阅读说明：本技术 基于特征选择的全波形机载激光雷达地物分类方法及系统 (Full-waveform airborne laser radar ground feature classification method and system based on feature selection ) 是由 张良 张帆 金贵 梁昌迅 于 2019-11-13 设计创作，主要内容包括：本发明涉及一种基于ReliefF特征选择的全波形机载激光雷达地物分类方法。该方法包括：对机载激光雷达全波形数据进行高斯分解,获取回波参数并使用LM算法进行优化；基于不同几何邻域提取机载激光雷达数据点云特征,利用优化后的回波参数提取全波形数据的波形特征；将机载激光雷达数据中的点云特征和全波形数据的波形特征相融合,使用ReliefF算法对各特征在分类时的重要性进行评价；进行特征选择,分析多种特征组合对不同地物分类效果的影响,利用随机森林分类器进行地物分类。本发明利用机载激光雷达点云数据与全波形数据,通过特征提取与选择找到分类的最优特征组合,从而达到实现高精度地物多分类的目的。(The invention relates to a full-waveform airborne laser radar ground object classification method based on Relieff feature selection. The method comprises the following steps: carrying out Gaussian decomposition on the full waveform data of the airborne laser radar, obtaining echo parameters and optimizing by using an LM algorithm; extracting the point cloud characteristics of the airborne laser radar data based on different geometric neighborhoods, and extracting the waveform characteristics of the full waveform data by using the optimized echo parameters; fusing point cloud characteristics in airborne laser radar data and waveform characteristics of full waveform data, and evaluating the importance of each characteristic in classification by using a Relieff algorithm; and (4) selecting features, analyzing the influence of various feature combinations on different ground feature classification effects, and classifying the ground features by using a random forest classifier. The invention utilizes the point cloud data and the full waveform data of the airborne laser radar to find the classified optimal feature combination through feature extraction and selection, thereby achieving the purpose of realizing high-precision ground object multi-classification.)

1. A full-waveform airborne laser radar ground object classification method based on feature selection is characterized by comprising the following steps:

a parameter obtaining step of performing Gaussian decomposition on the full waveform data to obtain echo parameters and optimizing the echo parameters;

a characteristic extraction step, namely extracting point cloud characteristics by setting different geometric neighborhoods, and acquiring waveform characteristics by using the optimized echo parameters;

a feature fusion step, namely fusing the point cloud features and the waveform features to obtain fusion features, and performing importance evaluation on the fusion features by using a Relieff algorithm;

and a land feature classification step, selecting the fusion features before evaluation to classify the land features.

2. The method for classifying the terrain features of the full-waveform airborne laser radar based on feature selection according to claim 1, wherein in the feature fusion step, the importance evaluation of the fusion features by using a Relieff algorithm comprises:

randomly selecting a training example sample R, searching a K neighbor sample H in a homogeneous sample of the sample R, and searching a K neighbor sample M in a non-homogeneous sample of the sample R, wherein K is a positive integer;

if the distance between the sample R and the sample H on a certain fusion feature is larger than the distance between the sample R and the sample M on the fusion feature, the weight of the fusion feature is enhanced, otherwise, the weight of the fusion feature is reduced;

and sequencing the fusion features after the weights are calculated.

3. The method for classifying the terrain features of the full-waveform airborne laser radar based on feature selection as claimed in claim 1, wherein in the parameter obtaining step, the Gaussian decomposition comprises the following specific steps:

the first derivative is taken of the full waveform data and the number of its zero crossings is determined as the number of echo components.

Calculating a second derivative of the full waveform data, determining the position of an inflection point of the full waveform data, and calculating an echo parameter of a Gaussian component;

and optimizing the initial values of the echo parameters by utilizing an LM algorithm, so that the sum of squares of the residual errors is smaller than a preset threshold value.

4. The feature selection-based full-waveform airborne laser radar ground object classification method according to claim 1, wherein in the feature extraction step, the point cloud feature extraction comprises:

extracting characteristic step numbers with good distinguishing effect on buildings from airborne laser radar point cloud data by using a grid neighborhood;

extracting elevation related features from airborne laser radar point cloud data based on the cylindrical neighborhood;

and extracting density related features from the airborne laser radar point cloud data based on the sphere neighborhood.

