Patent Document ID: 20150039229
Application ID: 14448664
Patent Flag: 0

Claim One:
1. A multi-beam bathymetric chart construction method based on submarine digital depth model feature extraction, comprising the steps of: Step 1, constructing a digital depth model (DDM) based on raw multi-beam echo sounding, using a multi-beam postprocessing system, processing raw multi-beam data as following: 1.1) applying tide correction, draft correction and sound velocity correction to the raw multi-beam echo sounding data set Raw={raw i } i=1,n , eliminating effects of water level and sound velocity on multi-beam echo sounding (MBES), and forming a preliminary processing data set Proc1={proc1 i } i=1,n , wherein n is a natural number; 1.2) building a topographic trend surface, using automatic filtering method to process Proc1={proc1 i } i=1,n , eliminating effects of gross errors on MBES, and forming a postprocessing data set Proc2={proc2 i } i=1,n ; 1.3) using three-view projection to process Proc2={proc2 i } i=1,n , further eliminating effects of ocean noise of a variety of sources on MBES, and forming a discrete full-beam bathymetric data set Proc3={x i , y i , z i } i=1,m , wherein m is a natural number; 1.4) using Inverse Distance Weighted (IDW) method, building a digital model of Proc3={x i , y i , z i } i=1,m , forming the digital depth model DDM = { ddm k , l = { x k , l , y k , l , dep k , l } } k = 1 , K l = 1 , L , wherein K is the number of rows of the model, L is the number of columns of the model, both K and L are natural numbers, x k,l , y k,l represent coordinate and dep k,l represents depth value of data points of the model respectively; Step 2, establishing a slope and second derivative composite model based on the DDM, 2.1) using eight-neighbor approach to calculate slope value slp( i,j ) of each grid point of the DDM: slp ( i , j ) = 1 8 ∑ i = k k + 1 ∑ j = l l + 1 a tan ( Δ z Δ d ) Δz=|dep (i,j) −dep (k,l) |, wherein Δz is depth difference from grid point (i,j) to (k,l) in the DDM; Δd=√{square root over ((x (i,j) −x (k,j) ) 2 +(y (i,j )−y (k,j) ) 2 )}{square root over ((x (i,j) −x (k,j) ) 2 +(y (i,j )−y (k,j) ) 2 )}{square root over ((x (i,j) −x (k,j) ) 2 +(y (i,j )−y (k,j) ) 2 )}{square root over ((x (i,j) −x (k,j) ) 2 +(y (i,j )−y (k,j) ) 2 )}, wherein Δd is distance from the grid point(i,j) to (k,l) in the model; 2.2) using the eight-neighbor approach to calculate the second derivative sec (i,j) of each grid point of the model: sec ( i , j ) = 1 8 ∑ i = k k + 1 ∑ j = l l + 1 a tan ( Δδ Δ d ) Δδ = | slp ( i , j ) - slp ( k , l ) | 2.3) forming the composite model DSSM = { dss i , j = ( ddm i , j , slp i , j , sec i , j ) } i = 1 , K j = 1 , L ; Step 3, extracting feature points based on DDM sub-blocks, 3.1) model sub-blocking: sub-blocking the model DSSM by interlaced dislocation of squares with side length of d, wherein d is determined in accordance with cartographic scale, d=[0.018×scale,0.03×scale], wherein scale is a scale value; or d is specified directly by a user; wherein the interlaced dislocation refers to that if a i-th row of the model is sub-blocked by d as interval, when it comes to a (i+Δi)-th row, leaves a half of d as interval firstly, and then sub-blocks the model by d as interval; thus the interlaced dislocation results in a diamond structure of sub-blocking; after the sub-blocking, obtaining a new diamond grid model DSSM 1 = { dss 1 I , J = { dss i , j } i = I , I + Δ i j = J , J + Δ i } I = 1 , K 1 J = 1 , L 1 , wherein each sub-block dss1 I,J consists of sub-models { dss i , j } i = I , I + Δ i j = J , J + Δ i , KI and LI are the number of rows and columns of the sub-blocking model respectively, and both KI and L1 are natural numbers; 3.2) Numerical calculation of the sub-blocking model: 