CN113450353B - Method and device for optimizing precision of leaf area index - Google Patents
Method and device for optimizing precision of leaf area index Download PDFInfo
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Abstract
The invention provides a method and a device for optimizing leaf area index precision, which relate to the technical field of data processing and comprise the following steps: acquiring a plurality of initial leaf area index images and quality control data of a region to be optimized, wherein the plurality of initial leaf area index images are used for representing leaf area indexes of the region to be optimized at different times; based on the quality control data, correcting the multiple initial leaf area index images to obtain multiple target leaf area index images; constructing a leaf area index set of pixel points in a region to be optimized based on a plurality of target leaf area index images, wherein the leaf area index set comprises leaf area indexes of the pixel points in the region to be optimized in the target leaf area index images; the Whittaker Smoother algorithm is utilized to smooth the leaf area index set of the pixel points in the area to be optimized to obtain the leaf area index smooth processing result of the area to be optimized, and the technical problems of low error and low efficiency of the existing leaf area index precision optimization method are solved.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a device for optimizing leaf area index precision.
Background
At present, a plurality of leaf area index measuring methods exist, remote sensing satellite remote sensing provides an effective way for large-range research of large-area vegetation, and MODIS Leaf Area Index (LAI) is one of related satellite remote sensing products. However, in the process of obtaining remote sensing data, the obtained data is influenced by various factors, such as cloud, atmospheric pollution and the like, so that a large amount of noise exists in the obtained data, the accuracy of MODIS Leaf Area Index (LAI) data is influenced, and the practical value of the remote sensing image of the leaf area index is reduced. Therefore, how to denoise the remote sensing image to improve the quality of the remote sensing data is an important research direction.
The MODIS remote sensing data noise reduction algorithm mainly comprises two types: one is noise reduction for single remote sensing images; the other is to perform noise reduction processing in the time dimension.
The first common method for single remote sensing image denoising comprises mean filtering, median filtering, wavelet transformation, adaptive filtering, neural network and the like, and the methods are all used for processing a single remote sensing image and do not consider the problem of time dimension. According to the traditional method, a relation model of remote sensing data and observation data is established by utilizing ground observation data according to a remote sensing mechanism, parameters are adjusted to enable an inversion result to be consistent with the ground data, then an inversion model is established to obtain a larger-range inversion result, the difficulty of the method lies in the establishment of the model, and the model is also an important source of result errors.
And the second type carries out noise reduction processing on the multi-temporal remote sensing image in a time dimension. The modern image signal processing technology is applied to the quality improvement of remote sensing data products. The foreign common method comprises the following steps: BISE, MVC, TWO, Savitzky-Golay filtering, wavelet transforms, etc. These model algorithms are usually limited to be complicated and have many limitations, the fitting result deviates from the original data, and for the large data volume of the remote sensing image, the calculation is completed with great calculation force and long time.
No effective solution has been proposed to the above problems.
Disclosure of Invention
In view of this, the present invention provides a method and an apparatus for optimizing a precision of a leaf area index, so as to alleviate technical problems of low error and efficiency of an existing method for optimizing a precision of a leaf area index.
In a first aspect, an embodiment of the present invention provides a method for optimizing a precision of a leaf area index, including: acquiring a plurality of initial leaf area index images and quality control data of a region to be optimized, wherein the initial leaf area index images are used for representing leaf area indexes of the region to be optimized at different time, and the quality control data is used for representing the quality of each pixel point in the initial leaf area index images; based on the quality control data, correcting the plurality of initial leaf area index images to obtain a plurality of target leaf area index images; constructing a leaf area index set of pixel points in the region to be optimized based on the target leaf area index images, wherein the leaf area index set comprises leaf area indexes of the pixel points in the region to be optimized in the target leaf area index images; and smoothing the leaf area index set of the pixel points in the region to be optimized by using the Whittaker Smoother algorithm to obtain a leaf area index smoothing result of the region to be optimized.
Further, based on the quality control data, correcting the plurality of initial leaf area index images to obtain a plurality of target leaf area index images, including: determining first target pixel points in a plurality of initial leaf area index images, wherein the first target pixel points are pixel points in a preset effective range in the leaf area index images; setting the leaf area index of a second target pixel point in a plurality of initial leaf area index images as a null value to obtain a plurality of intermediate leaf area index images, wherein the second target pixel point is a pixel point in the initial leaf area index images except the first target pixel point; determining a first final pixel point in a plurality of intermediate leaf area index images based on the quality control data, wherein the first final pixel point is a pixel point of which the quality control data is 0 in the intermediate leaf area index images; setting the leaf area index of a second final pixel point in the intermediate leaf area index images as a null value to obtain a plurality of target leaf area index images, wherein the second final pixel point is a pixel point except the first final pixel point in the intermediate leaf area index images.
