CN106643671B - A kind of underwater cloud denoising method based on airborne LiDAR sounding system - Google Patents
A kind of underwater cloud denoising method based on airborne LiDAR 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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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/30—Assessment of water resources
Abstract
The present invention proposes a kind of underwater cloud denoising method based on airborne LiDAR sounding system, it is characterized in that, the following steps are included: waveform analysis, read the shape information of point cloud, by the type for judging the analysis of shape information cloud, a cloud is determined as underwater topography point, suspicious points or noise spot, filters out noise spot；The suspicious points obtained after judgement are carried out the verifying of depth measurement performance parameter by the verifying of depth measurement performance parameter, are determined that suspicious points are underwater topography point or noise spot by verifying, are filtered out noise spot；The underwater topography point obtained after judgement is carried out elevation deviation from mean iterated denoising, obtains final underwater topography point by elevation deviation from mean iterated denoising.Method proposed by the present invention can effectively reject block distortion, improve the reliability of denoising result, guarantee the extraction accuracy of underwater topography.
Description
Technical field
The invention belongs to Surveying Science and Technology fields, are related to a kind of cloud denoising method, specially a kind of based on airborne
Underwater cloud denoising method of LiDAR sounding system.
Background technique
Airborne LiDAR (LightDetectionAndRanging, laser acquisition and measurement) sounding system, which belongs to, actively to be surveyed
Deep system, is the method to be taken sounding using scanning survey mode from air-launched laser, and sounding system is being surveyed
The laser pulse that can emit two kinds of wavelength of infrared light and blue green light when operation is measured, wherein infrared band is not easy to penetrate seawater to visit
It measures and just carries out echo after the water surface, and the decaying when blue green light that wavelength is between 0.47~0.58 μm penetrates seawater is minimum,
Penetrability is strong and good directionality, therefore bluish-green wave band may detect seabed.It is being shallower than 50 meters of the stretch of coastal water, has incomparable
Superiority, especially can waters that efficiently rapid survey Shallow islands submerged reef and ship can not arrive safe and sound, be littoral zone
Periodicity, emergency monitoring provide new technological means.Influence and water body due to limitation, the scattering of equipment precision is floating
The reflection characteristic etc. for swimming object leads to inevitably there are many rough error points in the three-dimensional point cloud of LiDAR sounding system acquisition, i.e.,
For noise spot, if by these noise points deletings, the extraction of later period underwater topography certainly will do not will affect.Therefore, the denoising of point cloud is whole
Essential important link in a LiDAR depth measurement Point Cloud Processing process.
Currently, three-dimensional point cloud of the method for point cloud denoising greatly both for the acquisition of traditional infrared LiDAR height-finding system, noise
Point accounting is few, distribution is discrete, can preliminary cancelling noise point by the distribution of cloud height value.Elevation deviation from mean method is base
Point cloud denoising is realized in Principle of Statistics, and the thought of this method is: the number of noise is much smaller than the number of non-noise point, therefore counts
Calculate region in all the points elevation mean value, then count the difference of each point and elevation mean value, finally according to depth displacement whether
Noise spot is detected as criterion greater than preset threshold value and is rejected.
Elevation deviation from mean method has preferable rejecting effect to isolated spotted noise, but to the rejecting effect of block distortion
It is undesirable, especially when noise spot distribution it is close in non-noise point distribution when the case where, need preset threshold value, and the threshold value is set
Surely it is difficult to determine do not have universality.
In addition, airborne LiDAR sounding system is when sounding the depth of the water, water body is main transmission medium, as transmission medium,
Influence of the water body compared to air for laser is increasingly complex, the decaying in addition to scattering, there are also water refraction, water body for laser
Deng relying only on traditional point cloud noise eliminating method and be readily detected wrong noise especially for complicated aquatic environment
Point.
Summary of the invention
For denoising method in background technique, threshold value undesirable for the rejecting effect of block distortion is difficult to set the present invention
With the poor problem of denoising reliability, a kind of the rejectings effect for block distortion is proposed preferably and the base of denoising high reliablity
In underwater cloud denoising method of airborne LiDAR sounding system.
