CN106643671A - Underwater point cloud denoising method based on airborne LiDAR depth sounding system - Google Patents
Underwater point cloud denoising method based on airborne LiDAR depth sounding system Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C13/00—Surveying specially adapted to open water, e.g. sea, lake, river or canal
- G01C13/008—Surveying specially adapted to open water, e.g. sea, lake, river or canal measuring depth of open water
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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
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Abstract
The invention provides an underwater point cloud denoising method based on an airborne LiDAR depth sounding system. The underwater point cloud denoising method is characterized by comprising the following steps: analyzing a waveform and reading waveform information of a point cloud; judging the type of the point cloud through analyzing the waveform information; judging that the point cloud is an underground topographic point, a suspicious point or a noise point and filtering the noise point; verifying depth sounding performance parameters and carrying out depth sounding performance parameter verification on the judged suspicious point; verifying and judging that the suspicious point is the underground topographic point or the noise point and filtering the noise point; carrying out elevation mean-deviation iterative denoising; and carrying out the elevation mean-deviation iterative denoising on the judged underground topographic point to obtain a final underground topographic point. The method provided by the invention can be used for effectively eliminating block-shaped noises; and the reliability of a denoising result is improved and the extraction precision of an underwater topography is guaranteed.
Description
Technical field
The invention belongs to Surveying Science and Technology field, is related to a kind of point cloud denoising method, it is specially a kind of based on airborne
The point cloud denoising method under water of LiDAR sounding systems.
Background technology
Airborne LiDAR (Light Detection And Ranging, laser acquisition and measurement) sounding system belongs to actively
Sounding system, is the method using scanning survey mode from air-launched laser to be taken sounding, and sounding system is being carried out
Can launch the laser pulse of two kinds of wavelength of infrared light and blue green light during measurement operation, its middle-infrared band be difficult to penetrate seawater so as to
Detect and just carry out after the water surface echo, and decay of blue green light of the wavelength between 0.47～0.58 μm when penetrating seawater is most
Little, penetrability is strong and good directionality, therefore bluish-green wave band may detect seabed.Its in the stretch of coastal water for being shallower than 50 meters, with can not
The superiority of analogy, particularly efficiently can quickly measure the waters that Shallow islands submerged reef and ship cannot arrive safe and sound, and be sea
The periodicity of bank band, answer acute monitoring provide new technological means.Due to equipment precision restriction, scattering impact and water
, inevitably there are many rough errors in the three-dimensional point cloud for causing the collection of LiDAR sounding systems in reflection characteristic of body floating material etc.
Point, as noise spot, if by these noise points deletings, the extraction of later stage underwater topography will certainly do not affected.Therefore, cloud denoising is put
It is requisite important step in whole LiDAR depth measurements Point Cloud Processing flow process.
At present, the method for putting cloud denoising is both for greatly the three-dimensional point cloud of traditional infrared LiDAR height-finding system collection, noise
Point accounting is few, it is discrete to be distributed, can preliminary cancelling noise point by a distribution of cloud height value.Elevation deviation from mean method is base
Point cloud denoising is realized in Principle of Statistics, the thought of the method is：The number of noise is much smaller than the number of non-noise point, therefore counts
Calculate institute's elevation average a little in region, then count each put and elevation average difference, finally according to depth displacement whether
Noise spot is detected more than threshold value set in advance as criterion and rejected.
Elevation deviation from mean method has to isolated spotted noise and preferably reject effect, but to the rejecting effect of block distortion
It is undesirable, especially when noise spot is distributed close situation when non-noise point is distributed, need preset threshold value, and the threshold value sets
It is fixed to be difficult to determine, not with universality.
Additionally, airborne LiDAR sounding systems are when sounding the depth of the water, water body is main transmission medium, as transmission medium,
Water body is increasingly complex for the impact of laser compared to air, and in addition to scattering, also water refraction, water body are for the decay of laser
Deng especially for complicated water quality environment, relying only on traditional point cloud noise eliminating method and be readily detected wrong noise
Point.