Further, smoothing the set of leaf area indexes of the pixel points in the region to be optimized by using the Whittaker Smoother algorithm to obtain a result of smoothing the leaf area indexes of the region to be optimized, including: determining the number of non-null values contained in the leaf area index set of the pixel points; if the number of the non-null values is larger than or equal to a preset threshold value, processing the null values based on a linear interpolation algorithm to obtain a first leaf area index set of pixel points; based on the Whittaker Smoother algorithm, performing first smoothing processing on a first leaf area index set of pixel points to obtain a first calculation result of the pixel points; determining an abnormal value in a first leaf area index set of the pixel points based on the first calculation result; removing the abnormal value from the first leaf area index set of the pixel point, and carrying out interpolation processing on a null value in the first leaf area index set of the pixel point to obtain a second leaf area index set of the pixel point; and performing second smoothing treatment on a second leaf area index set of the pixel points based on the Whittaker Smoother algorithm to obtain a leaf area index smoothing treatment result of the region to be optimized.
Further, based on the Whittaker Smoother algorithm, performing second smoothing processing on a second leaf area index set of the pixel points to obtain a leaf area index smoothing processing result of the region to be optimized, including: an obtaining step of obtaining a preset smooth result evaluation parameter and a preset weight value; a first processing step, based on the Whittaker Smoother algorithm, of performing first smoothing processing on a second leaf area index set of pixel points to obtain a second calculation result of the pixel points; a second processing step of calculating a smoothing result of the pixel point based on a second calculation result of the pixel point; if the smoothing result of the pixel point is larger than the preset smoothing result evaluation parameter, determining the smoothing result of the pixel point as a leaf area index smoothing processing result of the area to be optimized; if the smoothing result of the pixel point is smaller than or equal to the preset smoothing result evaluation parameter, determining the smoothing result of the pixel point as the preset smoothing result evaluation parameter, calculating a third leaf area index set of the pixel point based on the smoothing result of the pixel point, determining the third leaf area index set of the pixel point as the leaf area index set of the pixel point, repeatedly executing the first processing step and the second processing step until the repeated execution times is greater than the preset times, or the smoothing result of the pixel point is greater than the preset smoothing result evaluation parameter, and determining the smoothing result of the pixel point obtained by repeatedly executing the preset times or the smoothing result of the pixel point greater than the preset smoothing result evaluation parameter as the leaf area index smoothing processing result of the area to be optimized.
In a second aspect, an embodiment of the present invention further provides an apparatus for optimizing precision of a leaf area index, including: the device comprises an acquisition unit, a correction unit, a construction unit and a processing unit, wherein the acquisition unit is used for acquiring a plurality of initial leaf area index images and quality control data of a region to be optimized, the initial leaf area index images are used for representing leaf area indexes of the region to be optimized at different time, and the quality control data are used for representing the quality of each pixel point in the initial leaf area index images; the correcting unit is used for correcting the plurality of initial leaf area index images based on the quality control data to obtain a plurality of target leaf area index images; the constructing unit is configured to construct a leaf area index set of pixel points in the region to be optimized based on the target leaf area index images, where the leaf area index set includes leaf area indexes of the pixel points in the region to be optimized in the target leaf area index images; and the processing unit is used for smoothing the leaf area index set of the pixel points in the region to be optimized by using the Whittaker Smoother algorithm to obtain a leaf area index smoothing result of the region to be optimized.
Further, the correction unit is configured to: determining first target pixel points in a plurality of initial leaf area index images, wherein the first target pixel points are pixel points in a preset effective range in the leaf area index images; setting the leaf area index of a second target pixel point in a plurality of initial leaf area index images as a null value to obtain a plurality of intermediate leaf area index images, wherein the second target pixel point is a pixel point in the initial leaf area index images except the first target pixel point; determining a first final pixel point in a plurality of intermediate leaf area index images based on the quality control data, wherein the first final pixel point is a pixel point of which the quality control data is 0 in the intermediate leaf area index images; setting the leaf area index of a second final pixel point in the intermediate leaf area index images as a null value to obtain a plurality of target leaf area index images, wherein the second final pixel point is a pixel point except the first final pixel point in the intermediate leaf area index images.
Further, the processing unit is configured to: determining the number of non-null values contained in the leaf area index set of the pixel points; if the number of the non-null values is larger than or equal to a preset threshold value, processing the null values based on a linear interpolation algorithm to obtain a first leaf area index set of pixel points; based on the Whittaker Smoother algorithm, performing first smoothing processing on a first leaf area index set of pixel points to obtain a first calculation result of the pixel points; determining an abnormal value in a first leaf area index set of the pixel points based on the first calculation result; removing the abnormal value from the first leaf area index set of the pixel point, and carrying out interpolation processing on a null value in the first leaf area index set of the pixel point to obtain a second leaf area index set of the pixel point; and performing second smoothing treatment on a second leaf area index set of the pixel points based on the Whittaker Smoother algorithm to obtain a leaf area index smoothing treatment result of the region to be optimized.