3.2.1) Calculating an average depth value dep _ I , J = 1 Num ∑ i = I I + Δ i ∑ j = J J + Δ i dep ( i , j ) of dss 1 I , J = { dss i , j } i = I , I + Δ i j = J , J + Δ i , an average slope value slp _ I , J = 1 Num ∑ i = I I + Δ i ∑ j = J J + Δ i slp ( i , j ) , and an average second derivative value sec _ I , J = 1 Num ∑ i = I I + Δ i ∑ j = J J + Δ i sec ( i , j ) , wherein Num is the number of grid points of each sub-blocking model, and Num is a natural number; 3.2.2) Using the inverse distance weighted (IDW) method, calculating the depth value Δdep I,J of a central point of each sub-blocking model; 3.3) determining concavity and convexity of the sub-blocking model, 3.3.1) if Δdep I,J > dep I,J , and sec I,J >0, considering the surface of the sub-blocking model as concave; 3.3.2) if Δdep I,J < dep I,J , and sec I,J <0, considering the surface of the sub-blocking model as convex; 3.3.3) if in other cases, considering the concavity of the sub-blocking model not being determined hereby; 3.4) determining a feature depth point of the sub-blocking model, 3.4.1) if the surface of the sub-blocking model is concave, traversing the model dss 1 I , J = { dss i , j } i = I , I + Δ i j = J , J + Δ i , selecting a maximum depth point dss i — max,j — max as the feature point; if there's more than one maximum depth point, selecting the point both with the maximum slope value and near the central point; 3.4.2) if the surface of the sub-blocking model is convex, traversing the model dss 1 I , J = { dss i , j } i = I , I + Δ i j = J , J + Δ i , selecting a minimum depth point dss i — min,j — min as the feature point; if there's more than one minimum depth point, selecting the point both with the minimum slope value and near the central point; 3.4.3) if in other cases, selecting the central point dss i —min,j — cen as the feature point; 3.5) identifying the feature points of the model, If dss i,j is the feature point, setting identification as 1, otherwise setting the identification as 0; Step 4, querying the multi-beam sounding data based on the feature points, 4.1) traversing the data set Proc3={x i , y i , z i } i=1,m , querying the grid point dss I,J of the model according to the coordinates (x i , y i ) of each depth point, wherein the coordinates of the grid point dss i, J is (X I , Y J ); 4.2) if dis=√(x i −X 1 ) 2 +(y j −Y J ) 2 <0.5×grid_d, and dss I,J is identified as 1, using the IDW method, based on the data set Proc3={x i , y i , z i } i=1,m recalculating the depth value dep_new I,J of the grid points of the model DSSM, wherein grid_d is spatial distance between nearest two points; and Step 5, constructing the multi-beam bathymetric chart based on layers, using a mapping system or GIS, constructing the multi-beam bathymetric chart according to a isobath layer, a depth layer, and a modification layer; 5.1) in the isobath layer, building intermediate contour, auxiliary contour, index contour and depth annotations of index contour according to mapping scale and national standard; 5.2) in the modification layer, adding map title, scale, legend, frame, latitude and longitude lines and coastlines; 5.3) in the depth layer, traversing the model DSSM, if the model point dss I,J is identified as 1, and the depth value dep_new I,J is not null, using the point as the feature point, if the depth value dep_new I,J is null, using dep I,J as the feature point; 5.4) if the soundings of the bathymetric chart are sparse, decreasing the value d of sub-blocking size, returning to step 3; 5.5) if the soundings of the bathymetric chart are dense, increasing the value d of sub-blocking size, returning to step 3; 5.6) overlaying the layers, if graphic elements superimpose on each other, or the graphic elements are dense, deleting some auxiliary graphic elements.