the back scattering coefficients VV nl、VHnl of the working area before lodging and the back scattering coefficients VV of the working area after lodging and the back scattering coefficients VV l、VHl of the working area after lodging can be obtained.
The SLC is a first-level product in the sentinel No. 1 satellite product, can obtain phase and amplitude information, the phase information is a function of time, and can realize distance measurement according to the phase information and the speed, and can be used for ranging and deformation observation; GRD is also a primary product in the sentinel number 1 satellite product with multi-view intensity data related to the backscattering coefficient, which can be used for soil moisture inversion.
Based on any of the above embodiments, the method step S2 includes:
dividing the VH polarized backscattering coefficient before lodging of the operation area by the VV polarized backscattering coefficient before lodging of the operation area to obtain a lodging front coefficient ratio;
dividing the VH polarized backscattering coefficient after the operation area is lodged by the VV polarized backscattering coefficient after the operation area is lodged to obtain a lodged coefficient ratio;
respectively differentiating the VH polarized backscattering coefficient before the operation area lodging from the VH polarized backscattering coefficient after the operation area lodging, differentiating the VV polarized backscattering coefficient before the operation area lodging from the VV polarized backscattering coefficient after the operation area lodging, and differentiating the ratio of the coefficient before lodging from the ratio of the coefficient after lodging to obtain the backscattering variation characteristic;
Constructing an average texture feature based on second-order probability statistics based on the backscattering variation feature;
And carrying out interference treatment on the Sentinel-1 SLC image to obtain a VV polarization interference pattern and a VH polarization interference pattern, and estimating and obtaining the coherence coefficient based on the VV polarization interference pattern and the VH polarization interference pattern.
The construction of the average texture feature based on the second-order probability statistics based on the backscattering variation feature comprises the following steps:
Determining a two-dimensional matrix of the Sentinel-1 SLC image, and obtaining a normalized SAR image value based on a maximum value and a minimum value in the two-dimensional matrix;
based on the backscattering variation characteristics, counting a gray level co-occurrence matrix of the normalized SAR image value according to a preset direction and a preset displacement;
and calculating the average texture characteristics of the gray level co-occurrence matrix based on the optimized preset neighborhood window.
Wherein, the optimized preset neighborhood window comprises:
acquiring average texture features of the pre-lodging crop samples, average texture features of the post-lodging crop samples, covariance matrices of the pre-lodging crop samples and covariance matrices of the post-lodging crop samples;
Determining the optimized preset neighborhood window by adopting a J-M distance based on the average texture feature of the pre-lodging crop sample, the average texture feature of the post-lodging crop sample, the covariance matrix of the pre-lodging crop sample and the covariance matrix of the post-lodging crop sample;
The J-M distance comprises a preset feasible region, and the preset feasible region is divided into a first feasible region, a second feasible region and a third feasible region in sequence based on the degree of the separability of the texture features.
The interference processing is performed on the Sentinel-1SLC image to obtain a VV polarization coherence coefficient and a VH polarization coherence coefficient, including:
the phase information provided by the Sentinel-1 SLC images before and after lodging is used as a main phase and an auxiliary phase of the coherent coefficient calculation respectively;
Calculating the coordinate mapping relation between the main phase and the same-name point of the auxiliary phase, sampling the auxiliary phase image into a pixel grid of the main phase image according to the coordinate mapping relation, and enabling the same-name point of the main phase image and the auxiliary phase image to correspond to the same resolution unit on the ground;
marking the main phase value and the auxiliary phase value of the same space position in the VV polarization as a pixel pair, performing complex conjugate multiplication on the pixel pair by pixel pair to obtain an interference complex value, and calculating a primary differential interference phase pixel by pixel based on the real part and the imaginary part of the interference complex value;
Carrying out phase unwrapping on the primary differential interference phase of the VV polarization, and recovering the blurred whole-cycle phase 2k pi in the phase main value to obtain the VV polarization coherence coefficient;
marking a main phase value and a sub phase value of the same space position in the VH polarization as a pixel pair, performing complex conjugate multiplication on the pixel pair by pixel pair to obtain an interference complex value, and calculating a primary differential interference phase pixel by pixel based on a real part and an imaginary part of the interference complex value;
and carrying out phase unwrapping on the primary differential interference phase of the VH polarization, and recovering the blurred whole-cycle phase 2k pi in the phase main value to obtain the VH polarization coherence coefficient.