Further, the processing unit is further configured to perform the steps of: an obtaining step of obtaining a preset smooth result evaluation parameter and a preset weight value; a first processing step, based on the Whittaker Smoother algorithm, of performing first smoothing processing on a second leaf area index set of pixel points to obtain a second calculation result of the pixel points; a second processing step of calculating a smoothing result of the pixel point based on a second calculation result of the pixel point; if the smoothing result of the pixel point is larger than the preset smoothing result evaluation parameter, determining the smoothing result of the pixel point as a leaf area index smoothing processing result of the area to be optimized; if the smoothing result of the pixel point is smaller than or equal to the preset smoothing result evaluation parameter, determining the smoothing result of the pixel point as the preset smoothing result evaluation parameter, calculating a third leaf area index set of the pixel point based on the smoothing result of the pixel point, determining the third leaf area index set of the pixel point as the leaf area index set of the pixel point, repeatedly executing the first processing step and the second processing step until the repeated execution times is greater than the preset times, or the smoothing result of the pixel point is greater than the preset smoothing result evaluation parameter, and determining the smoothing result of the pixel point obtained by repeatedly executing the preset times or the smoothing result of the pixel point greater than the preset smoothing result evaluation parameter as the leaf area index smoothing processing result of the area to be optimized.
In a third aspect, an embodiment of the present invention further provides an electronic device, including a memory and a processor, where the memory is used to store a program that supports the processor to execute the method in the first aspect, and the processor is configured to execute the program stored in the memory.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to perform the steps of the method in the first aspect.
In the embodiment of the invention, a plurality of initial leaf area index images and quality control data of a region to be optimized are obtained, wherein the initial leaf area index images are used for representing leaf area indexes of the region to be optimized at different time, and the quality control data is used for representing the quality of each pixel point in the initial leaf area index images; based on the quality control data, correcting the multiple initial leaf area index images to obtain multiple target leaf area index images; constructing a leaf area index set of pixel points in a region to be optimized based on a plurality of target leaf area index images, wherein the leaf area index set comprises leaf area indexes of the pixel points in the region to be optimized in the target leaf area index images; the Whittaker Smoother algorithm is utilized to smooth the leaf area index set of the pixel points in the area to be optimized to obtain the leaf area index smooth processing result of the area to be optimized, so that the aim of efficiently and accurately optimizing the precision of the leaf area index is fulfilled, the technical problems of low error and low efficiency of the existing leaf area index precision optimizing method are solved, and the technical effects of improving the efficiency of the leaf area index precision optimization and reducing the error of the leaf area index precision optimization are achieved.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flowchart of a method for optimizing leaf area index accuracy according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an apparatus for optimizing precision of a leaf area index according to an embodiment of the present invention;
fig. 3 is a schematic diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present invention clearer, 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, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The first embodiment is as follows:
in accordance with an embodiment of the present invention, there is provided an embodiment of a method for optimizing leaf area index accuracy, it being noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than presented herein.
Fig. 1 is a flowchart of a method for optimizing the precision of a leaf area index according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, obtaining a plurality of initial leaf area index images and quality control data of a region to be optimized, wherein the initial leaf area index images are used for representing leaf area indexes of the region to be optimized at different time, and the quality control data is used for representing the quality of each pixel point in the initial leaf area index images;
preferably, the plurality of initial leaf area index images are 4-day 500m resolution leaf area index images generated by the MODIS satellite Aqua/Terra sensor (MCD15A 3H).
The quality control data can represent whether each pixel point is covered by a cloud layer or not, or the quality of the pixel points is poor due to other reasons.
Step S104, based on the quality control data, correcting the plurality of initial leaf area index images to obtain a plurality of target leaf area index images;
step S106, constructing a leaf area index set of pixel points in the region to be optimized based on the target leaf area index images, wherein the leaf area index set comprises leaf area indexes of the pixel points in the region to be optimized in the target leaf area index images;
and step S108, smoothing the leaf area index set of the pixel points in the region to be optimized by using the Whittaker Smoother algorithm to obtain a leaf area index smoothing result of the region to be optimized.
