CN105676205A - Airborne LiDAR waveform data Gaussian decomposition method - Google Patents
Airborne LiDAR waveform data Gaussian decomposition method Download PDFInfo
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- CN105676205A CN105676205A CN201610056137.8A CN201610056137A CN105676205A CN 105676205 A CN105676205 A CN 105676205A CN 201610056137 A CN201610056137 A CN 201610056137A CN 105676205 A CN105676205 A CN 105676205A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
Abstract
The invention discloses an airborne LiDAR waveform data Gaussian decomposition method. The method is characterized by carrying out modeling on an original waveform signal obtained after denoising through a Gaussian mixture model; determining an initial Gaussian component position by utilizing local maximum; dividing the detected waveform into different types of waveforms according to the detected distance between wave crests, and estimating initial parameters thereof respectively; estimating initial Gaussian components through Gaussian component width transverse step-by-step iteration decomposition; and after removing non-effective initial Gaussian components, optimizing parameters and number of the Gaussian components, and thus realizing precise decomposition of airborne LiDAR waveform data. The method has the following advantages: the method can effectively detect superposition wave and weak wave components in echo signals; through the loop iteration transverse decomposition strategy, the accuracy of estimation of the initial parameters is improved; and the method realizes optimization of the initial estimation, removes the non-effective Gaussian components in the initial estimation, determines the optimal decomposition number of the Gaussian components, and can realize quick and precise decomposition of the airborne LiDAR full waveform data.
Description
Technical field
The present invention relates to the decomposition method of a kind of laser radar echo signal wave, especially relate to the Gauss Decomposition method of the airborne LiDAR Wave data of small light spot.
Background technology
Airborne LiDAR (LightDetectionAndRanging) technology is the remote sensing technology of a quick obtaining earth's surface three-dimensional data, has become acquisition high accuracy DTM at present, and city is three-dimensional, the important means of forest parameters. And the LiDAR system possessing Full wave shape echo registering capacity can record backscatter signal in units of nanosecond. Compare conventional echo recording mode, full waveform recording mode can provide more abundant topographic details, user can pass through to analyze backscatter waveform can obtain the more physical characteristic of object, this has very big application potential for remotely-sensed data interpretation and Land cover investigation, in addition, the analysis of LiDAR Wave data and decomposition computation can also improve pulse detection reliability, precision and earth's surface resolution, and therefore the process of Wave data has become as an important technology during LiDAR data processes.
Existing airborne LiDAR Wave data is analyzed method and can be divided three classes: (1) pulse threshold detection method, and detection mode can be divided into again center of gravity method, first derivative balance method and ordinary differential detection. the advantage of this type of method is that processing speed is fast, but result reliability is largely dependent upon the reasonability of threshold value selection and the quality of echo-signal, is determined the response position of laser footpoint by threshold value, can not obtain the parameter information of waveform. (2) correlation method, it is based on the dependency relation between transmitting pulse waveform and reception impulse waveform to carry out waveform decomposition, echo component in ASDF method detection waveform data and the center such as Roncat, the method can eliminate the impact of noise and ringing effect very well, but miss some important parameter information (3) signal decomposition methods of waveform, this type of method is that with suitable analytical function modeling, echo pulse signal is come inverting waveform shape, the function being used for signal modeling at present mainly has Gaussian function, Generalized Gauss function, logarithm normal distribution, Weibull distribution etc.Gaussian function is most popular parametric equation in Full wave shape LiDAR data signal decomposition, the Gauss Decomposition of waveform generally includes two steps: i) first determine the number of Gaussian component and the parameter of each component, ii) optimization of characteristic parameter and post processing. Wherein haveing a problem in that number and parameter thereof how to determine Gaussian component, existing most methods cannot effectively be decomposed the superposition component in waveform and weak signal component and determine effective Gaussian component number and parameter thereof.
Summary of the invention
The present invention mainly solves the technical problem existing for prior art; Providing a kind of airborne LiDAR Gauss Decomposition method, what retain shape information simultaneously effective eliminates noise signal, the reliability that can be effectively improved in echo-signal the complicated components such as superposition ripple and decompose and accuracy.
Further object of the present invention is to solve the technical problem existing for prior art, provide a kind of waveshape signal transverse direction Gauss Decomposition method, can accurately estimate parameter and the number of various types of waveform Gaussian component, it is achieved that the accurate decomposition of LiDAR Full wave shape information.
