CN107798339A - A kind of multi-beam exceptional value data processing algorithm based on Thiessen polygon figure - Google Patents
A kind of multi-beam exceptional value data processing algorithm based on Thiessen polygon figure Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
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- G06F18/29—Graphical models, e.g. Bayesian networks
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B15/00—Measuring arrangements characterised by the use of electromagnetic waves or particle radiation, e.g. by the use of microwaves, X-rays, gamma rays or electrons
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
- G06F2218/02—Preprocessing
Abstract
The invention discloses a kind of multi-beam exceptional value data processing algorithm based on Thiessen polygon figure：Voronoi diagram is built to ocean floor topographic survey region, records the coordinate value on each summit；Available depth point distance is obtained, is radius by origin, available depth point distance of Voronoi polygon vertexs, the depth of water point searched in the range of this；Calculate Information Propagation Model of each depth of water point to Voronoi nodes；The initial value of depth of water sequence is calculated using medium filtering, control water depth point enters the order of wave filter；The node depth of water is estimated using dynamic linear models, obtains the depth of water estimate and depth of water uncertainty of node；According to depth of water uncertainty, the rough error of multi-beam water-depth measurement is rejected according to the precision measure requirement of IHO hydrographic survey specifications.The Voronoi diagram that the present invention is built can be good at reflecting the real terrain in seabed, the discrete depth of water point for making full use of multi-beam to measure, and improve the precision of multi-beam Bathymetric Data processing, and then improve the expression that becomes more meticulous of sea-floor relief.
Description
Technical field
The present invention relates to multi-beam exceptional value data processing, more particularly to a kind of multi-beam based on Thiessen polygon figure is different
Constant value data processing algorithm.
Background technology
Because measuring table is influenceed by wave, tide, stream and the marine environment such as wind, multibeam sounding system is surveyed in field operation
" glitch ", i.e. exceptional value occurs in the amount stage unavoidably.The domestic semi-automatic filtering side for mainly using man-machine interactively at present
Formula handles exceptional value, and not only efficiency is low and depth measurement result is vulnerable to the influence of people's subjective factor for this processing mode.And for
The multi-beam Bathymetric Data processing of magnanimity, the mode of human-edited are only applicable to the post processing of data, are not suitable for observing the reality of data
When handle.
Foreign countries achieve some effective algorithms in terms of abnormality value removing, and simplest algorithm is exactly using maximum, most
The detection principles such as small depth thresholding, ruling grade, minimum angles, lateral separation carry out rejecting abnormalities value；VARMA et al. proposes root
Estimate rejecting abnormalities value according to the intermediate value and standard deviation of whole data；DU et al. proposes a kind of data manually filtered that build and compiled
Collect program；LIRAKIS et al. carries out the filtering of multiple form with PFM systems.These algorithms accelerate more to a certain extent
The speed of beam data processing, but the Robustness least squares of these models are poor, and a less region can only be filtered every time
Ripple, thus it is inefficient.
The content of the invention
It is an object of the invention to solve current domestic multi-beam exceptional value data processing method-man-machine interactively editor's
Deficiency, there is provided one kind is based on the multi-beam exceptional value data processing algorithm of Thiessen polygon figure (Voronoi diagram), improves multi-beam
The efficiency of data processing, Robustness least squares, robustness and reduce artificial Subjective Intervention.
The technical solution adopted in the present invention is：A kind of multi-beam exceptional value data processing based on Thiessen polygon figure is calculated
Method, comprise the following steps：
Step A, Thiessen polygon figure is built according to delaunay (Delaunay) triangulation to ocean floor topographic survey region,
And record the coordinate value on each summit of each Thiessen polygon figure polygon；
Step B, available depth point distance is obtained, using the polygon vertex of Thiessen polygon figure as origin, available depth point
Distance is radius, the depth of water point searched in the range of this；
Step C, calculate Information Propagation Model of each depth of water point to Thiessen polygon node of graph；
Step D, the initial value of depth of water sequence is calculated using medium filtering, and control water depth point enters the order of wave filter；
Step E, the Thiessen polygon node of graph depth of water is estimated using Bayesian Dynamic Linear Models, obtains node
Depth of water estimate and depth of water uncertainty；
Step F, will according to the precision measure of the hydrographic survey specification of International Hydrography Organization according to depth of water uncertainty
Ask and the rough error of multi-beam water-depth measurement is rejected, complete multi-beam exceptional value data processing.
Further, in step B, available depth point distance, delta is calculated according to the following equationmax：
Wherein, SHIt is the scale factor of worst expected horizontal error；ΔminIt is minimum grid space length；α is to make by oneself
The exponential factor of justice；For maximum error of measuring as defined in the hydrographic survey specification S-44 of International Hydrography Organization；For
The vertical survey variance of i-th of depth of water point；σH,iFor error in the horizontal survey of i-th of depth of water point.