The content of the invention
The present invention is for denoising method in background technology is undesirable for the rejecting effect of block distortion, threshold value is difficult to set
The problem poor with denoising reliability, it is proposed that a kind of preferable for the rejecting effect of the block distortion and high base of denoising reliability
In the point cloud denoising method under water of airborne LiDAR sounding systems.
A kind of point cloud denoising method under water based on airborne LiDAR sounding systems, it is characterised in that comprise the following steps：
S100：Waveform analysis, reads the shape information of point cloud, by the class for judging the analysis of shape information a cloud
Type, by a cloud underwater topography point, suspicious points or noise spot are judged to, filter noise spot；
S200：The suspicious points obtained after step S100 judges are carried out depth measurement performance ginseng by the checking of depth measurement performance parameter
Number checking, suspicious points are judged as underwater topography point or noise spot by checking, filter noise spot；
S300：Elevation deviation from mean iterated denoising, by obtain after step S100 and step S200 judge under water
Form point carries out elevation deviation from mean iterated denoising, obtains final underwater topography point.
Further, the shape information described in step S100 is the echo strength of bluish-green laser.
Further, the step S100 waveform judges to be specially：
S101：Determine judgment threshold；
S102：Determine zoom ranges x；
S103：Determine decision condition, carry out the judgement of underwater topography point, noise spot and suspicious points：If bluish-green laser echo
Intensity then judges that the point is underwater topography point in the range of (δ * (100%+x%) ,+∞), if bluish-green laser echo strength (-
∞, δ * (100%-x%)) in the range of, then the point is judged as noise spot, if bluish-green laser echo strength is in [δ * (100%-
), x% δ * (100%+x%)] point of scope is judged to suspicious points.
Further, zoom ranges x are 20.
Further, the judgment threshold is the echo strength threshold value of bluish-green laser, and the judgment threshold is by following several
Individual parameter determination：Lasing intensity, the unrestrained attenuation coefficient of water, water-bottom reflectivity, atmospheric effect and sounding system device parameters.
Further, the judgment threshold includes deep water passage judgment threshold or shallow water passage judgment threshold.
Further, the step S200 depth measurement performance parameter checking is specially：
S201：Determine Laser bathymetry depth measurement performance standard coefficient k；
S202：Determine the unrestrained attenuation coefficient of water；
S203：Calculate depth capacity D that sounding system can be detectedmax, specially
S204：By the water depth value h and D of the suspicious points obtained after step S100 judgesmaxIt is compared, if h is ＞ Dmax,
Then the point is judged into noise spot, if h≤Dmax, then the point is judged into underwater topography point.
Further, the step S300 elevation deviation from mean iterated denoising is specially：
S301：Calculate the elevation average of all underwater topography pointsWherein, μiFor under water
The height value of form point, n is underwater topography point quantity；
S302：Calculate error in the height value of all underwater topography pointsWherein, m is iteration
Number of times；
S303：Determine noise spot judgment threshold θth, determine iteration ends threshold θ；
S304：Noise spot judgement, if the height value of underwater topography point meets condition, is judged to noise spot；
S305：Repeat step S501～step S504, calculates middle error σ obtained in each repetitive processmWith the last time
Middle error σ obtained in repetitive processm-1Ratio, if terminating repetitive process；
S306：The noise spot by judging to obtain is filtered, the point of the underwater topography after noise spot will be filtered as finally under water
Topographic(al) point.
Further, noise spot judgment threshold θ in step S303thFor 2 σ.
Further, iteration ends threshold θ is 90% in step S303.
Proposed by the present invention cloud denoising method is the cloud data under water gathered for airborne LiDAR sounding systems, is led to
Cross waveform analysis and tentatively judge underwater topography point, noise spot and suspicious points, this step plays guiding function to a cloud denoising；By surveying
The deeper suspicious points of the depth of water in deep performance parameter checking previous step, finally by the underwater topography point obtained to above-mentioned two step
Carry out elevation deviation from mean iterated denoising to complete the rejecting of whole noise spot and the extraction work of final underwater topography point.For
The cloud data of airborne LiDAR sounding systems collection, point cloud denoising method under water proposed by the invention and conventional point cloud denoising
Method is compared, and using waveform analysis denoising, effectively rejects block distortion；During elevation deviation from mean iterated denoising, adopt
Adaptive threshold is iterated denoising, and judgment threshold has universality, improves the reliability of denoising result, ensure that under water
Landform extraction accuracy, with more preferable denoising effect.