A kind of underwater cloud denoising method based on airborne LiDAR sounding system, which comprises the following steps:
S100: the shape information of point cloud is read in waveform analysis, by the class for judging the analysis of shape information cloud
A cloud is determined as underwater topography point, suspicious points or noise spot, filters out noise spot by type；
S200: the verifying of depth measurement performance parameter, the suspicious points obtained after being determined by step S100 carry out depth measurement performance ginseng
Number verifying determines that suspicious points are underwater topography point or noise spot by verifying, filters out noise spot；
S300: elevation deviation from mean iterated denoising, obtained after being determined by step S100 and step S200 under water
Form point carries out elevation deviation from mean iterated denoising, obtains final underwater topography point.
Further, shape information described in step S100 is the echo strength of bluish-green laser.
Further, the step S100 waveform judgement specifically:
S101: judgment threshold δ is determined；
S102: zoom ranges x is determined；
S103: determining decision condition, carries out the judgement of underwater topography point, noise spot and suspicious points: if bluish-green laser echo
Intensity in (δ (100%+x%) ,+∞) range, then determine the point for underwater topography point, if bluish-green laser echo strength (-
∞, δ (100%-x%)) in range, then determine that the point is noise spot, if bluish-green laser echo strength is in (δ (100%-x%), δ
(100%+x%)) point of range is judged to suspicious points.
Further, the zoom ranges x is 20.
Further, the judgment threshold is the echo strength threshold value of bluish-green laser, and the judgment threshold δ passes through following several
A parameter determines: 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 channel judgment threshold or shallow water channel judgment threshold.
Further, the step S200 depth measurement performance parameter verifying specifically:
S201: Laser bathymetry depth measurement performance standard coefficient k is determined；
S202: the unrestrained decay coefficient K d of water is determined；
S203: the depth capacity D that sounding system can detect is calculatedmax, specially
S204: the water depth value h and D of the suspicious points obtained after being determined by step S100maxIt is compared, if h > Dmax,
The point is then determined as noise spot, if h≤Dmax, then the point is determined as underwater topography point.
Further, the step S300 elevation deviation from mean iterated denoising specifically:
S301: the elevation mean value of all underwater topography points is calculatedWherein, μiFor under water
The height value of form point, n are underwater topography point quantity；
S302: error in the height value of all underwater topography points is calculatedWherein, m is iteration
Number；
S303: noise spot judgment threshold θ is determinedth, determine iteration ends threshold θ；
S304: noise spot judgement, if the height value of underwater topography point meets conditionThen sentence
It is set to noise spot；
S305: step S301~step S304 is repeated, middle error σ obtained in each repetitive process is calculatedmWith the last time
Middle error σ obtained in repetitive processm-1RatioIfThen terminate repetitive process；
S306: filtering out by determining obtained noise spot, will filter out the underwater topography point after noise spot as finally underwater
Topographic(al) point.
Further, noise spot judgment threshold θ in the step S303thFor 2 σ.
Further, iteration ends threshold θ is 90% in the step S303.
Proposed by the present invention cloud denoising method is the underwater point cloud data for the acquisition of airborne LiDAR sounding system, is led to
It crosses waveform analysis and tentatively judges that underwater topography point, noise spot and suspicious points, this step have denoised guiding function to a cloud；Pass through survey
The deeper suspicious points of the depth of water in deep performance parameter verifying previous step, finally by the underwater topography point obtained to above-mentioned two step
Elevation deviation from mean iterated denoising is carried out to complete the rejecting of entire noise spot and the extraction work of final underwater topography point.For
The point cloud data of airborne LiDAR sounding system acquisition, underwater cloud denoising method proposed by the invention and conventional point cloud denoise
Method is compared, and is denoised using waveform analysis, effectively rejecting block distortion；During elevation deviation from mean iterated denoising, use
Adaptive threshold is iterated denoising, and judgment threshold has universality, improves the reliability of denoising result, can guarantee underwater
Landform extraction accuracy has preferably denoising effect.
Detailed description of the invention
Fig. 1 is step flow chart of the 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；
Fig. 5 is the CZMIL sounding system test point cloud data collected (elevation section) chosen；
Fig. 6 is that (elevation cuts open effect picture after unimproved elevation deviation from mean method carries out a cloud denoising to test data
Face)；
Fig. 7 is that the method for the present invention carries out effect picture (elevation section) after step S100 cloud denoises to test data；
Fig. 8 is that the method for the present invention carries out effect picture (elevation section) after step S200 point cloud denoises to test data；
Fig. 9 is that the method for the present invention carries out effect picture (final underwater topography point after step S300 point cloud denoises to test data
Elevation section).
Specific embodiment
With reference to the accompanying drawings and embodiments, technical solution of the present invention is described in detail.