5. The feature selection-based full-waveform airborne laser radar terrain classification method according to claim 1, wherein in the terrain classification step, the influence of different feature combinations on the terrain classification effect is analyzed, and a random forest classifier is used for terrain classification, and the method comprises the following steps:

selecting a characteristic combination which meets the requirement according to the characteristic importance sorting result;

training a random forest classifier by using training sample data;

classifying the experimental data of the fused point cloud characteristics and the waveform characteristics by using a trained random forest classifier to obtain the category of each laser point;

evaluating the precision change of ground feature classification carried out by different feature combinations, and analyzing the influence of adding different features on the classification effect of each ground feature type.

6. The full-waveform airborne lidar terrain classification method based on feature selection as claimed in claim 1, further comprising:

the full-waveform preprocessing step is used for removing full-waveform data noise, and specifically comprises the following steps:

and calculating the average value of data in each preset range before and after the original waveform, and setting a noise threshold according to a 3 sigma principle to remove background noise.

And performing smooth filtering on the data by using a Gaussian function of a window with a preset size, and removing random noise in the waveform.

7. The method for full-waveform airborne lidar terrain classification based on feature selection according to claim 1, wherein the obtaining of the full-waveform data comprises:

carrying a set of full-waveform laser radar system on the aviation carrier;

carrying out aviation flight on the measuring area according to the formulated flight scheme;

and generating a theoretical model according to the full-waveform airborne laser radar data to obtain airborne laser radar point cloud data and matched full-waveform data.

8. A full-waveform airborne laser radar terrain classification system based on feature selection is characterized by comprising:

the parameter acquisition module is used for carrying out Gaussian decomposition on the full waveform data to acquire echo parameters and optimizing the echo parameters;

the characteristic extraction module is used for extracting point cloud characteristics by setting different geometric neighborhoods and acquiring waveform characteristics by using the optimized echo parameters;

the characteristic fusion module is used for fusing the point cloud characteristics and the waveform characteristics to obtain fusion characteristics, and importance evaluation is carried out on the fusion characteristics by using a Relieff algorithm;

and the ground feature classification module is used for selecting the fusion features before evaluation to classify the ground features.

9. The system for full-waveform airborne lidar terrain classification based on feature selection according to claim 8, wherein the feature fusion module performs importance evaluation on the fusion features by using a Relieff algorithm comprising:

randomly selecting a training example sample R, searching a K neighbor sample H in a homogeneous sample of the sample R, and searching a K neighbor sample M in a non-homogeneous sample of the sample R, wherein K is a positive integer;

if the distance between the sample R and the sample H on a certain fusion feature is larger than the distance between the sample R and the sample M on the fusion feature, the weight of the fusion feature is enhanced, otherwise, the weight of the fusion feature is reduced;

and sequencing the fused features after the high-speed weighting.

10. The system for full-waveform airborne lidar terrain classification based on feature selection according to claim 8, wherein the parameter obtaining module comprises the following specific steps of gaussian decomposition:

the first derivative is taken of the full waveform data and the number of its zero crossings is determined as the number of echo components.

Calculating a second derivative of the full waveform data, determining the position of an inflection point of the full waveform data, and calculating an echo parameter of a Gaussian component;

and optimizing the initial values of the echo parameters by utilizing an LM algorithm, so that the sum of squares of the residual errors is smaller than a preset threshold value.

[ technical field ] A method for producing a semiconductor device

The invention relates to a method and a system for classifying ground objects of an airborne laser radar, belongs to the technical field of airborne laser radars, and particularly relates to a full-waveform airborne laser radar ground object classification method based on feature selection.

[ background of the invention ]

An Airborne laser radar (ALS) is an active earth observation System which integrates a high-precision Inertial Measurement Unit (IMU), a Global positioning navigation System (GPS) and a laser scanning ranging System, can obtain the distance between a scanner and a laser foot point by measuring the propagation time of laser pulses in the air, and is widely applied to forest parameter estimation, three-dimensional city modeling, power line extraction, digital ground model generation and other aspects. In recent years, ALS has many advantages such as less influence of weather, fast operation period, high timeliness and penetrability, and is increasingly becoming an important way of remote sensing data acquisition technology.