Description of the drawings
Fig. 1 is the step flow chart of the present invention；
Fig. 2 is waveform analysis flow chart of steps of the present invention；
Fig. 3 is depth measurement performance parameter verification step flow chart of the present invention；
Fig. 4 is elevation deviation from mean iterated denoising flow chart of steps of the present invention；
The test cloud data (elevation section) that Fig. 5 is gathered by the CZMIL sounding systems of selection；
Fig. 6 is design sketch after a cloud denoising to be carried out to test data without improved elevation deviation from mean method (elevation is cutd open
Face)；
Fig. 7 carries out design sketch (elevation section) after the cloud denoising of step S100 to test data for the inventive method；
Fig. 8 carries out design sketch (elevation section) after the point cloud denoising of step S200 to test data for the inventive method；
Fig. 9 carries out design sketch (final underwater topography point after the point cloud denoising of step S300 to test data for the inventive method
Elevation section).
Specific embodiment
Below in conjunction with the accompanying drawings and embodiment, technical scheme is described in detail.
Embodiment 1：
Airborne LiDAR (Light Detection And Ranging, laser acquisition and measurement) sounding system belongs to actively
Sounding system, is the method using scanning survey mode from air-launched laser to be taken sounding, and sounding system is being carried out
Can launch the laser pulse of two kinds of wavelength of infrared light and blue green light during measurement operation, its middle-infrared band be difficult to penetrate seawater so as to
Detect and just carry out after the water surface echo, and decay of blue green light of the wavelength between 0.47～0.58 μm when penetrating seawater is most
Little, penetrability is strong and good directionality, therefore bluish-green wave band may detect seabed, by calculating infrared laser pulses and bluish-green laser arteries and veins
The echo time of punching differs to calculate depth under water.The method in the stretch of coastal water for being shallower than 50 meters, with unrivaled superiority,
Particularly efficiently can quickly measure the waters that Shallow islands submerged reef and ship cannot arrive safe and sound, be littoral zone periodicity,
Acute monitoring is answered to provide new technological means.
The present invention is a kind of point cloud denoising method under water based on airborne LiDAR sounding systems, and proposed by the invention goes
Method for de-noising is improved to traditional elevation deviation from mean method, in order to make up the method to the rejecting effect of block distortion not
Foot, the approximate location for first carrying out waveform analysis to cloud data to determine noise spot and underwater topography point is distributed, then by depth measurement
The performance parameter rub deeper to the depth of water is rejected, and finally by the continuous iteration of elevation deviation from mean the mesh of denoising is reached
's.Steps flow chart is as shown in figure 1, comprise the following steps that:
S100：Waveform analysis, reads the shape information of point cloud, by the class for judging the analysis of shape information a cloud
Type, by a cloud underwater topography point, suspicious points or noise spot are judged to.
S200：The suspicious points obtained after step S100 judges are carried out depth measurement performance ginseng by the checking of depth measurement performance parameter
Number checking, suspicious points are judged as underwater topography point or noise spot by checking.
S300：Elevation deviation from mean iterated denoising, by obtain after step S100 and step S200 judge under water
Form point carries out elevation deviation from mean iterated denoising, obtains final underwater topography point.
Point cloud denoising method proposed by the invention puts the principle that cloud is obtained under water first, according to the waveform of a cloud
Feature is analyzed judgement, and the bluish-green laser of some strength, the bluish-green laser are launched when airborne laser sounding system sounds the depth of the water
The laser intensity returned through seawater Jing sub-bottom reflections is just considered underwater topography point more than certain threshold value, therefore, in this reality
In applying example, the echo strength of the shape information for laser bluish-green laser of judgment threshold is used for during waveform analysis, if laser
The waveform of echo is the waveform of infrared laser and bluish-green laser band overlapping, then carry out judging the intensity of second echo, i.e., blue
The echo strength of green laser.