In the embodiment of the invention, a plurality of initial leaf area index images and quality control data of a region to be optimized are obtained, wherein the initial leaf area index images are used for representing leaf area indexes of the region to be optimized at different time, and the quality control data is used for representing the quality of each pixel point in the initial leaf area index images; based on the quality control data, correcting the multiple initial leaf area index images to obtain multiple target leaf area index images; constructing a leaf area index set of pixel points in a region to be optimized based on a plurality of target leaf area index images, wherein the leaf area index set comprises leaf area indexes of the pixel points in the region to be optimized in the target leaf area index images; the Whittaker Smoother algorithm is utilized to smooth the leaf area index set of the pixel points in the area to be optimized to obtain the leaf area index smooth processing result of the area to be optimized, so that the aim of efficiently and accurately optimizing the precision of the leaf area index is fulfilled, the technical problems of low error and low efficiency of the existing leaf area index precision optimizing method are solved, and the technical effects of improving the efficiency of the leaf area index precision optimization and reducing the error of the leaf area index precision optimization are achieved.
In the embodiment of the present invention, step S104 includes the following steps:
step S11, determining first target pixel points in a plurality of initial leaf area index images, wherein the first target pixel points are pixel points in a leaf area index image within a preset effective range;
step S12, setting the leaf area indexes of second target pixel points in a plurality of initial leaf area index images as null values to obtain a plurality of intermediate leaf area index images, wherein the second target pixel points are pixel points in the initial leaf area index images except the first target pixel points;
step S13, determining a first final pixel point in a plurality of intermediate leaf area index images based on the quality control data, wherein the first final pixel point is a pixel point of which the quality control data in the intermediate leaf area index images is 0;
step S14, setting the leaf area index of a second final pixel point in the intermediate leaf area index images to be a null value, and obtaining a plurality of target leaf area index images, where the second final pixel point is a pixel point in the intermediate leaf area index images other than the first final pixel point.
In the embodiment of the invention, the improved Whittaker Smoother (WS) algorithm is used for improvement, and the image with invalid values and abnormal values removed is subjected to improved Whittaker Smoother smoothing filtering processing, so that the image noise is reduced, and the identification degree is improved.
Specifically, a preset valid range may be set to 0-100, data outside the range is regarded as an invalid value (i.e., a second target pixel), and for a set L of pixels in an initial leaf area index image, any one pixel in the set L is considered
>100 or
<0, then
(NaN represents a null value), a median leaf area index image is obtained, in which,
is the pixel point of the ith row and the jth column.
Then, the quality control data set is C, the last bit of the binary number is the most leaf area index data verification standard obtained through AND operation,
use of
The data is corrected, and the correction is carried out,
is 0 represents data
Good quality (i.e. first final pixel), i.e.If it is not
>0, then
(i.e., the second final pixel).
In the embodiment of the present invention, step S108 includes the following steps:
step S21, determining the number of non-null values contained in the leaf area index set of the pixel points;
step S22, if the number of the non-null values is greater than or equal to a preset threshold value, processing the null values based on a linear interpolation algorithm to obtain a first leaf area index set of pixel points;
step S23, based on the Whittaker Smoother algorithm, performing first smoothing processing on a first leaf area index set of pixel points to obtain a first calculation result of the pixel points;
step S24, determining abnormal values in the first leaf area index set of the pixel points based on the first calculation result;
step S25, removing the abnormal value from the first leaf area index set of the pixel point, and carrying out interpolation processing on the null value in the first leaf area index set of the pixel point to obtain a second leaf area index set of the pixel point;
and step S26, performing second smoothing processing on a second leaf area index set of the pixel points based on the Whittaker Smoother algorithm to obtain a leaf area index smoothing processing result of the region to be optimized.
In the embodiment of the present invention, taking one pixel point in one to-be-optimized area as an example, P =
Is a set of leaf area indices for a pixel point in a plurality of target leaf area index images, wherein,
。
first, P = is judged
And if the number of the non-null values is greater than or equal to a preset threshold value, filling the null values based on a linear interpolation algorithm to obtain a first leaf area index set of the pixel points, wherein the preset threshold value is generally set to be 3.
Then, the first set of leaf area indices for the pixel points is smoothed using the following equation:
wherein, the length of the first leaf area index set P of the pixel point is k,
is an identity matrix of k x k order,
is a matrix of (k-2) x k orders,
is that
The transpose matrix of (a) is,
，λ=2。
the first calculation result of the pixel point is S =
。
Then, based on the first calculation result, determining an abnormal value in the first leaf area index set of the pixel point, and eliminating the abnormal value from the first leaf area index set of the pixel point, namely
Then, interpolation processing is carried out on null values in the first leaf area index set of the pixel points, and a second leaf area index set of the pixel points is obtained.
And finally, performing second smoothing treatment on a second leaf area index set of the pixel points by using a Whittaker Smoother algorithm to obtain a leaf area index smoothing treatment result of the region to be optimized.