Further, in step C, calculate each depth of water point using following models and the information of Thiessen polygon node of graph is passed
Broadcast model ej(si), obtain predetermined depth d of i-th of depth of water point to node jijWith prediction variance of i-th of depth of water point to node j
Wherein,ζiFor the depth of i-th of depth of water point,For the horizontal survey of i-th of depth of water point
Variance,For the vertical survey variance of i-th of depth of water point；δij=| | xi-nj| | it is propagation distance, xiFor i-th depth of water point
Horizontal level, njFor the horizontal level of j-th of node.
Further, in step E, the Thiessen polygon node of graph depth of water is carried out using following Bayesian Dynamic Linear Models
Estimation：
Wherein,Variancy propagation value for the n depth of water o'clock to j-th of node；For (n-1)th
Variancy propagation value of the individual depth of water o'clock to j-th of node；It is the n depth of water o'clock to j-th of node depth of water propagation values；Depth of water propagation values for (n-1)th depth of water o'clock to j-th of node；Gj[n] is filtering gain index；For
Variancy propagation value of n-th of the depth of water o'clock to j-th of node；Obtained in step (3)Be n-th of depth of water point to section
Point j prediction variance；εj[n] is new breath amount；dj[n] is the d obtained in step (3)ij, be n-th of depth of water point to the pre- of node j
Depth measurement degree；For node j depth of water estimate；For node j depth of water uncertainty.
The beneficial effects of the invention are as follows：A kind of multi-beam exceptional value data processing based on Thiessen polygon figure of the present invention is calculated
Method, there is high efficiency, objectivity, robustness, accuracy in terms of multi-beam Bathymetric Data is handled；Under certain condition, can also enter
The real-time data processing of row.The Thiessen polygon figure built first can be good at reflecting the real terrain in seabed, fully profit
The discrete depth of water point measured with multi-beam, the precision of multi-beam Bathymetric Data processing is improved, and then improve the table that becomes more meticulous of sea-floor relief
Reach.
Brief description of the drawings
Fig. 1 is the algorithm flow chart of the present invention.
Embodiment
The invention will be further described below in conjunction with the accompanying drawings.
As shown in Figure 1, a kind of multi-beam exceptional value data processing algorithm based on Thiessen polygon figure, is first according to moral
Lip river Triangle ID subdivision algorithm structure Thiessen polygon figure, then carries out depth information by Bayesian Dynamic Linear Models and does not read
The transmission of degree of certainty, finally according to the multi-beam specifications of surveys of International Hydrography Organization (IHO) carry out exceptional value identification and
Reject.Specifically include following steps：
Step A, Thiessen polygon figure is built according to Delaunay triangulation algorithm to ocean floor topographic survey region, and recorded every
The coordinate value on each summit of individual Thiessen polygon figure polygon.
Step B, obtain available depth point distance, deltamax, using the polygon vertex of Thiessen polygon figure as origin, effective water
Deep point distance, deltamaxFor radius, the depth of water point searched in the range of this.(1) calculates available depth point distance according to the following equation
Δmax：
Wherein, SHIt is scale factor (the usual S of worst expected horizontal errorH=1.96)；ΔminIt is minimum grid space
Distance；α is customized exponential factor (general α=2)；For the hydrographic survey specification S-44 of International Hydrography Organization
Maximum error of measuring as defined in (5th)；For the vertical survey variance of i-th of depth of water point；σH,iFor the level of i-th of depth of water point
Error in measurement.
Step C, Information Propagation Model e of each depth of water point to Thiessen polygon node of graph is calculated using following models (2)j
(si), obtain predetermined depth d of i-th of depth of water point to node jijWith prediction variance of i-th of depth of water point to node j
Wherein,ζiFor the depth of i-th of depth of water point,For the horizontal survey of i-th of depth of water point
Variance,For the vertical survey variance of i-th of depth of water point；δij=| | xi-nj| | it is propagation distance, xiFor i-th depth of water point
Horizontal level, njFor the horizontal level of j-th of node.
Step D, the initial value of depth of water sequence is calculated using medium filtering, and control water depth point enters the order of wave filter.
Step E, the Thiessen polygon node of graph depth of water is entered using following Bayesian Dynamic Linear Models (DLM) (3)-(8)
Row estimation, obtain node j depth of water estimateWith depth of water uncertainty
Wherein,Variancy propagation value for the n depth of water o'clock to j-th of node；For (n-1)th
Variancy propagation value of the individual depth of water o'clock to j-th of node；It is the n depth of water o'clock to j-th of node depth of water propagation values；Depth of water propagation values for (n-1)th depth of water o'clock to j-th of node；Gj[n] is filtering gain index；For
Variancy propagation value of n-th of the depth of water o'clock to j-th of node；Obtained in step (3)Be n-th of depth of water point to section
Point j prediction variance；εj[n] is new breath amount；dj[n] is the d obtained in step (3)ij, be n-th of depth of water point to the pre- of node j
Depth measurement degree；For node j depth of water estimate；For node j depth of water uncertainty.