The above-mentioned technical problem of the present invention is addressed mainly by following technical proposals:
A kind of airborne LiDAR Wave data Gauss Decomposition method, it is characterised in that comprise the following steps:
Step 1, pre-treatment step: Raw waveform signals is carried out pretreatment, to remove the background noise in waveshape signal and adopt filtering method that echo-signal filtering is removed the random noise in waveshape signal, concrete grammar is:
Step 1.1, calculates the meansigma methods of data of echo-signal last 5% as background noise, and subtracting background noise;
Step 1.2, carries out S-G filtering to the waveshape signal after removing background noise, selects filter window to be sized to 5, and polynomial order is 3, calculates random noise δ according to the root-mean-square error of Wave data before and after filteringnoise;
Step 2, initial estimation step: a detection crest threshold value is set, obvious crest is identified with local maximum, spacing according to adjacent peaks, the waveform detected is divided into obvious superposition ripple, weak superposition ripple and individual waves, estimate its initial parameter respectively, the initial parameter method of estimation of detection waveform is: for obvious superposition ripple, about crest location, find left and right flex point, flex point is tried to achieve by the second order five point value differential of discrete data, initial parameter is determined by left and right corner position, for weak superposition ripple and individual waves, its initial parameter is determined by half-wave width formula,
Step 3, horizontal decomposition step: choose the minimum Gaussian component in obvious superposition ripple medium wave peak position and the weak superposition ripple determined and individual waves matching Wave data, if the maximum of regression criterion is less than crest threshold value, then perform step 4; Otherwise, return step 2 to be iterated decomposing;
Step 4, optimization step: according to the width of the distance between adjacent Gaussian component and Gaussian component as condition, remove invalid initial Gaussian component, remaining Gaussian component matching waveshape signal, if the precision after matching is unsatisfactory for requirement, then add a Gaussian component in optimization process, until meeting predetermined accuracy, realize the decomposition completely of Wave data, specifically include:
Step 4.1, removes invalid Gaussian component, if the width that the waveform component detected regenerates a maximum waveform is maxa_sigma, if adjacent peaks distance is less than 2maxa_sigma, and crest difference is more than maxa/2, then remove the component that crest is less;If adjacent peaks distance is more than 2maxa_sigma, less than 3maxa_sigma, and crest difference is more than maxa/3, then remove the component that crest is less; If initial estimation Gaussian component width is less than δ0/ 2, or more than 2 δ0, then this component is removed; Using amplitude less than the component of certain threshold value as smooth sea, and press the sequence of amplitude size;
Step 4.2, setting accuracy threshold value, removes the waveform component matching waveshape signal after reactive component by previous step, if the root-mean-square error of matching is more than predetermined accuracy threshold value, from smooth sea, then adding a Gaussian component, continuing matching waveform until meeting predetermined accuracy.
In above-mentioned one airborne LiDAR Wave data Gauss Decomposition method, it is characterised in that in described step 2, crest detection threshold value AnIt is set to the random noise of three times, according to Xi-2<Xi-1<Xi>Xi+1>Xi+2&Xi>AnCondition find obvious crest, if not finding obvious crest, then using waveshape signal maximum as crest.
Therefore, present invention have the advantage that 1, can retain shape information simultaneously effective eliminate noise signal, the reliability that can be effectively improved in echo-signal the complicated components such as superposition ripple and decompose and accuracy; 2, parameter and the number of various types of waveform Gaussian component can accurately be estimated, it is achieved that the accurate decomposition of LiDAR Full wave shape information.
Accompanying drawing explanation
Accompanying drawing 1 is the workflow diagram of the present invention.
Accompanying drawing 2 is original waveform data signal schematic representation in the embodiment of the present invention.
Accompanying drawing 3 is initial decomposition Gaussian component schematic diagram in the embodiment of the present invention.
Accompanying drawing 4 is Gaussian component schematic diagram after optimizing in the embodiment of the present invention.
Accompanying drawing 5a is system point cloud multiecho contrast schematic diagram in the embodiment of the present invention.
Accompanying drawing 5b is decomposition point cloud multiecho contrast schematic diagram in the embodiment of the present invention.
Detailed description of the invention
By the examples below, and in conjunction with accompanying drawing, technical scheme is described in further detail.