Embodiment 1:
Airborne LiDAR (LightDetectionAndRanging, laser acquisition and measurement) sounding system, which belongs to, actively to be surveyed
Deep system, is the method to be taken sounding using scanning survey mode from air-launched laser, and sounding system is being surveyed
The laser pulse that can emit two kinds of wavelength of infrared light and blue green light when operation is measured, wherein infrared band is not easy to penetrate seawater to visit
It measures and just carries out echo after the water surface, and the decaying when blue green light that wavelength is between 0.47~0.58 μm penetrates seawater is minimum,
Penetrability is strong and good directionality, therefore bluish-green wave band may detect seabed, passes through and calculates infrared laser pulses and bluish-green laser pulse
Echo time difference calculate depth under water.This method is being shallower than 50 meters of the stretch of coastal water, has unrivaled superiority, special
Be not can waters that efficiently rapid survey Shallow islands submerged reef and ship can not arrive safe and sound, for littoral zone periodicity, answer
Acute monitoring provides new technological means.
The present invention is a kind of underwater cloud denoising method based on airborne LiDAR sounding system, and proposed by the invention goes
Method for de-noising improves traditional elevation deviation from mean method, in order to make up this method to the rejecting effect of block distortion not
Foot is first carried out waveform analysis to point cloud data and is distributed with the approximate location for determining noise spot and underwater topography point, then passes through depth measurement
The performance parameter rub deeper to the depth of water is rejected, and the mesh of denoising is reached finally by the continuous iteration of elevation deviation from mean
's.Steps flow chart is as shown in Figure 1, the specific steps are as follows:
S100: the shape information of point cloud is read in waveform analysis, by the class for judging the analysis of shape information cloud
A cloud is determined as underwater topography point, suspicious points or noise spot by type.
S200: the verifying of depth measurement performance parameter, the suspicious points obtained after being determined by step S100 carry out depth measurement performance ginseng
Number verifying determines that suspicious points are underwater topography point or noise spot by verifying.
S300: elevation deviation from mean iterated denoising, obtained after being determined by step S100 and step S200 under water
Form point carries out elevation deviation from mean iterated denoising, obtains final underwater topography point.
The principle that point cloud denoising method proposed by the invention is obtained from underwater point cloud first, according to the waveform of cloud
Feature is analyzed and determined, the bluish-green laser of some strength, the bluish-green laser are emitted when airborne laser sounding system sounds the depth of the water
The laser intensity returned through seawater through sub-bottom reflection is just considered underwater topography point greater than certain threshold value, therefore, in this reality
It applies in example, the shape information that judgment threshold is used for during waveform analysis is the echo strength of laser bluish-green laser, if laser
The waveform of echo is the waveform of infrared laser and bluish-green laser band overlapping, then carries out the intensity for judging second of echo, i.e., blue
The echo strength of green laser.
The step process of waveform analysis is as shown in Figure 2, the specific steps are as follows:
S101: determining 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 channel judgment threshold and shallow water channel judgment threshold, wherein deep water channel
Threshold value δ1It is 200, shallow water channel threshold value δ2It is 50.The judgment threshold δ is determined by following parameter: lasing intensity,
Unrestrained attenuation coefficient, water-bottom reflectivity, atmospheric effect and the sounding system device parameters of water.
S102: determining scaling x, and the accuracy judged is ensured by setting scaling, in the present embodiment, scaling
Ratio value is 20, i.e., positive and negative 20% scaling is carried out to judgment threshold δ.
S103: determining decision condition, carries out the judgement of underwater topography point, noise spot and suspicious points: if bluish-green laser echo
Intensity in (δ (100%+x%) ,+∞) range, then determine the point for underwater topography point, if bluish-green laser echo strength (-
∞, δ (100%-x%)) in range, then determine that the point is noise spot, if bluish-green laser echo strength is in (δ (100%-x%), δ
(100%+x%)) point of range is judged to suspicious points.
Since the suspicious points obtained after step S100 waveform analysis are largely the deeper class underwater topography points of the depth of water, because
This, can be further determined whether by LiDAR depth measurement performance parameter as noise spot.
The step process of depth measurement performance parameter verifying is as shown in Figure 3, the specific steps are as follows:
S201: determining Laser bathymetry depth measurement performance standard coefficient k, and in the present embodiment, k is CZMIL (Coastal
Zone Mapping and Imaging LiDAR) sounding system typical coefficient, being worth is 4.0.