Step F, will according to the precision measure of the hydrographic survey specification of International Hydrography Organization according to depth of water uncertainty
Ask and the rough error of multi-beam water-depth measurement is rejected, complete multi-beam exceptional value data processing.After exporting rejecting abnormalities value
Normal Value Data.
Although the preferred embodiments of the present invention are described above in conjunction with accompanying drawing, the invention is not limited in upper
The embodiment stated, above-mentioned embodiment is only schematical, be not it is restricted, this area it is common
Technical staff in the case of present inventive concept and scope of the claimed protection is not departed from, may be used also under the enlightenment of the present invention
By make it is many in the form of, these are belonged within protection scope of the present invention.
Claims (4)
1. a kind of multi-beam exceptional value data processing algorithm based on Thiessen polygon figure, it is characterised in that comprise the following steps：
Step A, Thiessen polygon figure is built according to Delaunay triangulation algorithm to ocean floor topographic survey region, and recorded each safe
The coordinate value on each summit of gloomy polygon diagram polygon；
Step B, available depth point distance is obtained, using the polygon vertex of Thiessen polygon figure as origin, available depth point distance
For radius, the depth of water point searched in the range of this；
Step C, calculate Information Propagation Model of each depth of water point to Thiessen polygon node of graph；
Step D, the initial value of depth of water sequence is calculated using medium filtering, and control water depth point enters the order of wave filter；
Step E, the Thiessen polygon node of graph depth of water is estimated using Bayesian Dynamic Linear Models, obtains the depth of water of node
Estimate and depth of water uncertainty；
Step F, according to depth of water uncertainty, according to the precision measure requirement pair of the hydrographic survey specification of International Hydrography Organization
The rough error of multi-beam water-depth measurement is rejected, and completes multi-beam exceptional value data processing.
2. a kind of multi-beam exceptional value data processing algorithm based on Thiessen polygon figure according to claim 1, it is special
Sign is, in step B, calculates available depth point distance, delta according to the following equationmax：
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Wherein, SHIt is the scale factor of worst expected horizontal error；ΔminIt is minimum grid space length；α is customized finger
The number factor；For maximum error of measuring as defined in the hydrographic survey specification S-44 of International Hydrography Organization；For i-th
The vertical survey variance of depth of water point；σH,iFor error in the horizontal survey of i-th of depth of water point.
3. a kind of multi-beam exceptional value data processing algorithm based on Thiessen polygon figure according to claim 1, it is special
Sign is, in step C, Information Propagation Model e of each depth of water point to Thiessen polygon node of graph is calculated using following modelsj
(si), obtain predetermined depth d of i-th of depth of water point to node jijWith prediction variance of i-th of depth of water point to node j
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Prosposition is put, njFor the horizontal level of j-th of node.
4. a kind of multi-beam exceptional value data processing algorithm based on Thiessen polygon figure according to claim 1, it is special
Sign is, in step E, the Thiessen polygon node of graph depth of water is estimated using following Bayesian Dynamic Linear Models：
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Wherein,Variancy propagation value for the n depth of water o'clock to j-th of node；For (n-1)th water
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Variancy propagation value of n-th of the depth of water o'clock to j-th of node；Obtained in step (3)For n-th of depth of water point pair
Node j prediction variance；εj[n] is new breath amount；dj[n] is the d obtained in step (3)ij, be n-th of depth of water point to node j's
Predetermined depth；For node j depth of water estimate；For node j depth of water uncertainty.
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| CN111366936A (en) * | 2020-03-03 | 2020-07-03 | 广州点深软件有限公司 | Multi-beam sounding data processing method and device |
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| US20160063724A1 (en) * | 2013-05-10 | 2016-03-03 | Pathxl Limited | Apparatus And Method For Processing Images Of Tissue Samples |
| CN105841777A (en) * | 2016-03-17 | 2016-08-10 | 深圳大学 | Multi-beam sounding estimation method and system based on adaptive selection node |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN102305944A (en) * | 2011-07-28 | 2012-01-04 | 李典基 | Underwater sedimentation amount detecting analysis processing system |
| CN102831645A (en) * | 2012-07-18 | 2012-12-19 | 哈尔滨工程大学 | Method for establishing digital elevation model applied to submarine topography |
| US20160063724A1 (en) * | 2013-05-10 | 2016-03-03 | Pathxl Limited | Apparatus And Method For Processing Images Of Tissue Samples |
| CN103971367A (en) * | 2014-04-28 | 2014-08-06 | 河海大学 | Hydrologic data image segmenting method |
| CN105841777A (en) * | 2016-03-17 | 2016-08-10 | 深圳大学 | Multi-beam sounding estimation method and system based on adaptive selection node |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111366936A (en) * | 2020-03-03 | 2020-07-03 | 广州点深软件有限公司 | Multi-beam sounding data processing method and device |
| CN111366936B (en) * | 2020-03-03 | 2023-08-22 | 广州点深软件有限公司 | Multi-beam sounding data processing method and device |
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