Embodiment:
A kind of airborne LiDAR waveform data resolving method, as shown in Figure 1, comprises the following steps:
Step 1, carries out pretreatment to Raw waveform signals, and Raw waveform signals is removed the background noise in waveshape signal as shown in Figure 2 and adopts filtering method that echo-signal filtering is removed the random noise in waveshape signal, the concrete operation method of pretreatment is as follows:
Step 1.1, calculates the meansigma methods of data of echo-signal last 5% as background noise, and subtracting background noise;
Step 1.2, carries out S-G filtering to the waveshape signal after removing background noise, selects filter window to be sized to 5, and polynomial order is 3, calculates random noise δ according to the root-mean-square error of Wave data before and after filteringnoise。
Step 2, arranges detection crest threshold value An=3 δnoise, with Xi-2<Xi-1<Xi>Xi+1>Xi+2&Xi>AnObvious crest is found, if not finding obvious crest, then using maximum Maxa as crest, for each obvious crest μ for conditioniIf the distance between its with adjacent obvious crest is less than 4 δ0(δ0For system fire pulse width), then it is labeled as obvious superposition ripple; For obvious superposition crest μi, in certain limit around, find left and right flex point, flex point is tried to achieve by the second order five point value differential formulas of discrete data, is determined the initial parameter of obvious superposition ripple by left and right flex point. For weak superposition ripple and individual waves, find about crest as crest value XiThe position h of the Wave data at half placel, hrIf the i-th-1 Wave data value obtains half more than crest value and i+1 Wave data value obtains half less than crest value, then the position of i-th Wave data value is just as hl, hrValue.Location parameter μ is crest location, by half-wave width formula:Try to achieve left and right width δl、δr, and compare its size, take the less initial value for current Gaussian component δ.
Step 3, choose the Gaussian component minimum by the obvious superposition ripple medium wave peak position obtained in step 2 and the Gaussian component matching Wave data of the weak superposition ripple determined and individual waves, if the maximum of regression criterion is less than crest threshold value, then perform step (4); Otherwise, returning step 2 and be iterated decomposing, initial decomposition Gaussian component is as shown in Figure 3;
Step 4, according to the width of the distance between adjacent Gaussian component and Gaussian component as condition, remove invalid initial Gaussian component, remaining Gaussian component matching waveshape signal, if the precision after matching is unsatisfactory for requirement, then adds a Gaussian component in optimization process, until meeting predetermined accuracy, after optimization, Gaussian component is as shown in Figure 4, it is achieved the decomposition completely of Wave data, and concrete operation method is as follows:
Step 4.1, from the initial Gaussian component obtained by above step, if the width that the waveform component detected regenerates a maximum waveform is maxa_sigma, the condition removing invalid Gaussian component is as follows: if adjacent peaks distance is less than 2maxa_sigma, and crest difference is more than maxa/2, then remove the component that crest is less; If adjacent peaks distance is more than 2maxa_sigma, less than 3maxa_sigma, and crest difference is more than maxa/3, then remove the component that crest is less; If initial estimation Gaussian component width is less than δ0/ 2, or more than 2 δ0, then this component is removed;
Step 4.2, arranges smooth sea detection threshold value At=maxa/6, is divided into Gaussian component obvious ripple and smooth sea according to threshold value, and smooth sea is ranked up according to crest size;
Step 4.3, setting accuracy threshold value ∈=1.5 δnoiseThe obvious waveform component matching Raw waveform signals after reactive component is removed by previous step, if the root-mean-square error of waveshape signal is more than predetermined accuracy threshold value before and after matching, from smooth sea, then adding the maximum Gaussian component of amplitude in fit procedure, continuing matching waveform until meeting predetermined accuracy.
Specific embodiment described herein is only to present invention spirit explanation for example. Described specific embodiment can be made various amendment or supplements or adopt similar mode to substitute by those skilled in the art, but without departing from the spirit of the present invention or surmount the scope that appended claims is defined.