S202: determining the unrestrained decay coefficient K d of water, and in the present embodiment, data field Kd value is about 0.5m-1。
S203: the depth capacity D that sounding system can detect is calculatedmax, speciallyThe present embodiment is fallen into a trap
Calculate DmaxIt is 8 meters.
S204: the water depth value h and D of the suspicious points obtained after being determined by step S100maxIt is compared, if h > Dmax,
The point is then determined as noise spot, if h≤Dmax, then the point is determined as underwater topography point.
The verifying of depth measurement performance parameter can use above step, and plug gram disk depth data proof method can also be used.
It is possible to there are only a few noises in the underwater topography point determined by step S100 and step S200
Therefore point is filtered denoising using the method for elevation deviation from mean iteration.If there is partial noise point method can pick accordingly
It removes, otherwise the extraction of whole underwater topography is not influenced using the method due to the intensive of cloud without noise spot.The present invention
The elevation deviation from mean iterated denoising method of middle use is the improvement to traditional elevation deviation from mean denoising method, and this method is not pre-
Depth displacement threshold value is first set, but judgment threshold is used as by error in 2 times, is missed in automatic fitration and constantly iteration depth displacement
Difference makes its maximum cancelling noise point while guaranteeing point cloud transition ride comfort.
The step process of elevation deviation from mean iterated denoising is as shown in Figure 4, the specific steps are as follows:
S301: the elevation mean value of all underwater topography points is calculatedWherein, μiFor under water
The height value of form point, n are underwater topography point quantity.
S302: error in the height value of all underwater topography points is calculatedWherein, m is iteration
Number.
S303: noise spot judgment threshold θ is determinedth, 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 conditionThen sentence
It is set to noise spot.
S305: step S301~step S304 is repeated, middle error σ obtained in each repetitive process is calculatedmWith the last time
Middle error σ obtained in repetitive processm-1RatioIfThen terminate repetitive process.
S306: filtering out by determining obtained noise spot, will filter out the underwater topography point after noise spot as finally underwater
Topographic(al) point.
Choose the water of one group of CZMIL (Coastal Zone Mapping and Imaging LiDAR) sounding system acquisition
Numeric field data carries out noise points deleting test, and wherein Fig. 5 is original point cloud data (elevation section), and Fig. 6 is unimproved elevation
Effect picture (elevation section) after the method points cloud denoising of deviation from mean, wherein threshold value is set as 0.5 meter, and Fig. 7, Fig. 8 and Fig. 9 are this
Effect picture (the elevation after step S100, step S200 and step S300 the point cloud denoising that treated of invention the method
Section).
Known to comparison of test results: the method for unimproved elevation deviation from mean is when calculating elevation mean value vulnerable to noise
Point, which influences the very big deviation of appearance, causes denoising effect undesirable, especially when close minute in underwater topography point of distribution of noise spot
When cloth；It is more reliable that cloud denoising result is carried out using the method for the invention, more can accurately extract underwater topography data.
It should be appreciated that above by the explanation that preferred embodiment carries out technical solution of the present invention be it is schematical rather than
It is restrictive.Those skilled in the art on the basis of the above embodiments can be to technical side documented by each embodiment
Case is modified or equivalent replacement of some of the technical features；And these are modified or replaceed, and should all be covered at this
Within the protection scope of invention.Therefore, protection scope of the present invention should be subject to the protection scope in claims.
Claims (9)
1. a kind of underwater cloud denoising method based on airborne LiDAR sounding system, which comprises the following steps:
S100: the shape information of point cloud is read in waveform analysis, will by judging the analysis of shape information the type of cloud
Point cloud is determined as underwater topography point, suspicious points or noise spot, filters out noise spot；
S200: the verifying of depth measurement performance parameter, the suspicious points obtained after being determined by step S100 carry out depth measurement performance parameter and test
Card determines that suspicious points are underwater topography point or noise spot by verifying, filters out noise spot；
S300: elevation deviation from mean iterated denoising, the underwater topography point obtained after being determined by step S100 and step S200
Elevation deviation from mean iterated denoising is carried out, final underwater topography point is obtained；
Wherein, the step S300 elevation deviation from mean iterated denoising specifically:
S301: the elevation mean value of all underwater topography points is calculatedWherein, μiFor underwater topography point
Height value, n be underwater topography point quantity；
S302: error in the height value of all underwater topography points is calculatedWherein, m is time of iteration
Number；
S303: noise spot judgment threshold θ is determinedth, determine iteration ends threshold θ；
S304: noise spot judgement, if the height value of underwater topography point meets conditionThen it is determined as
Noise spot；
S305: step S301~step S304 is repeated, middle error σ obtained in each repetitive process is calculatedmIt was repeated with the last time
Middle error σ obtained in journeym-1RatioIfThen terminate repetitive process；
S306: filtering out by determining obtained noise spot, will filter out the underwater topography point after noise spot as final underwater topography
Point.