Specifically, after obtaining the pre-lodging VV and VH polarized backscattering coefficients VV nl、VHnl and the post-lodging backscattering coefficients VV l、VHl of the operation area, the two polarized data are respectively subjected to quotient calculation, and the characteristics of the backscattering dual polarization Ratio nl、Ratiol before and after lodging are obtained.
The VV and VH polarized backscattering coefficients of the Sentinel-1 GRD images before and after lodging are calculated to obtain the Ratio nl、Ratiol characteristics before and after lodging respectively:
The method for constructing the backscattering variation characteristics (VV var、VHvar、Ratiovar) of the crops before and after lodging by respectively differentiating the polarized backscattering coefficients VV nl、VHnl、Rationl of the pre-lodging image with the corresponding polarized characteristics (including VV l、VHl、Ratiol) in the post-lodging image comprises:
VHvar＝VHnl-VHl
VVvar＝VVnl-VVl
Ratiovar＝Rationl-Ratiol
Wherein, VV var、VHvar、Ratiovar is the backscattering coefficient variation characteristic, VV nl and VH nl are the VV and VH polarized backscattering coefficients of the Sentinel-1 GRD data before lodging, respectively, and Ratio nl is the Ratio of VV nl to VH nl; VV l and VH l are the VV and VH polarized backscattering coefficients of the post-lodging Sentinel-1 GRD data, respectively, and Ratio l is the Ratio of VV l to VH l.
Further, a glcm_mean (Gray-level Co-occurrence Matrix _mean, gray-level Co-m) based on the second-order probability statistics is constructed from the three feature maps (VV var、VHvar、Ratiovar):
Gray domain map g xy for three backscatter change features s xy for any position (x, y) in the image:
Wherein s min and s min are the minimum and maximum values in the two-dimensional matrix of the whole SAR image, and g xy is the value of the normalized SAR image.
Here, based on the mapped backscattering variation characteristics, a probability matrix from the i-value pixel position to the j-value pixel position of the image under a certain calculated preset direction θ and a preset displacement d is counted, that is, a gray level co-occurrence matrix p (i, j, d, θ) under a certain preset neighborhood window is counted:
wherein, # represents the number of times of occurrence of the image value in a certain window, the distribution range of the pixel intensity is [ i, j ], (x 1,y1) and (x 2,y2) are respectively the first and second pixel positions adjacent to each other in the θ direction.
According to the gray level co-occurrence matrix, calculating GLCM_mean characteristics under a certain neighborhood space window:
where N is the gray level of the image.
It should be noted that, the present invention optimizes the direction angle and window size parameters required for calculating the glcm_mean feature, and adjusts the individual parameters one by one while keeping other parameters constant, and determines the optimal value of the parameter according to the J-M (Jeffries-Matusita) distance JM ij between the lodged crop and the non-lodged crop sample in the feature map, specifically:
JMij＝2*(1-e-B)
Wherein m nl and m l are the average values of the texture features of the unbent crop and the lodged crop sample, respectively, and Σx nl and Σx l are the covariance matrices of the texture features of the unbent and lodged crop samples, respectively.
The JM ij has a feasible region interval of [0,2], and the invention is defined as follows: when JM ij is less than 1, the feature of the parameter configuration is poor in separability, and parameter searching needs to be continued; when JM ij is less than or equal to 1 and less than 1.8, the texture features obtained by the current parameters are indicated to have certain distinction, and parameter searching still needs to be continued; when JM ij is less than or equal to 1.8 and less than or equal to 2, the texture features of the current parameter configuration are proved to have higher differentiation, and the current parameter can be determined to be optimal.