Specifically, step S26 includes the following steps:
step S31, an obtaining step, obtaining a preset smooth result evaluation parameter and a preset weight value;
step S32, a first processing step, based on the Whittaker Smoother algorithm, performing first smoothing processing on a second leaf area index set of pixel points to obtain a second calculation result of the pixel points;
step S33, a second processing step of calculating a smoothing result of the pixel point based on a second calculation result of the pixel point;
step S35, if the smoothing result of the pixel point is larger than the preset smoothing result evaluation parameter, determining the smoothing result of the pixel point as the leaf area index smoothing processing result of the area to be optimized;
step S35, if the smoothing result of the pixel point is less than or equal to the preset smoothing result evaluation parameter, determining the smoothing result of the pixel point as the preset smoothing result evaluation parameter, calculating a third leaf area index set of the pixel point based on the smoothing result of the pixel point, determining the third leaf area index set of the pixel point as the leaf area index set of the pixel point, and repeatedly executing the first processing step and the second processing step until the number of times of repeated execution is greater than the preset number of times, or the smoothing result of the pixel point is greater than the preset smoothing result evaluation parameter, and determining the smoothing result of the pixel point obtained by repeatedly executing the preset number of times or the smoothing result of the pixel point greater than the preset smoothing result evaluation parameter as the leaf area index smoothing result of the region to be optimized.
In the embodiment of the invention, the preset level is firstly obtainedThe sliding result evaluation parameter F =100000000 and the preset weight value W = None, λ is set to 1, and a Whittaker Smoother algorithm is used for performing first sliding processing on a second leaf area index set of a pixel point to obtain a second calculation result S1= of the pixel point
The specific calculation process is the same as step 23, and is not described herein again.
Then, a D vector is calculated, wherein
And setting an identification vector: m =
If, if
Then, then
,
Then, then
Calculating the weight W =ifW = None
Calculating the formula
Dmax is the maximum of the absolute values of the elements of the D vector;
smoothing result of pixel points
Wherein m is the length of the P vector.
If the smoothing result of the pixel point is larger than the preset smoothing result evaluation parameter, determining the smoothing result of the pixel point as a leaf area index smoothing processing result of the area to be optimized;
if the smoothing result of the pixel point is smaller than or equal to the preset smoothing result evaluation parameter, determining the smoothing result of the pixel point as the preset smoothing result evaluation parameter, calculating a third leaf area index set of the pixel point based on the smoothing result of the pixel point, determining the third leaf area index set of the pixel point as the leaf area index set of the pixel point, repeatedly executing the first processing step and the second processing step until the repeated execution times is greater than the preset times, or the smoothing result of the pixel point is greater than the preset smoothing result evaluation parameter, and determining the smoothing result of the pixel point obtained by repeatedly executing the preset times or the smoothing result of the pixel point greater than the preset smoothing result evaluation parameter as the leaf area index smoothing processing result of the area to be optimized.
It should be noted that, the specific calculation process of the third leaf area index set for calculating the pixel point based on the smoothing result of the pixel point is as follows, and the third leaf area index set P3= for the pixel point
Calculating the formula
。
According to the embodiment of the invention, the multi-time-phase data are smoothly filtered according to the time dimension, the growth change of the vegetation on the earth has time continuity, and according to the characteristic, the time sequence smoothing filtering is adopted, so that the trend of the vegetation growth change along with time can be reflected.
Optimization is carried out based on Whittaker Smoother filtering algorithm, and compared with Savitzky-Golay filtering (SG), the WS algorithm speed is improved by over one hundred times.
In the embodiment of the invention, only MODIS LAI original data are prepared, initial year and month parameters are input and calculated, and the smooth filtered MODIS LAI product can be obtained through calculation.
By means of the algorithm, continuous high-precision LAI products can be generated in the spatial dimension.
Example two:
the embodiment of the present invention further provides an optimizing device for precision of a leaf area index, where the optimizing device for precision of a leaf area index is used to execute the optimizing method for precision of a leaf area index provided in the foregoing content of the embodiment of the present invention, and the following is a specific description of the optimizing device for precision of a leaf area index provided in the embodiment of the present invention.
As shown in fig. 2, fig. 2 is a schematic diagram of the above-mentioned leaf area index accuracy optimizing device, and the leaf area index accuracy optimizing device includes: an acquisition unit 10, a correction unit 20, a construction unit 30 and a processing unit 40.
The acquiring unit 10 is configured to acquire a plurality of initial leaf area index images and quality control data of a region to be optimized, where the plurality of initial leaf area index images are used to represent leaf area indexes of the region to be optimized at different times, and the quality control data is used to represent quality of each pixel point in the plurality of initial leaf area index images;
the correcting unit 20 is configured to correct the multiple initial leaf area index images based on the quality control data to obtain multiple target leaf area index images;
the constructing unit 30 is configured to construct a leaf area index set of pixel points in the region to be optimized based on the target leaf area index images, where the leaf area index set includes leaf area indexes of the pixel points in the region to be optimized in the target leaf area index images;
the processing unit 40 is configured to perform smoothing processing on the set of leaf area indexes of the pixel points in the region to be optimized by using the Whittaker Smoother algorithm, so as to obtain a result of smoothing processing on the leaf area indexes of the region to be optimized.