2. a kind of underwater cloud denoising method based on airborne LiDAR sounding system according to claim 1, feature exist
In shape information described in step S100 is the echo strength of bluish-green laser.
3. a kind of underwater cloud denoising method based on airborne LiDAR sounding system according to claim 2, feature exist
In the step S100 waveform judgement specifically:
S101: judgment threshold δ is determined；
S102: zoom ranges x is determined；
S103: determining decision condition, carries out the judgement of underwater topography point, noise spot and suspicious points: if bluish-green laser echo strength
In (δ (100%+x%) ,+∞) range, then the point is determined for underwater topography point, if bluish-green laser echo strength is in (- ∞, δ
(100%-x%)) in range, then determine that the point is noise spot, if bluish-green laser echo strength is in (δ (100%-x%), δ
(100%+x%)) point of range is judged to suspicious points.
4. a kind of underwater cloud denoising method based on airborne LiDAR sounding system according to claim 3, feature exist
In the zoom ranges x is 20.
5. a kind of underwater cloud denoising method based on airborne LiDAR sounding system according to claim 3, feature exist
In the judgment threshold is the echo strength threshold value of bluish-green laser, and the judgment threshold δ is determined by following parameter: being swashed
Light emitting intensity, the unrestrained attenuation coefficient of water, water-bottom reflectivity, atmospheric effect and sounding system device parameters.
6. a kind of underwater cloud denoising method based on airborne LiDAR sounding system according to claim 3, feature exist
In the judgment threshold includes deep water channel judgment threshold or shallow water channel judgment threshold.
7. a kind of underwater cloud denoising method based on airborne LiDAR sounding system according to claim 1, feature exist
In the step S200 depth measurement performance parameter verifying specifically:
S201: Laser bathymetry depth measurement performance standard coefficient k is determined；
S202: the unrestrained decay coefficient K d of water is determined；
S203: the depth capacity D that sounding system can detect is calculatedmax, specially
S204: the water depth value h and D of the suspicious points obtained after being determined by step S100maxIt is compared, if h > Dmax, then will
The point is determined as noise spot, if h≤Dmax, then the point is determined as underwater topography point.
8. a kind of underwater cloud denoising method based on airborne LiDAR sounding system according to claim 1, feature exist
In noise spot judgment threshold θ in the step S303thFor 2 σ.
9. a kind of underwater cloud denoising method based on airborne LiDAR sounding system according to claim 1, feature exist
In iteration ends threshold θ is 90% in the step S303.
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Families Citing this family (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109799494B (en) * | 2017-11-17 | 2020-09-29 | 中国林业科学研究院资源信息研究所 | Satellite-borne photon counting laser radar data rapid denoising and filtering method |
| CN108037509A (en) * | 2017-12-12 | 2018-05-15 | 辽宁科技大学 | A kind of undersea detection method and device |
| CN109631863A (en) * | 2019-01-22 | 2019-04-16 | 青岛秀山移动测量有限公司 | A kind of intertidal zone integration mapping method that vacant lot combines |
| 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 |
| 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 |
| CN111144318B (en) * | 2019-12-27 | 2023-08-29 | 苏州联视泰电子信息技术有限公司 | Noise reduction method for point cloud data of underwater sonar system |
| 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 |
| CN114509050B (en) * | 2021-12-31 | 2023-07-21 | 广州南方卫星导航仪器有限公司 | Water depth data processing method and system |
| CN115422981B (en) * | 2022-11-04 | 2023-04-07 | 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 (3)
| 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 |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10684362B2 (en) * | 2011-06-30 | 2020-06-16 | 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 (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
|---|
| 机载LiDAR点云滤波及分类算法研究;李炼;《中国优秀硕士学位论文全文数据库信息科技辑》;20150415;第38-39页 |
| 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;李凯;《遥感学报》;20150531;第1-4页 |
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