However, the traditional airborne laser radar can only provide discrete three-dimensional point cloud data and echo intensity information, and the limited information greatly restricts the accuracy of ground feature classification. The method for making up the lack of point cloud physical information through a multi-source data fusion method is a commonly used solution at present, but the method also brings a problem of high requirement on data completeness.

At present, with the development of a full-waveform LiDAR observation technology, the acquisition of full-waveform LiDAR data provides a new possibility for making up the deficiency of the point cloud data physical information of the traditional airborne laser radar and improving the ground object classification precision. Compared to conventional airborne laser LiDAR systems, full waveform LiDAR systems have two advantages: (1) the return signals received by a full-waveform LiDAR system contain more comprehensive ground geometry information. This means that full waveform LiDAR data can describe the roughness, horizontal distribution, and vertical structure of a target feature (especially in a forest area) in more detail than traditional LiDAR data within the same spot. (2) After the full waveform data is subjected to waveform decomposition, richer high-density and high-precision three-dimensional point cloud data can be obtained.

The basic purpose of full waveform data decomposition is to achieve a transformation from one-dimensional signals to three-dimensional point data, i.e. to obtain three-dimensional terrain information from one-dimensional waveform signals. The additional waveform information (pulse width, amplitude and the like) can be extracted by adopting a Gaussian decomposition method, and the obtained waveform parameters not only enable the waveform data processing precision to be higher, but also can reflect the characteristics of the ground features. Therefore, through the utilization of the full-waveform airborne laser radar data, the ground features can be described more finely, rich information is provided, and the method has extremely important significance for ground feature classification.

[ summary of the invention ]

In view of the above, in order to overcome the defects of the prior art, the invention provides a full-waveform airborne laser radar ground feature classification method and system based on feature selection, aiming at the problems that the classification precision of the ground features is limited and the full-waveform data processing has small application range and large calculation consumption.

In order to achieve the aim, the invention discloses a full-waveform airborne LiDAR ground object classification method based on Relieff feature selection, which comprises the following steps:

preparing and installing a full-waveform airborne laser radar, and generating airborne laser radar data;

and step two, preprocessing is carried out after the full-waveform airborne laser radar data is acquired, and noise influence is removed.

And step three, carrying out Gaussian decomposition on the full waveform data after pretreatment to obtain echo parameters, and optimizing by using an LM algorithm.

And step four, extracting point cloud characteristics by setting different geometric neighborhoods, and acquiring waveform characteristics by using the optimized echo parameters.

And fifthly, fusing the point cloud characteristics in the acquired airborne laser radar data with the waveform characteristics of the full waveform data, and evaluating the importance of each characteristic by using a Relieff algorithm.

And step six, selecting features according to the sorting result, analyzing the influence of various feature combinations on different ground feature sorting effects, and sorting the ground features by using a random forest classifier.

Further, the specific method of the first step is as follows:

11) carrying a set of full-waveform LiDAR system on an aviation carrier, wherein the full-waveform LiDAR system comprises an Inertial Measurement Unit (IMU), a Differential Global Positioning System (DGPS), a laser scanning ranging system and an imaging device;

12) carrying out aviation flight on the measuring area according to the formulated flight scheme;

13) and generating a theoretical model according to the full-waveform airborne laser radar data to obtain a set of full-waveform airborne laser radar data and full-waveform data.

The specific method of the second step is as follows:

21) and calculating the average value of front and back 5% data in the original waveform, setting a noise threshold according to a 3 sigma principle, and removing background noise.

22) And performing smooth filtering on the data by using a Gaussian function with the window size of 5 to remove random noise in the waveform.

The concrete method of the third step is as follows:

31) and (3) solving a first derivative of the waveform data after the preprocessing, and determining the number of zero crossing points of the waveform data as the number of echo components.

32) Calculating the second derivative of the preprocessed waveform data, and determining the position of the inflection pointCalculating echo parameters (A) of the Gaussian component_i、μ_i、ω_i)。

33) Optimizing each echo parameter initial value by using LM algorithm to ensure that the sum of squares of residuals is ξ^Tξ is less than a preset threshold.