The step of waveform analysis, flow process was as shown in Fig. 2 comprise the following steps that：
S101：Determine judgment threshold δ, in the present embodiment, CZMIL (Coastal Zone Mapping and Imaging
LiDAR) judgment threshold of sounding system can be divided into deep water passage judgment threshold and shallow water passage judgment threshold, wherein deep water passage
Threshold value is 200, and shallow water passage threshold value is 50.The judgment threshold is by following parameter determination：Lasing intensity, water
Unrestrained attenuation coefficient, water-bottom reflectivity, atmospheric effect and sounding system device parameters.
S102：Determine scaling x, the accuracy for guaranteeing to judge by arranging scaling, in the present embodiment, scaling
Ratio value is 20, i.e., positive and negative 20% scaling is carried out to judgment threshold.
S103：Determine decision condition, carry out the judgement of underwater topography point, noise spot and suspicious points：If bluish-green laser echo
Intensity then judges that the point is underwater topography point in the range of (δ * (100%+x%) ,+∞), if bluish-green laser echo strength (-
∞, δ * (100%-x%)) in the range of, then the point is judged as noise spot, if bluish-green laser echo strength is in [δ * (100%-
), x% δ * (100%+x%)] point of scope is judged to suspicious points.
Because the suspicious points major part obtained after the waveform analysis of step S100 is the deeper class underwater topography point of the depth of water, because
This, can be further determined whether as noise spot by LiDAR depth measurement performance parameters.
The step of depth measurement performance parameter is verified flow process is as shown in figure 3, comprise the following steps that：
S201：Determine Laser bathymetry depth measurement performance standard coefficient k, in the present embodiment, k is CZMIL (Coastal
Zone Mapping and Imaging LiDAR) sounding system typical coefficient, be worth for 4.0.
S202：Determine the unrestrained attenuation coefficient of water, in the present embodiment, data field Kd values are about 0.5m-1。
S203：Calculate depth capacity D that sounding system can be detectedmax, speciallyThe present embodiment is fallen into a trap
Calculate DmaxFor 8 meters.
S204：By the water depth value h and D of the suspicious points obtained after step S100 judgesmaxIt is compared, if h is ＞ Dmax,
Then the point is judged into noise spot, if h≤Dmax, then the point is judged into underwater topography point.
The checking of depth measurement performance parameter can adopt above step, may also be employed filling in gram disk depth data proof method.
Judge in the underwater topography point for obtaining it is possible to there is only a few noise through step S100 and step S200
Point, therefore, filtration denoising is carried out using the method for elevation deviation from mean iteration.If there is partial noise point method can pick accordingly
Remove, otherwise without noise spot, due to the intensive of a cloud, the extraction of overall underwater topography is not affected using the method.The present invention
The elevation deviation from mean iterated denoising method of middle employing is the improvement to traditional elevation deviation from mean denoising method, and the method is not pre-
Depth displacement threshold value is first set, but judgment threshold is used as by error in 2 times, in automatic fitration and constantly iteration depth displacement by mistake
Difference so as to the cancelling noise point to greatest extent while point cloud transition ride comfort is ensured.
The step of elevation deviation from mean iterated denoising, flow process was as shown in figure 4, comprise the following steps that：
S301：Calculate the elevation average of all underwater topography pointsWherein, μiFor under water
The height value of form point, n is underwater topography point quantity.
S302：Calculate error in the height value of all underwater topography pointsWherein, m is iteration
Number of times.
S303：Determine noise spot judgment threshold θth, determine iteration ends threshold θ, in the present embodiment, noise spot judgment threshold
θthFor 2 σ, iteration ends threshold θ is 90%.
S304：Noise spot judgement, if the height value of underwater topography point meets condition, is judged to noise spot.
S305：Repeat step S501～step S504, calculates middle error σ obtained in each repetitive processmWith the last time
Middle error σ obtained in repetitive processm-1Ratio, if terminating repetitive process.
S306：The noise spot by judging to obtain is filtered, the point of the underwater topography after noise spot will be filtered as finally under water
Topographic(al) point.