In addition, the invention also carries out interference processing based on the Sentinel-1 SLC data to respectively form interference patterns of VV and VH polarization so as to estimate the coherence coefficient, and comprises the following steps:
The coherence coefficient gamma vv、γvh of the polarization of the working areas VV and VH is obtained by estimating the spatial average of all pixel numbers L of a single SAR image:
Wherein i is the number of pixels in the image, VV nl and VH nl are the VV and VH polarizations of the Sentinel-1 SLC image before lodging, and VV l and VH l are the VV and VH polarizations of the Sentinel-1 SLC image after lodging, respectively.
Since the coherence coefficient provides amplitude information and phase information of the SAR signal for lodging crop monitoring as well as a large amount of interference phase noise, this can reduce the spatial continuity of the lodging crop monitoring results, similar to random noise in the backscattering coefficient. Therefore, the invention integrates the second-order probability texture features based on space adjacent feature statistics on the basis of comprehensively considering the backscattering change information and the coherence coefficient, thereby achieving the purposes of suppressing noise interference and improving the precision and the space continuity of the lodging monitoring result.
Based on any of the above embodiments, the method step S3 includes:
superposing the pre-lodging crop sample and the post-lodging crop sample to a Google Earth remote sensing image, and obtaining multi-sample data through visual interpretation;
extracting characteristic values from the multiple sample data based on the backscattering variation characteristics, the coherence coefficient and the average texture characteristics to obtain a sample point characteristic data set;
And inputting the sample point characteristic data set and the corresponding sample label into a random forest-recursive characteristic elimination filter to obtain the lodging crop monitoring sensitive characteristic.
Specifically, the collected lodging crop sample points and the non-lodging crop sample points are overlapped into Google Earth remote sensing images with high spatial resolution, and more similar samples are obtained through visual interpretation.
And (3) carrying out characteristic value extraction operation on the plurality of samples to obtain a VV var、VHvar、Ratiovar、GLCM_Mean、γvv、γvh characteristic data set of the spatial position of the sample point, then inputting the sample label, usually 0 and 1, and the characteristic data set into a random forest-recursive characteristic elimination filter, gradually eliminating the characteristics which are not suitable for monitoring the lodging crops, and reserving the monitoring sensitive characteristics of the lodging crops.
Based on any of the above embodiments, the method step S4 includes:
performing centering treatment on the matrix of the lodging crop monitoring sensitive characteristic and a corresponding sample tag set matrix to respectively obtain a sensitive characteristic principal component scoring matrix, a sensitive characteristic principal component decomposition load matrix and a sensitive characteristic principal component error matrix, and a sample tag set principal component scoring matrix, a sample tag set principal component decomposition load matrix and a sample tag set principal component error matrix;
Performing multiple linear regression on the sensitive feature principal component score matrix and the sample label set principal component score matrix to obtain an unknown pixel sensitive feature score matrix, and obtaining an unknown pixel label based on the unknown pixel sensitive feature score matrix;
Dividing the matrix of the lodging crop monitoring sensitive characteristic and the corresponding sample label set matrix into a training set and a testing set according to a preset proportion, inputting the training set and the testing set into a partial least square regression classification model, and performing model tuning to obtain lodging crop distribution information of the operation area.
Specifically, the invention encodes a sample label set and sensitive features into a matrix, inputs an optimal partial least square classification model, judges labels of unknown pixels in a feature map one by one, and thus obtains a monitoring result of lodging crops in a working area, and the calculation process comprises the following steps:
after the centers of the sensitive characteristic matrix X and the sample label set matrix Y are processed, decomposing the X and the Y to obtain principal component score matrices T and U:
X＝TP+E
Y＝UQ+F
Multiple linear regression was performed on T and U:
U＝TB
B＝(XTX)-1XTY
Calculating a scoring matrix T ' of a sensitive feature matrix X ' of the unknown pixel according to the matrix P, thereby predicting a label Y ' of the unknown pixel:
Y′＝T′BQ
Wherein T and U are respectively a X, Y principal component scoring matrix, P and Q are respectively a principal component decomposed load matrix, and E and F are respectively an error matrix.