Claims (2)
1. an airborne LiDAR Wave data Gauss Decomposition method, it is characterised in that comprise the following steps:
Step 1, pre-treatment step: Raw waveform signals is carried out pretreatment, to remove the background noise in waveshape signal and adopt filtering method that echo-signal filtering is removed the random noise in waveshape signal, concrete grammar is:
Step 1.1, calculates the meansigma methods of data of echo-signal last 5% as background noise, and subtracting background noise;
Step 1.2, carries out S-G filtering to the waveshape signal after removing background noise, selects filter window to be sized to 5, and polynomial order is 3, calculates random noise δ according to the root-mean-square error of Wave data before and after filteringnoise;
Step 2, initial estimation step: a detection crest threshold value is set, obvious crest is identified with local maximum, spacing according to adjacent peaks, the waveform detected is divided into obvious superposition ripple, weak superposition ripple and individual waves, estimate its initial parameter respectively, the initial parameter method of estimation of detection waveform is: for obvious superposition ripple, about crest location, find left and right flex point, flex point is tried to achieve by the second order five point value differential of discrete data, initial parameter is determined by left and right corner position, for weak superposition ripple and individual waves, its initial parameter is determined by half-wave width formula,
Step 3, horizontal decomposition step: choose the minimum Gaussian component in obvious superposition ripple medium wave peak position and the weak superposition ripple determined and individual waves matching Wave data, if the maximum of regression criterion is less than crest threshold value, then perform step 4;Otherwise, return step 2 to be iterated decomposing;
Step 4, optimization step: according to the width of the distance between adjacent Gaussian component and Gaussian component as condition, remove invalid initial Gaussian component, remaining Gaussian component matching waveshape signal, if the precision after matching is unsatisfactory for requirement, then add a Gaussian component in optimization process, until meeting predetermined accuracy, realize the decomposition completely of Wave data, specifically include:
Step 4.1, removes invalid Gaussian component, if the width that the waveform component detected regenerates a maximum waveform is maxa_sigma, if adjacent peaks distance is less than 2maxa_sigma, and crest difference is more than maxa/2, then remove the component that crest is less; If adjacent peaks distance is more than 2maxa_sigma, less than 3maxa_sigma, and crest difference is more than maxa/3, then remove the component that crest is less; If initial estimation Gaussian component width is less than δ0/ 2, or more than 2 δ0, then this component is removed; Using amplitude less than the component of certain threshold value as smooth sea, and press the sequence of amplitude size;
Step 4.2, setting accuracy threshold value, removes the waveform component matching waveshape signal after reactive component by previous step, if the root-mean-square error of matching is more than predetermined accuracy threshold value, from smooth sea, then adding a Gaussian component, continuing matching waveform until meeting predetermined accuracy.
2. one according to claim 1 airborne LiDAR Wave data Gauss Decomposition method, it is characterised in that in described step 2, crest detection threshold value AnIt is set to the random noise of three times, according to Xi-2<Xi-1<Xi>Xi+1>Xi+2&Xi>AnCondition find obvious crest, if not finding obvious crest, then using waveshape signal maximum as crest.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106154247A (en) * | 2016-06-24 | 2016-11-23 | 南京林业大学 | A kind of multiple dimensioned Full wave shape laser radar data optimizes decomposition method |
| CN110135299A (en) * | 2019-04-30 | 2019-08-16 | 中国地质大学（武汉） | A kind of single band bluish-green laser wave analyzing device and system for shallow water depth measurement |
| CN110488242A (en) * | 2018-05-15 | 2019-11-22 | 宁波傲视智绘光电科技有限公司 | Echo signal processing method and device, radar and storage device |
| CN110673109A (en) * | 2019-11-01 | 2020-01-10 | 自然资源部国土卫星遥感应用中心 | Full waveform data decomposition method for satellite-borne large-light-spot laser radar |
| CN111077532A (en) * | 2019-11-22 | 2020-04-28 | 同济大学 | Surface feature space information acquisition method based on deconvolution and Gaussian decomposition |