Choose the water of one group of CZMIL (Coastal Zone Mapping and Imaging LiDAR) sounding system collection
Numeric field data carries out noise points deleting test, and wherein Fig. 5 is original point cloud data (elevation section), and Fig. 6 is without improved elevation
Design sketch (elevation section) after the method points cloud denoising of deviation from mean, wherein threshold value are set to 0.5 meter, and Fig. 7, Fig. 8 and Fig. 9 are this
Design sketch (elevation after the point cloud denoising Jing after the process of step S100, step S200 and step S300 of invention methods described
Section).
Comparison of test results understands：Noise is easily received when elevation average is calculated without the method for improved elevation deviation from mean
Point affects very big deviation occur to cause denoising effect undesirable, particularly when the distribution of noise spot it is close in underwater topography point minute
During cloth；A cloud denoising result is carried out using the method for the invention more reliable, more can accurately extract underwater topography data.
It should be appreciated that above by preferred embodiment be to the explanation that technical scheme is carried out it is schematic rather than
Restricted.One of ordinary skill in the art can be to the technical side described in each embodiment on the basis of above-described embodiment
Case is modified, or carries out equivalent to which part technical characteristic；And these modifications or replacement, all should cover at this
Within the protection domain of invention.Therefore, protection scope of the present invention should be defined by scope of the claims.
Claims (10)
1. a kind of point cloud denoising method under water based on airborne LiDAR sounding systems, it is characterised in that comprise the following steps：
S100：Waveform analysis, reads the shape information of point cloud, by the type for judging the analysis of shape information a cloud, will
Point cloud is judged to underwater topography point, suspicious points or noise spot, filters noise spot；
S200：Depth measurement performance parameter is verified, the suspicious points obtained after step S100 judges is carried out into depth measurement performance parameter and is tested
Card, suspicious points are judged as underwater topography point or noise spot by checking, filter noise spot；
S300：Elevation deviation from mean iterated denoising, the underwater topography point that will be obtained after step S100 and step S200 judge
Elevation deviation from mean iterated denoising is carried out, final underwater topography point is obtained.
2. a kind of point cloud denoising method under water based on airborne LiDAR sounding systems according to claim 1, its feature exists
In the shape information described in step S100 is the echo strength of bluish-green laser.
3. a kind of point cloud denoising method under water based on airborne LiDAR sounding systems according to claim 2, its feature exists
In the step S100 waveform judges to be specially：
S101：Determine judgment threshold；
S102：Determine zoom ranges x；
S103：Determine decision condition, carry out the judgement of underwater topography point, noise spot and suspicious points：If bluish-green laser echo strength
In the range of (δ * (100%+x%) ,+∞), then judge that the point is underwater topography point, if bluish-green laser echo strength (- ∞,
δ * (100%-x%)) in the range of, then the point is judged as noise spot, if bluish-green laser echo strength is in [δ * (100%-x%), δ *
(100%+x%)] point of scope is judged to suspicious points.
4. a kind of point cloud denoising method under water based on airborne LiDAR sounding systems according to claim 3, its feature exists
In zoom ranges x are 20.
5. a kind of point cloud denoising method under water based on airborne LiDAR sounding systems according to claim 3, its feature exists
In the judgment threshold is the echo strength threshold value of bluish-green laser, and the judgment threshold is by following parameter determination：Laser
Emissive porwer, the unrestrained attenuation coefficient of water, water-bottom reflectivity, atmospheric effect and sounding system device parameters.
6. a kind of point cloud denoising method under water based on airborne LiDAR sounding systems according to claim 3, its feature exists
In the judgment threshold includes deep water passage judgment threshold or shallow water passage judgment threshold.
7. a kind of point cloud denoising method under water based on airborne LiDAR sounding systems according to claim 1, its feature exists
In the step S200 depth measurement performance parameter checking is specially：
S201：Determine Laser bathymetry depth measurement performance standard coefficient k；
S202：Determine the unrestrained attenuation coefficient of water；
S203：Calculate depth capacity D that sounding system can be detectedmax, specially
S204：By the water depth value h and D of the suspicious points obtained after step S100 judgesmaxIt is compared, if h is ＞ Dmax, then will
The point is judged to noise spot, if h≤Dmax, then the point is judged into underwater topography point.