The concrete method of the fourth step is as follows:

41) and setting different geometric neighborhoods (a grid neighborhood, a cylinder neighborhood and a sphere neighborhood) to extract point cloud characteristics.

42) According to the optimized echo parameters (A)_i、μ_i、ω_i) And extracting waveform characteristics.

The concrete method of the step five is as follows:

51) and selecting representative experimental data for manual classification, and using the representative experimental data as a training sample of a Relieff algorithm and a classifier.

52) And fusing point cloud characteristics and waveform characteristics for experimental data and training samples.

53) And (3) performing importance evaluation on the point cloud characteristics and the waveform characteristics in the training sample data by using a Relieff algorithm.

The concrete method of the sixth step is as follows:

61) and selecting the characteristic combination which meets the requirement according to the characteristic importance sorting result.

62) A random forest classifier (50 trees) is trained using training sample data.

63) And classifying the experimental data of the fused point cloud characteristics and the waveform characteristics by using a trained random forest classifier to obtain the category of each laser point.

64) Evaluating the accuracy change of ground feature classification carried out by different feature combinations, analyzing the influence of adding different features on the classification effect of each ground feature type, feeding back to the step a to find out the optimal feature combination, and re-executing the steps 63 and 64 to obtain high-accuracy ground feature classification data.

Therefore, compared with the prior art, the invention has the advantages that:

(1) the aim of realizing high-precision ground feature classification is fulfilled by utilizing two sets of data of the full-wave airborne laser radar and through characteristic analysis and processing, and the airborne laser radar point cloud classification result obtained through the method has a good effect on multi-classification of urban areas.

(2) By fusing point cloud characteristics and waveform characteristics to classify the ground objects, the overall precision and the Kappa coefficient of the ground object classification method can be remarkably improved, the missing classification error and the misclassification error of each ground object type are also remarkably reduced, and a high-precision ground object classification result is obtained.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

FIG. 1 is a schematic flow diagram of a full waveform on-board LiDAR ground object classification method of the present invention based on Relieff feature selection;

FIG. 2 is a diagram illustrating the effect of preprocessing full waveform data;

FIG. 3 is a diagram of the effect of Gaussian decomposition on full waveform data;

FIG. 4 is a diagram of a classification effect of a waveform feature and a point cloud feature;

FIG. 5 shows the effect of features on the classification of land features, where 1a-1b are the classifications for land, 2a-2b are the classifications for low and low vegetation, 3a-3b are the classifications for high vegetation, and 4a-4b are the classifications for buildings.

[ detailed description ] embodiments

The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. 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.

Referring to fig. 1, for the method for classifying the ground features of the full-waveform airborne laser radar based on the ReliefF feature selection provided by the embodiment, firstly, the full-waveform airborne laser radar is prepared and installed, the airborne laser radar data is generated, then the full-waveform data is preprocessed, noise influence is removed, the preprocessed full-waveform data is subjected to gaussian decomposition, echo parameters are obtained, an LM algorithm is used for optimization, point cloud features are extracted by setting different geometric neighborhoods, and the echo parameters optimized by the LM algorithm are used for obtaining the waveform features. Then point cloud characteristics in the on-board laser radar data and waveform characteristics of the full waveform data are fused, and importance of each characteristic is evaluated by utilizing a Relieff algorithm; and finally, according to the feature importance sorting result, selecting features, analyzing the influence of various feature combinations on different ground feature sorting effects, and sorting the ground features by using a random forest classifier.

The method for classifying the features of the full-waveform airborne laser radar based on the ReliefF feature selection according to the embodiment will be described in detail with reference to fig. 1. The method comprises the following specific steps:

step 1, carrying a set of full-waveform LiDAR system on an aviation carrier, wherein the full-waveform LiDAR system comprises an Inertial Measurement Unit (IMU), a differential Global Positioning System (GPS) (DGPS), a laser scanning distance measurement system and an imaging device.

And 2, performing data acquisition by using the full-waveform airborne laser radar in the step 1 to obtain two sets of data, namely airborne laser radar data generated by the full-waveform airborne laser radar and full-waveform data.