In the embodiment of the invention, a plurality of initial leaf area index images and quality control data of a region to be optimized are obtained, wherein the initial leaf area index images are used for representing leaf area indexes of the region to be optimized at different time, and the quality control data is used for representing the quality of each pixel point in the initial leaf area index images; based on the quality control data, correcting the multiple initial leaf area index images to obtain multiple target leaf area index images; constructing a leaf area index set of pixel points in a region to be optimized based on a plurality of target leaf area index images, wherein the leaf area index set comprises leaf area indexes of the pixel points in the region to be optimized in the target leaf area index images; the Whittaker Smoother algorithm is utilized to smooth the leaf area index set of the pixel points in the area to be optimized to obtain the leaf area index smooth processing result of the area to be optimized, so that the aim of efficiently and accurately optimizing the precision of the leaf area index is fulfilled, the technical problems of low error and low efficiency of the existing leaf area index precision optimizing method are solved, and the technical effects of improving the efficiency of the leaf area index precision optimization and reducing the error of the leaf area index precision optimization are achieved.
Preferably, the correction unit is configured to: determining first target pixel points in a plurality of initial leaf area index images, wherein the first target pixel points are pixel points in a preset effective range in the leaf area index images; setting the leaf area index of a second target pixel point in a plurality of initial leaf area index images as a null value to obtain a plurality of intermediate leaf area index images, wherein the second target pixel point is a pixel point in the initial leaf area index images except the first target pixel point; determining a first final pixel point in a plurality of intermediate leaf area index images based on the quality control data, wherein the first final pixel point is a pixel point of which the quality control data is 0 in the intermediate leaf area index images; setting the leaf area index of a second final pixel point in the intermediate leaf area index images as a null value to obtain a plurality of target leaf area index images, wherein the second final pixel point is a pixel point except the first final pixel point in the intermediate leaf area index images.
Preferably, the processing unit is configured to: determining the number of non-null values contained in the leaf area index set of the pixel points; if the number of the non-null values is larger than or equal to a preset threshold value, processing the null values based on a linear interpolation algorithm to obtain a first leaf area index set of pixel points; based on the Whittaker Smoother algorithm, performing first smoothing processing on a first leaf area index set of pixel points to obtain a first calculation result of the pixel points; determining an abnormal value in a first leaf area index set of the pixel points based on the first calculation result; removing the abnormal value from the first leaf area index set of the pixel point, and carrying out interpolation processing on a null value in the first leaf area index set of the pixel point to obtain a second leaf area index set of the pixel point; and performing second smoothing treatment on a second leaf area index set of the pixel points based on the Whittaker Smoother algorithm to obtain a leaf area index smoothing treatment result of the region to be optimized.
Preferably, the processing unit is further configured to perform the following steps: an obtaining step of obtaining a preset smooth result evaluation parameter and a preset weight value; a first processing step, based on the Whittaker Smoother algorithm, of performing first smoothing processing on a second leaf area index set of pixel points to obtain a second calculation result of the pixel points; a second processing step of calculating a smoothing result of the pixel point based on a second calculation result of the pixel point; if the smoothing result of the pixel point is larger than the preset smoothing result evaluation parameter, determining the smoothing result of the pixel point as a leaf area index smoothing processing result of the area to be optimized; if the smoothing result of the pixel point is smaller than or equal to the preset smoothing result evaluation parameter, determining the smoothing result of the pixel point as the preset smoothing result evaluation parameter, calculating a third leaf area index set of the pixel point based on the smoothing result of the pixel point, determining the third leaf area index set of the pixel point as the leaf area index set of the pixel point, repeatedly executing the first processing step and the second processing step until the repeated execution times is greater than the preset times, or the smoothing result of the pixel point is greater than the preset smoothing result evaluation parameter, and determining the smoothing result of the pixel point obtained by repeatedly executing the preset times or the smoothing result of the pixel point greater than the preset smoothing result evaluation parameter as the leaf area index smoothing processing result of the area to be optimized.
Example three:
an embodiment of the present invention further provides an electronic device, including a memory and a processor, where the memory is used to store a program that supports the processor to execute the method described in the first embodiment, and the processor is configured to execute the program stored in the memory.
Referring to fig. 3, an embodiment of the present invention further provides an electronic device 100, including: a processor 60, a memory 61, a bus 62 and a communication interface 63, wherein the processor 60, the communication interface 63 and the memory 61 are connected through the bus 62; the processor 60 is arranged to execute executable modules, such as computer programs, stored in the memory 61.