It should be noted that, the invention divides the sample label and the corresponding sensitive characteristic data set into a training set and a testing set according to the proportion of 3:1, inputs a partial least square regression classification model, verifies the precision of the classification model in the training set and the testing set, and repeatedly adjusts the classification model parameters until the precision in the training set and the testing set is optimal.
According to the invention, through the coherence analysis of the dual-time-phase SAR remote sensing data, the coherence coefficient of the lodging crop in the working area is constructed, the lodging crop is monitored by combining the backward scattering change characteristic and the texture characteristic, and the phase change information of the SAR remote sensing data is applied to the lodging crop identification, so that the deformation characteristic of the crop can be provided for the lodging crop, and the accuracy of the monitoring result is further improved.
The technical scheme of the invention is further described below by taking a county under a certain city as an operation area for remote sensing monitoring of lodging crops.
The county features are high in southeast and low in northwest, the south is mainly low mountain hills, the middle is mainly plain, the crop planting area in the county is up to 24.2 ten thousand hectares, and the corn planting is mainly. The crops in the region are subjected to large-area lodging under the influence of typhoons on 9 th and 4 th of 2020. The data were obtained as follows: sentinel-1 SLC images of 8, 23 and 16 in 2020 were obtained from the European air office official website, and the lodging crop samples were collected at 9, 11-16 in 2020.
Preprocessing a Sentinel-1 SLC image to obtain a ground-distance multi-view Sentinel-1 GRD image, and dividing the two polarized data respectively based on a VV before lodging and a VH polarized backscattering coefficient VV nl、VHnl and a VV after lodging and a VH polarized backscattering coefficient VV l、VHl of an operation area to obtain Ratio l、Rationl characteristics. In this case, the coordinate information of the collected sample is encoded into a vector point with a space geographic coordinate, and is loaded into arcGIS software, and more similar samples are obtained based on visual interpretation by superposition with a Google Earth remote sensing image with high spatial resolution.
The polarized backscattering coefficients VV nl、VHnl、Rationl of the pre-lodging image are respectively differenced with the corresponding polarized features (VV l、VHl、Ratiol) in the post-lodging image, and finally the backscattering variation features of the 3 crops before and after lodging are obtained, which comprises: VV var、VHvar、Ratiovar, the process is implemented based on the GDAL library of Python.
In this case, the average texture (glcm_mean) feature based on second order probability statistics is constructed using VV var、VHvar、Ratiovar features, which is implemented by Python calls GDAL and Numpy libraries. And determining optimal parameters according to J-M distances of lodged and unbent crops in the texture characteristics under the settings of different window sizes and direction parameters, and finally obtaining 3 GLCM_mean characteristics corresponding to the VV var、VHvar、Ratiovar.
And carrying out interference processing on the Sentinel-1 SLC data to construct coherent variation characteristics of two polarization modes of VV and VH. The process is realized by SNAP software, and finally 2 coherence features of VV and VH are obtained.
Further, extracting the characteristic value of the collected sample to obtain a VV var、VHvar、Ratiovar、GLCM_Mean、γvv、γvh characteristic data set of the spatial position of the sample point. And (3) inputting the sample labels and the characteristic data sets into a random forest-recursive characteristic elimination filter, gradually eliminating the characteristics which are not suitable for monitoring lodging crops, and reserving sensitive characteristics.
The bias least square classification method is used based on the python machine learning library scikit-learn, and lodging area identification is achieved, so that distribution information of lodging crops in the whole operation area is obtained.
And encoding the sample label set and the sensitive characteristic into a matrix, inputting an optimal least square classification model, and distinguishing the labels of unknown pixels in the characteristic diagram pixel by pixel, thereby obtaining the monitoring result of the lodging crop in the working area.
The crop lodging monitoring method based on the synthetic aperture radar dual-polarized data coherence comprises the steps of data preparation, feature extraction and lodging area identification, wherein the lodging crop classification is converted into an image identification process of SAR polarization features, and the coherence coefficient, the backward scattering change information and the texture features are applied to the lodging crop classification, so that the monitoring method can fully consider the crop morphology and the scattered signal time-varying features and simultaneously reduce noise disturbance of SAR signals, and the accuracy of classification results is improved.