And 3, reading corresponding WDP full waveform data according to the LAS 1.3 and version-up airborne laser radar data more than 1.3 obtained in the step 2.

And 4, preprocessing the full-waveform airborne laser radar data obtained in the step 3, and removing noise influence.

And 5, carrying out Gaussian decomposition on the full waveform data preprocessed in the step 4 to obtain each echo parameter and optimizing the echo parameters through an LM algorithm.

And 6, extracting point cloud characteristics by setting different geometric neighborhoods, and acquiring waveform characteristics by using the echo parameters optimized in the step 5.

And 7, fusing the point cloud characteristics in the airborne laser radar data obtained in the step 6 and the waveform characteristics of the full waveform data, and evaluating the importance of each characteristic by using a Relieff algorithm.

And 8, selecting features according to the feature importance ranking result in the step 7, analyzing the influence of various feature combinations on different ground feature classification effects, and classifying the ground features by using a random forest classifier.

In the step 4, the full-waveform airborne laser radar data is preprocessed, and the steps and the calculation method for removing the noise influence are as follows:

and 4.1, setting the data average value of 5 percent of the echo data before and after as a standard noise value.

And 4.2, determining a noise threshold according to a 3 sigma principle, and removing background noise in the data.

And 4.3, adopting a Gaussian template with the window size of 5 to carry out smooth filtering processing on the data subjected to threshold denoising, and removing random noise in the data.

In the step 5, the full-waveform airborne laser radar data is subjected to Gaussian decomposition, and the steps of obtaining each echo parameter and optimizing through the LM algorithm and the calculation method are as follows:

and 5.1, detecting the echo number, solving a first derivative of the preprocessed waveform data, and counting the number of zero crossing points of the first derivative to be used as the echo number.

Step 5.2, calculating initial values of the waveform parameters, solving a second derivative of the preprocessed waveform data, and obtaining a position l of a continuous inflection point_2i-1And l_2i. Therefore, the peak position and the wave width of the ith echo can be calculated by the formula (1) and the formula (2), and the initial value of the peak position

Corresponding amplitude as peak

And 5.3, optimizing the initial values of the parameters by using an LM algorithm to ensure that the iterative residual sum of squares ξ^Tξ is smaller than the preset threshold, the concrete steps are as follows:

step 5.3.1, let x be the same as R_mIs a parameter vector (R)_mColumn vector of length m in real field), y ∈ R_nIs an observation vector (R)_nColumn vector of length n in real domain), any parameter x can be used to calculate corresponding estimation value by function f(see equation 3), ξ is the residual, which is determined by the observed y and estimated values

The difference between them is calculated (see equation 4).

Step 5.3.2, for a sufficiently small valueF (x) can be linearly expanded by taylor series, as in equation (5).

Wherein J is the Jacobian matrix

Step 5.3.3, finding a local optimum value in each iteration process

Ultimately minimizing the result of equation (6):

step 5.3.4, calculate by equation (6)The values of (a) are improved for each parameter value, see equation (7).

Wherein x_i-1Obtaining a parameter value for a previous iteration; x is the number of_iThe correction value for the current iteration.

5.3.5, calculating residual ξ after the iteration by using the corrected parameter value, if the sum of the squares of the residual ξ is present^Tξ is less than the preset threshold value, otherwise the above steps 5.3.2-5.3.4 are repeated.

In the step 6, the step of extracting the point cloud features and the waveform features and the calculation method are as follows:

and 6.1, extracting the characteristic step number with a good distinguishing effect on the building from the point cloud data of the airborne laser radar by using the grid neighborhood.

6.2, extracting elevation related features from the airborne laser radar point cloud data based on the cylinder neighborhood, wherein the cylinder neighborhood can be described by a formula (8):

wherein R is_cylinderRadius of cylinder neighborhood, (x)_i,y_i) (x) is the plane coordinate of the current point_i,y_i) Is the coordinates of the search point.

And 6.2.1, extracting the maximum positive height difference characteristic, namely the height difference between the laser foot point which is highest in the current cylinder neighborhood and is higher than the current point and the current point.