The Memory 61 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. The communication connection between the network element of the system and at least one other network element is realized through at least one communication interface 63 (which may be wired or wireless), and the internet, a wide area network, a local network, a metropolitan area network, and the like can be used.
The bus 62 may be an ISA bus, PCI bus, EISA bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 3, but this does not indicate only one bus or one type of bus.
The memory 61 is used for storing a program, the processor 60 executes the program after receiving an execution instruction, and the method executed by the apparatus defined by the flow process disclosed in any of the foregoing embodiments of the present invention may be applied to the processor 60, or implemented by the processor 60.
The processor 60 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 60. The Processor 60 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the device can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory 61, and the processor 60 reads the information in the memory 61 and, in combination with its hardware, performs the steps of the above method.
Example four:
the embodiment of the present invention further provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and when the computer program is executed by a processor, the computer program performs the steps of the method in the first embodiment.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one logical division, and there may be other divisions when actually implemented, and for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or units through some communication interfaces, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
Finally, it should be noted that: the above-mentioned embodiments are only specific embodiments of the present invention, which are used for illustrating the technical solutions of the present invention and not for limiting the same, and the protection scope of the present invention is not limited thereto, although the present invention is described in detail with reference to the foregoing embodiments, those skilled in the art should understand that: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (8)
1. A method for optimizing the precision of a leaf area index is characterized by comprising the following steps:
acquiring a plurality of initial leaf area index images and quality control data of a region to be optimized, wherein the initial leaf area index images are used for representing leaf area indexes of the region to be optimized at different time, and the quality control data is used for representing the quality of each pixel point in the initial leaf area index images;
based on the quality control data, correcting the plurality of initial leaf area index images to obtain a plurality of target leaf area index images;
constructing a leaf area index set of pixel points in the region to be optimized based on the target leaf area index images, wherein the leaf area index set comprises leaf area indexes of the pixel points in the region to be optimized in the target leaf area index images;
smoothing the leaf area index set of the pixel points in the region to be optimized by utilizing a Whittaker Smoother algorithm to obtain a leaf area index smoothing result of the region to be optimized;
smoothing the leaf area index set of the pixel points in the region to be optimized by using a Whittaker Smoother algorithm to obtain a leaf area index smoothing result of the region to be optimized, wherein the smoothing process comprises the following steps:
determining the number of non-null values contained in the leaf area index set of the pixel points;
if the number of the non-null values is larger than or equal to a preset threshold value, processing the null values based on a linear interpolation algorithm to obtain a first leaf area index set of pixel points;
based on the Whittaker Smoother algorithm, performing first smoothing processing on a first leaf area index set of pixel points to obtain a first calculation result of the pixel points;
determining an abnormal value in a first leaf area index set of the pixel points based on the first calculation result;
removing the abnormal value from the first leaf area index set of the pixel point, and carrying out interpolation processing on a null value in the first leaf area index set of the pixel point to obtain a second leaf area index set of the pixel point;
and performing second smoothing treatment on a second leaf area index set of the pixel points based on the Whittaker Smoother algorithm to obtain a leaf area index smoothing treatment result of the region to be optimized.
2. The method of claim 1, wherein modifying the plurality of initial leaf area index images based on the quality control data to obtain a plurality of target leaf area index images comprises:
determining first target pixel points in a plurality of initial leaf area index images, wherein the first target pixel points are pixel points in a preset effective range in the leaf area index images;
setting the leaf area index of a second target pixel point in a plurality of initial leaf area index images as a null value to obtain a plurality of intermediate leaf area index images, wherein the second target pixel point is a pixel point in the initial leaf area index images except the first target pixel point;
determining a first final pixel point in a plurality of intermediate leaf area index images based on the quality control data, wherein the first final pixel point is a pixel point of which the quality control data is 0 in the intermediate leaf area index images;
setting the leaf area index of a second final pixel point in the intermediate leaf area index images as a null value to obtain a plurality of target leaf area index images, wherein the second final pixel point is a pixel point except the first final pixel point in the intermediate leaf area index images.
3. The method according to claim 1, wherein performing a second smoothing process on a second leaf area index set of pixel points based on the Whittaker Smoother algorithm to obtain a leaf area index smoothing process result of the region to be optimized, comprises:
an obtaining step of obtaining a preset smooth result evaluation parameter and a preset weight value;
a first processing step, based on the Whittaker Smoother algorithm, performing the first smoothing processing again on a second leaf area index set of the pixel points to obtain a second calculation result of the pixel points;
a second processing step of calculating a smoothing result of the pixel point based on a second calculation result of the pixel point;
if the smoothing result of the pixel point is larger than the preset smoothing result evaluation parameter, determining the smoothing result of the pixel point as a leaf area index smoothing processing result of the area to be optimized;
if the smoothing result of the pixel point is smaller than or equal to the preset smoothing result evaluation parameter, determining the smoothing result of the pixel point as the preset smoothing result evaluation parameter, calculating a third leaf area index set of the pixel point based on the smoothing result of the pixel point, determining the third leaf area index set of the pixel point as the leaf area index set of the pixel point, repeatedly executing the first processing step and the second processing step until the repeated execution times is greater than the preset times, or the smoothing result of the pixel point is greater than the preset smoothing result evaluation parameter, and determining the smoothing result of the pixel point obtained by repeatedly executing the preset times or the smoothing result of the pixel point greater than the preset smoothing result evaluation parameter as the leaf area index smoothing processing result of the area to be optimized.