| CN112039633A (en) * | 2020-08-25 | 2020-12-04 | 珠海格力电器股份有限公司 | Signal sending method and device and signal receiving method and device |
| CN113203987A (en) * | 2021-07-05 | 2021-08-03 | 成都启英泰伦科技有限公司 | Multi-sound-source direction estimation method based on K-means clustering |
| CN117031442A (en) * | 2023-10-08 | 2023-11-10 | 中国地质大学（武汉） | Laser radar water area topography measurement method and device integrating multichannel waveform data |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102538673A (en) * | 2011-12-19 | 2012-07-04 | 中国林业科学研究院资源信息研究所 | Method for extracting forest stand vertical structure parameters on basis of airborne small-facula LiDAR (light detection and ranging) waveform data |
| US8537338B1 (en) * | 2011-08-24 | 2013-09-17 | Hrl Laboratories, Llc | Street curb and median detection using LIDAR data |
| CN104155638A (en) * | 2014-06-11 | 2014-11-19 | 南京林业大学 | Tree species classification method based on LiDAR (Light Detection and Ranging) false-vertical waveform model |
| CN104180754A (en) * | 2014-07-28 | 2014-12-03 | 南京林业大学 | Inversion method for biophysical property of forest based on LiDAR comprehensive waveform model |
| CN104408018A (en) * | 2014-11-19 | 2015-03-11 | 武汉大学 | Shape-preserving denoising method and system for LiDAR (Light Detection And Ranging) waveform |
| CN104849722A (en) * | 2015-05-08 | 2015-08-19 | 南京林业大学 | Light detection and ranging (LiDAR) waveform comprehensive feature-based individual tree identification method |
-
2016
- 2016-01-27 CN CN201610056137.8A patent/CN105676205A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8537338B1 (en) * | 2011-08-24 | 2013-09-17 | Hrl Laboratories, Llc | Street curb and median detection using LIDAR data |
| CN102538673A (en) * | 2011-12-19 | 2012-07-04 | 中国林业科学研究院资源信息研究所 | Method for extracting forest stand vertical structure parameters on basis of airborne small-facula LiDAR (light detection and ranging) waveform data |
| CN104155638A (en) * | 2014-06-11 | 2014-11-19 | 南京林业大学 | Tree species classification method based on LiDAR (Light Detection and Ranging) false-vertical waveform model |
| CN104180754A (en) * | 2014-07-28 | 2014-12-03 | 南京林业大学 | Inversion method for biophysical property of forest based on LiDAR comprehensive waveform model |
| CN104408018A (en) * | 2014-11-19 | 2015-03-11 | 武汉大学 | Shape-preserving denoising method and system for LiDAR (Light Detection And Ranging) waveform |
| CN104849722A (en) * | 2015-05-08 | 2015-08-19 | 南京林业大学 | Light detection and ranging (LiDAR) waveform comprehensive feature-based individual tree identification method |
Non-Patent Citations (1)
| Title |
|---|
| 刘诏等: ""全波形机载激光数据分解研究"", 《高技术通讯》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN106154247A (en) * | 2016-06-24 | 2016-11-23 | 南京林业大学 | A kind of multiple dimensioned Full wave shape laser radar data optimizes decomposition method |
| CN106154247B (en) * | 2016-06-24 | 2018-07-10 | 南京林业大学 | A kind of multiple dimensioned Full wave shape laser radar data optimizes decomposition method |
| CN110488242A (en) * | 2018-05-15 | 2019-11-22 | 宁波傲视智绘光电科技有限公司 | Echo signal processing method and device, radar and storage device |
| CN110135299A (en) * | 2019-04-30 | 2019-08-16 | 中国地质大学（武汉） | A kind of single band bluish-green laser wave analyzing device and system for shallow water depth measurement |
| CN110135299B (en) * | 2019-04-30 | 2021-07-16 | 中国地质大学（武汉） | Single-waveband blue-green laser waveform analysis method and system for shallow water sounding |
| CN110673109A (en) * | 2019-11-01 | 2020-01-10 | 自然资源部国土卫星遥感应用中心 | Full waveform data decomposition method for satellite-borne large-light-spot laser radar |
| CN110673109B (en) * | 2019-11-01 | 2021-09-21 | 自然资源部国土卫星遥感应用中心 | Full waveform data decomposition method for satellite-borne large-light-spot laser radar |
| CN111077532A (en) * | 2019-11-22 | 2020-04-28 | 同济大学 | Surface feature space information acquisition method based on deconvolution and Gaussian decomposition |
| CN112039633A (en) * | 2020-08-25 | 2020-12-04 | 珠海格力电器股份有限公司 | Signal sending method and device and signal receiving method and device |
| CN113203987A (en) * | 2021-07-05 | 2021-08-03 | 成都启英泰伦科技有限公司 | Multi-sound-source direction estimation method based on K-means clustering |
| CN117031442A (en) * | 2023-10-08 | 2023-11-10 | 中国地质大学（武汉） | Laser radar water area topography measurement method and device integrating multichannel waveform data |
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