And 6.2.2, extracting the maximum negative height difference characteristic, namely the height difference between the laser foot point which is lowest in the current cylinder neighborhood and is lower than the current point and the current point.

And 6.2.3, extracting the height change range characteristics and the height difference between the highest laser foot point and the lowest point in the current cylinder neighborhood.

And 6.2.4, extracting elevation variance characteristics and the variance value of the elevation in the neighborhood of the current point.

Step 6.2.5, extracting the height difference mean feature, the height difference between the current point elevation and the mean of all points in the cylinder neighborhood.

And 6.2.6, extracting height difference deviation degree characteristics, reflecting the symmetry condition of height distribution in the neighborhood by the deviation degree of the height difference between the current point and all points in the neighborhood of the cylinder, and calculating according to a formula (9).

In the formula, E represents the third-order normalized moment, X represents the elevation of all points in the neighborhood, mu is the mean value of the elevation of the neighborhood, and sigma is the standard deviation of the elevation.

And 6.2.7, extracting the height difference kurtosis characteristic, describing the steep degree of the elevation distribution form in the neighborhood by the kurtosis of the height difference between the current point and all points in the neighborhood of the cylinder, and calculating according to a formula (10).

In the formula, x_iIs the elevation value of the ith laser foot point,

is the mean elevation of the neighborhood, n is the inner package of the neighborhoodThe number of points contained.

Step 6.3, the section of the cylinder neighborhood is divided into a plurality of grids according to a certain height (0.5m), and three different characteristics are extracted, wherein the method specifically comprises the following steps:

and 6.3.1, extracting the non-empty grid number characteristic which is the number of the non-empty grids in the statistical section view.

And 6.3.2, extracting maximum elevation variance characteristics, if the value of the number of the non-empty grids is not less than 8, calculating the elevation variance values of point clouds contained in the three grids with the maximum elevation values, and otherwise, the value of the characteristics is 0.

And 6.3.3, extracting the maximum grid elevation variance characteristic which is the elevation variance of the data in the grid with the most points and represents the dispersion degree of the laser foot points in the area.

6.4, extracting density correlation characteristics of the airborne laser radar point cloud data based on the sphere neighborhood, and specifically comprising the following steps:

step 6.4.1, extract the point density feature, this feature represents the laser foot point density in the neighborhood of the cylinder, calculate see equation (11).

In the formula, N_cylinderThe number of the laser foot points in the cylinder neighborhood is shown, r is the radius of the cylinder neighborhood, and h is the elevation change range, namely the elevation difference between the highest point and the lowest point.

And 6.4.2, extracting a point density ratio characteristic which is the ratio of the point densities of the cylinder neighborhood and the sphere neighborhood and can be calculated by a formula (12).

Wherein D is_sThe laser foot point density in the sphere neighborhood is calculated in formula (13).

In the formula, N_sphereThe number of the laser foot points in the sphere neighborhood.

6.5, extracting the waveform characteristics containing rich physical information based on the echo parameters optimized by the LM algorithm, and specifically comprising the following steps:

step 6.5.1, amplitude features are extracted, which vary with the radiation and geometric characteristics of different objects. Parameter A optimized by LM Algorithm in step 5_i(echo amplitude) representation.

At step 6.5.2, a peak position feature is extracted representing the time interval between the position of the response of the transmit pulse to the target and the initial recording of the echo pulse. Parameter mu optimized by LM Algorithm in step 5_i(echo peak position) representation.

And 6.5.3, extracting half-wave width characteristics which are closely related to the shape of the earth surface. Parameter omega optimized by LM algorithm in step 5_i(half wave width of echo) representation.

And 6.5.4, extracting the echo intensity characteristic intensity, representing the energy reflected by the target to the sensor, and calculating according to a formula (14).

In the formula A_iRepresenting the amplitude, ω, of the ith echo_iHalf wave width representing ith echo waveform

And 6.5.4, calculating the characteristic of the backscattering coefficient, namely the effective collision area of the laser pulse and the ground according to a formula (15).