8. a kind of point cloud denoising method under water based on airborne LiDAR sounding systems according to claim 1, its feature exists
In the step S300 elevation deviation from mean iterated denoising is specially：
S301：Calculate the elevation average of all underwater topography points(i=1,2 ... n), wherein, μiFor underwater topography point
Height value, n be underwater topography point quantity；
S302：Calculate error in the height value of all underwater topography pointsWherein, m is the secondary of iteration
Number；
S303：Determine noise spot judgment threshold θth, determine iteration ends threshold θ；
S304：Noise spot judgement, if the height value of underwater topography point meets condition, is judged to noise spot；
S305：Repeat step S501～step S504, calculates middle error σ obtained in each repetitive processmRepeated with the last time
Middle error σ obtained in journeym-1Ratio, if terminating repetitive process；
S306：The noise spot by judging to obtain is filtered, the point of the underwater topography after noise spot will be filtered as final underwater topography
Point.
9. a kind of point cloud denoising method under water based on airborne LiDAR sounding systems according to claim 8, its feature exists
In noise spot judgment threshold θ in step S303thFor 2 σ.
10. a kind of point cloud denoising method under water based on airborne LiDAR sounding systems according to claim 8, its feature
It is that iteration ends threshold θ is 90% in step S303.
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Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108037509A (en) * | 2017-12-12 | 2018-05-15 | 辽宁科技大学 | A kind of undersea detection method and device |
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| CN109799494A (en) * | 2017-11-17 | 2019-05-24 | 中国林业科学研究院资源信息研究所 | A kind of quick noise-removed filtering method of spaceborne photon counting laser radar data |
| 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 |
| CN111144318A (en) * | 2019-12-27 | 2020-05-12 | 苏州联视泰电子信息技术有限公司 | Point cloud data noise reduction method for underwater sonar system |
| CN112136018A (en) * | 2019-04-24 | 2020-12-25 | 深圳市大疆创新科技有限公司 | Point cloud noise filtering method of distance measuring device, distance measuring device and mobile platform |
| CN114111628A (en) * | 2021-12-07 | 2022-03-01 | 西安理工大学 | Three-dimensional reconstruction algorithm for underwater target laser point cloud data |
| CN114509050A (en) * | 2021-12-31 | 2022-05-17 | 广州南方卫星导航仪器有限公司 | Water depth data processing method and system |
| CN115422981A (en) * | 2022-11-04 | 2022-12-02 | 自然资源部第一海洋研究所 | Land and water classification method and system for single-frequency airborne laser sounding data and application |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103745441A (en) * | 2014-01-08 | 2014-04-23 | 河海大学 | Method of filtering airborne LiDAR (Light Detection and Ranging) point cloud |
| CN105264336A (en) * | 2013-04-05 | 2016-01-20 | 洛克希德马丁公司 | Underwater platform with LIDAR and related methods |
| CN105430368A (en) * | 2014-09-22 | 2016-03-23 | 中兴通讯股份有限公司 | Two-viewpoint stereo image synthesizing method and system |
| US20160223671A1 (en) * | 2011-06-30 | 2016-08-04 | The Regents Of The University Of Colorado | Remote measurement of shallow depths in semi-transparent media |
-
2016
- 2016-12-01 CN CN201611088217.8A patent/CN106643671B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160223671A1 (en) * | 2011-06-30 | 2016-08-04 | The Regents Of The University Of Colorado | Remote measurement of shallow depths in semi-transparent media |
| CN105264336A (en) * | 2013-04-05 | 2016-01-20 | 洛克希德马丁公司 | Underwater platform with LIDAR and related methods |
| CN103745441A (en) * | 2014-01-08 | 2014-04-23 | 河海大学 | Method of filtering airborne LiDAR (Light Detection and Ranging) point cloud |
| CN105430368A (en) * | 2014-09-22 | 2016-03-23 | 中兴通讯股份有限公司 | Two-viewpoint stereo image synthesizing method and system |
Non-Patent Citations (2)
| Title |
|---|
| 李凯: "黄海、东海区域漫衰减系数光谱遥感反演及激光测深性能评估", 《遥感学报》 * |
| 李炼: "机载LiDAR点云滤波及分类算法研究", 《中国优秀硕士学位论文全文数据库信息科技辑》 * |
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