The crop lodging monitoring system based on the synthetic aperture radar dual-polarized data coherence, which is provided by the invention, is described below, and the crop lodging monitoring system based on the synthetic aperture radar dual-polarized data coherence, which is described below, and the crop lodging monitoring method based on the synthetic aperture radar dual-polarized data coherence, which is described above, can be correspondingly referred to each other.
Fig. 2 is a schematic structural diagram of a crop lodging monitoring system based on dual-polarized data coherence of a synthetic aperture radar, as shown in fig. 2, including: a first processing module 21, a second processing module 22, a third processing module 23, and a fourth processing module 24, wherein:
The first processing module 21 is used for collecting crop samples with different lodging degrees in an operation area, acquiring dual-polarized SAR remote sensing data before and after lodging, and extracting backscattering coefficients before and after lodging in the operation area based on the dual-polarized SAR remote sensing data; the second processing module 22 is configured to determine a backscattering variation characteristic, a coherence coefficient, and an average texture characteristic before and after lodging based on the backscattering coefficient; the third processing module 23 is configured to screen and obtain lodging crop monitoring sensitive features in the backscattering variation feature, the coherence coefficient and the average texture feature based on a random forest-recursive feature elimination algorithm; the fourth processing module 24 is configured to identify the lodging crop using a partial least squares classification method, and obtain the spatial distribution information of the lodging crop in the operation area.
According to the invention, the coherence coefficient provided by the dual-phase dual-polarized SAR single-view complex data is applied to crop lodging monitoring, the morphological change characteristics before and after crop lodging are fully considered, the second-order probability texture characteristics based on space adjacent statistics are fused, the backward scattering change characteristics and the noise interference of the coherence coefficient are suppressed to a certain extent, and the precision of crop lodging monitoring results is further improved.
Fig. 3 illustrates a physical schematic diagram of an electronic device, as shown in fig. 3, where the electronic device may include: processor 310, communication interface (Communications Interface) 320, memory 330 and communication bus 340, wherein processor 310, communication interface 320 and memory 330 communicate with each other via communication bus 340. Processor 310 may invoke logic instructions in memory 330 to perform a method for crop lodging monitoring based on synthetic aperture radar dual polarized data coherence, the method comprising: collecting crop samples with different lodging degrees in an operation area, acquiring dual-polarized SAR remote sensing data before and after lodging, and extracting backward scattering coefficients before and after lodging in the operation area based on the dual-polarized SAR remote sensing data; based on the backscattering coefficients, determining backscattering change characteristics, coherence coefficients and average texture characteristics before and after lodging; screening and obtaining lodging crop monitoring sensitive characteristics in the backscattering change characteristics, the coherence coefficient and the average value texture characteristics based on a random forest-recursive characteristic elimination algorithm; and identifying lodging crops by adopting a partial least square classification method to obtain the space distribution information of the lodging crops in the operation area.
Further, the logic instructions in the memory 330 described above may be implemented in the form of software functional units and may be stored in a computer-readable storage medium when sold or used as a stand-alone product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a read-only memory (ROM), a random access memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, the computer program product comprising a computer program, the computer program being storable on a non-transitory computer readable storage medium, the computer program, when executed by a processor, being capable of executing the method for monitoring crop lodging based on dual polarized data coherence of synthetic aperture radar provided by the above methods, the method comprising: collecting crop samples with different lodging degrees in an operation area, acquiring dual-polarized SAR remote sensing data before and after lodging, and extracting backward scattering coefficients before and after lodging in the operation area based on the dual-polarized SAR remote sensing data; based on the backscattering coefficients, determining backscattering change characteristics, coherence coefficients and average texture characteristics before and after lodging; screening and obtaining lodging crop monitoring sensitive characteristics in the backscattering change characteristics, the coherence coefficient and the average value texture characteristics based on a random forest-recursive characteristic elimination algorithm; and identifying lodging crops by adopting a partial least square classification method to obtain the space distribution information of the lodging crops in the operation area.