4. An apparatus for optimizing the accuracy of a leaf area index, comprising: an acquisition unit, a correction unit, a construction unit and a processing unit, wherein,
the acquiring unit is used for acquiring a plurality of initial leaf area index images and quality control data of a region to be optimized, wherein the initial leaf area index images are used for representing leaf area indexes of the region to be optimized at different time, and the quality control data is used for representing the quality of each pixel point in the initial leaf area index images;
the correcting unit is used for correcting the plurality of initial leaf area index images based on the quality control data to obtain a plurality of target leaf area index images;
the constructing unit is configured to construct a leaf area index set of pixel points in the region to be optimized based on the target leaf area index images, where the leaf area index set includes leaf area indexes of the pixel points in the region to be optimized in the target leaf area index images;
the processing unit is used for smoothing the leaf area index set of the pixel points in the region to be optimized by utilizing a Whittaker Smoother algorithm to obtain a leaf area index smoothing result of the region to be optimized;
wherein the processing unit is configured to:
determining the number of non-null values contained in the leaf area index set of the pixel points;
if the number of the non-null values is larger than or equal to a preset threshold value, processing the null values based on a linear interpolation algorithm to obtain a first leaf area index set of pixel points;
based on the Whittaker Smoother algorithm, performing first smoothing processing on a first leaf area index set of pixel points to obtain a first calculation result of the pixel points;
determining an abnormal value in a first leaf area index set of the pixel points based on the first calculation result;
removing the abnormal value from the first leaf area index set of the pixel point, and carrying out interpolation processing on a null value in the first leaf area index set of the pixel point to obtain a second leaf area index set of the pixel point;
and performing second smoothing treatment on a second leaf area index set of the pixel points based on the Whittaker Smoother algorithm to obtain a leaf area index smoothing treatment result of the region to be optimized.
5. The apparatus of claim 4, wherein the modification unit is configured to:
determining first target pixel points in a plurality of initial leaf area index images, wherein the first target pixel points are pixel points in a preset effective range in the leaf area index images;
setting the leaf area index of a second target pixel point in a plurality of initial leaf area index images as a null value to obtain a plurality of intermediate leaf area index images, wherein the second target pixel point is a pixel point in the initial leaf area index images except the first target pixel point;
determining a first final pixel point in a plurality of intermediate leaf area index images based on the quality control data, wherein the first final pixel point is a pixel point of which the quality control data is 0 in the intermediate leaf area index images;
setting the leaf area index of a second final pixel point in the intermediate leaf area index images as a null value to obtain a plurality of target leaf area index images, wherein the second final pixel point is a pixel point except the first final pixel point in the intermediate leaf area index images.
6. The apparatus of claim 4, wherein the processing unit is further configured to perform the steps of:
an obtaining step of obtaining a preset smooth result evaluation parameter and a preset weight value;
a first processing step, based on the Whittaker Smoother algorithm, performing the first smoothing processing again on a second leaf area index set of the pixel points to obtain a second calculation result of the pixel points;
a second processing step of calculating a smoothing result of the pixel point based on a second calculation result of the pixel point;
if the smoothing result of the pixel point is larger than the preset smoothing result evaluation parameter, determining the smoothing result of the pixel point as a leaf area index smoothing processing result of the area to be optimized;
if the smoothing result of the pixel point is smaller than or equal to the preset smoothing result evaluation parameter, determining the smoothing result of the pixel point as the preset smoothing result evaluation parameter, calculating a third leaf area index set of the pixel point based on the smoothing result of the pixel point, determining the third leaf area index set of the pixel point as the leaf area index set of the pixel point, repeatedly executing the first processing step and the second processing step until the repeated execution times is greater than the preset times, or the smoothing result of the pixel point is greater than the preset smoothing result evaluation parameter, and determining the smoothing result of the pixel point obtained by repeatedly executing the preset times or the smoothing result of the pixel point greater than the preset smoothing result evaluation parameter as the leaf area index smoothing processing result of the area to be optimized.
7. An electronic device comprising a memory for storing a program that enables a processor to perform the method of any of claims 1 to 3 and a processor configured to execute the program stored in the memory.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method according to any one of the claims 1 to 3.
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