In the formula, C_calFor the scaling constant, R is the distance of the laser beam from the ground, A is the amplitude of the incident waveform, w is the half-wave width of the incident waveform, Γ is the gamma function representing the effect of the sensor on the echo, and α is the angle between the horizontal plane and the normal, i.e., the ground slope.

In the step 7, the step of fusing the point cloud characteristics and the waveform characteristics and using the Relieff algorithm to perform importance evaluation and the calculation method are as follows:

and 7.1, selecting a part of representative experimental data to perform manual classification to serve as a training sample.

7.2, fusing point cloud features and waveform features in the training sample data, and evaluating the importance of each feature by using a Relieff algorithm, wherein the specific steps are as follows:

and 7.2.1, setting the weight of all point cloud features and waveform features to be 0.

Step 7.2.2, randomly selecting a training instance R.

And 7.2.3, searching K neighbor (K is set to be 10) samples H from the samples of the same class at the current point.

At step 7.2.4, K neighbor (K set to 10) samples M are found from samples of different classes at the current point.

At step 7.2.5, the distances between the current sample R and the features of sample H and sample M are calculated, respectively.

Step 7.2.6, if the distance between R and H in a certain feature is greater than R and M, the feature is considered to be beneficial for distinguishing same type from different types, and the weight of the feature is enhanced; on the contrary, if the distance between R and H in a certain feature is smaller than that between R and M, the feature is considered to have a negative effect on distinguishing the same class from different classes, and the weight of the feature is reduced.

Step 7.2.7, repeating the above steps 7.2.2-7.2.6 m times, and finally obtaining the average weight of each feature. The larger the weight value is, the stronger the classification ability of the feature is, whereas the weaker the classification ability of the feature is.

And 7.3, sequencing the feature weights acquired in the step 7.3.

In the step 8, the step of analyzing the influence of different feature combinations on the ground feature classification effect and classifying the ground features by using a random forest classifier and the calculation method are as follows:

and 8.1, selecting different feature combinations according to the feature sorting result in the step 7.

And 8.2, training the random forest classifier (50 trees) by using the training sample data.

And 8.3, classifying the experimental data by using the trained random forest classifier to acquire the class information of each laser point.

And 8.4, performing precision evaluation on the classification result, analyzing the influence of each characteristic on different ground feature classification effects, feeding back to the step 8.1 to find out an optimal characteristic combination, and re-executing the steps 8.2 and 8.3 to obtain high-precision ground feature classification data.

As shown in fig. 2, 3, 4 and 5, the steps of full waveform data preprocessing, gaussian decomposition, feature fusion classification, and the influence of each feature on the classification effect of the feature are illustrated.

In fig. 2, the original waveform (solid line) has two parts, namely background noise and random noise, which are mainly represented by small jitter, and the small jitter can seriously hinder the judgment of the number of gaussian function variables. Threshold denoising (dotted line) generates a good processing effect on background noise, removes the jitter of the head and tail of the original waveform, and smooth filtering (dot line graph) removes random noise to make the waveform smoother and maximally reserve the original waveform information.

In fig. 3, the full waveform gaussian decomposition results, two gaussian components (see two dotted lines) generated after decomposition are better fitted to the pre-processed full waveform data (solid line), and the information contained in the full waveform data can be completely described.

In fig. 4, the classification results of the fused waveform feature and the point cloud feature on four types of ground objects such as ground, low vegetation, high vegetation and buildings are basically consistent with the real type of the point cloud.

In fig. 5, according to the result of feature sorting performed by the ReliefF algorithm, features are gradually added to the classifier, and the influence of each feature on the classification effect of different surface features is analyzed to obtain an optimal feature combination. Analysis shows that the misclassification error and the misclassification error of four types of ground objects are integrally downward along with the addition of the features, but the two types of errors are basically stable when the first six features are added, so that the dimensionality of a feature space is greatly reduced after the Relieff algorithm is improved, and the fast, effective and high-precision ground object classification of airborne laser radar point cloud data can be realized.

The method can effectively improve the automatic classification precision of the airborne laser radar data points, provides high-quality basic data for the fields of environmental monitoring, geological survey, agricultural survey, urban and rural planning and the like, and has a good effect on multi-classification of urban areas.

The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.