In yet another aspect, the present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, is implemented to perform the method for monitoring crop lodging based on synthetic aperture radar dual polarized data coherence provided by the above methods, the method comprising: collecting crop samples with different lodging degrees in an operation area, acquiring dual-polarized SAR remote sensing data before and after lodging, and extracting backward scattering coefficients before and after lodging in the operation area based on the dual-polarized SAR remote sensing data; based on the backscattering coefficients, determining backscattering change characteristics, coherence coefficients and average texture characteristics before and after lodging; screening and obtaining lodging crop monitoring sensitive characteristics in the backscattering change characteristics, the coherence coefficient and the average value texture characteristics based on a random forest-recursive characteristic elimination algorithm; and identifying lodging crops by adopting a partial least square classification method to obtain the space distribution information of the lodging crops in the operation area.
The apparatus embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
From the above description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course may be implemented by means of hardware. Based on this understanding, the foregoing technical solution may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the respective embodiments or some parts of the embodiments.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (5)
1. The crop lodging monitoring method based on the synthetic aperture radar dual-polarized data coherence is characterized by comprising the following steps:
Collecting crop samples with different lodging degrees in an operation area, acquiring dual-polarized SAR remote sensing data before and after lodging, and extracting backward scattering coefficients before and after lodging in the operation area based on the dual-polarized SAR remote sensing data;
Based on the backscattering coefficients, determining backscattering change characteristics, coherence coefficients and average texture characteristics before and after lodging;
screening and obtaining lodging crop monitoring sensitive characteristics in the backscattering change characteristics, the coherence coefficient and the average value texture characteristics based on a random forest-recursive characteristic elimination algorithm;
identifying lodging crops by adopting a partial least square classification method to obtain lodging crop space distribution information of the operation area;
The method for acquiring crop samples with different lodging degrees in an operation area, acquiring dual-polarized SAR remote sensing data before and after lodging, extracting a back scattering coefficient before and after lodging in the operation area based on the dual-polarized SAR remote sensing data comprises the following steps:
Determining a pre-lodging crop sample and a post-lodging crop sample, acquiring single-view complex Sentinel-1SLC images in the dual-polarized SAR remote sensing data, and preprocessing the Sentinel-1SLC images to obtain ground distance multi-view Sentinel-1GRD images;
The VV polarized backscattering coefficient before operation area lodging, the VH polarized backscattering coefficient before operation area lodging, the VV polarized backscattering coefficient after operation area lodging and the VH polarized backscattering coefficient after operation area lodging are respectively obtained from the Sentinel-1GRD image;
The determining the backscattering variation characteristics, the coherence coefficient and the average texture characteristics before and after lodging based on the backscattering coefficients comprises the following steps:
dividing the VH polarized backscattering coefficient before lodging of the operation area by the VV polarized backscattering coefficient before lodging of the operation area to obtain a lodging front coefficient ratio;
dividing the VH polarized backscattering coefficient after the operation area is lodged by the VV polarized backscattering coefficient after the operation area is lodged to obtain a lodged coefficient ratio;
respectively differentiating the VH polarized backscattering coefficient before the operation area lodging from the VH polarized backscattering coefficient after the operation area lodging, differentiating the VV polarized backscattering coefficient before the operation area lodging from the VV polarized backscattering coefficient after the operation area lodging, and differentiating the ratio of the coefficient before lodging from the ratio of the coefficient after lodging to obtain the backscattering variation characteristic;
Constructing an average texture feature based on second-order probability statistics based on the backscattering variation feature;
Carrying out interference processing on the Sentinel-1SLC image to obtain a VV polarization coherence coefficient and a VH polarization coherence coefficient, and representing the morphological change before and after lodging of crops based on phase difference information in the VV polarization coherence coefficient and the VH polarization coherence coefficient;
The constructing an average texture feature based on second-order probability statistics based on the backscattering variation feature comprises the following steps:
Determining a two-dimensional matrix of the Sentinel-1SLC image, and obtaining a normalized SAR image value based on a maximum value and a minimum value in the two-dimensional matrix;
based on the backscattering variation characteristics, counting a gray level co-occurrence matrix of the normalized SAR image value according to a preset direction and a preset displacement;
calculating the average texture characteristics of the gray level co-occurrence matrix based on the optimized preset neighborhood window;
The optimized preset neighborhood window comprises the following steps:
acquiring average texture features of the pre-lodging crop samples, average texture features of the post-lodging crop samples, covariance matrices of the pre-lodging crop samples and covariance matrices of the post-lodging crop samples;
Determining the optimized preset neighborhood window by adopting a J-M distance based on the average texture feature of the pre-lodging crop sample, the average texture feature of the post-lodging crop sample, the covariance matrix of the pre-lodging crop sample and the covariance matrix of the post-lodging crop sample;
The J-M distance comprises a preset feasible region, and the preset feasible region is divided into a first feasible region, a second feasible region and a third feasible region in sequence based on the degree of the separability of the texture features.
2. The method for monitoring crop lodging based on dual-polarized data coherence of synthetic aperture radar according to claim 1, wherein the performing interference processing on the Sentinel-1SLC image to obtain a VV polarization coherence coefficient and a VH polarization coherence coefficient comprises:
the phase information provided by the Sentinel-1SLC images before and after lodging is used as a main phase and an auxiliary phase of the coherent coefficient calculation respectively;
Calculating the coordinate mapping relation between the main phase and the same-name point of the auxiliary phase, sampling the auxiliary phase image into a pixel grid of the main phase image according to the coordinate mapping relation, and enabling the same-name point of the main phase image and the auxiliary phase image to correspond to the same resolution unit on the ground;
marking the main phase value and the auxiliary phase value of the same space position in the VV polarization as a pixel pair, performing complex conjugate multiplication on the pixel pair by pixel pair to obtain an interference complex value, and calculating a primary differential interference phase pixel by pixel based on the real part and the imaginary part of the interference complex value;
Carrying out phase unwrapping on the primary differential interference phase of the VV polarization, and recovering the blurred whole-cycle phase 2k pi in the phase main value to obtain the VV polarization coherence coefficient;
marking a main phase value and a sub phase value of the same space position in the VH polarization as a pixel pair, performing complex conjugate multiplication on the pixel pair by pixel pair to obtain an interference complex value, and calculating a primary differential interference phase pixel by pixel based on a real part and an imaginary part of the interference complex value;
and carrying out phase unwrapping on the primary differential interference phase of the VH polarization, and recovering the blurred whole-cycle phase 2k pi in the phase main value to obtain the VH polarization coherence coefficient.
3. The method for monitoring the lodging of crops based on the coherence of dual-polarized data of the synthetic aperture radar according to claim 1, wherein the method for identifying the lodging crops by adopting the partial least squares classification method to obtain the spatial distribution information of the lodging crops in the operation area comprises the following steps:
performing centering treatment on the matrix of the lodging crop monitoring sensitive characteristic and a corresponding sample tag set matrix to respectively obtain a sensitive characteristic principal component scoring matrix, a sensitive characteristic principal component decomposition load matrix and a sensitive characteristic principal component error matrix, and a sample tag set principal component scoring matrix, a sample tag set principal component decomposition load matrix and a sample tag set principal component error matrix;
Performing multiple linear regression on the sensitive feature principal component score matrix and the sample label set principal component score matrix to obtain an unknown pixel sensitive feature score matrix, and obtaining an unknown pixel label based on the unknown pixel sensitive feature score matrix;
dividing the matrix of the lodging crop monitoring sensitive characteristic and the corresponding sample label set matrix into a training set and a testing set according to a preset proportion, inputting the training set and the testing set into a partial least square regression classification model, and performing model tuning to obtain lodging crop space distribution information of the operation area.
4. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the synthetic aperture radar dual polarized data coherence based crop lodging monitoring method according to any of claims 1 to 3 when the program is executed.
5. A non-transitory computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of a synthetic aperture radar dual polarized data coherence based crop lodging monitoring method according to any of claims 1 to 3.
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