U.S. patent application number 16/627900 was filed with the patent office on 2022-05-26 for geological linear body extraction method based on tensor voting coupled with hough transformation.
The applicant listed for this patent is CHANG'AN UNIVERSITY. Invention is credited to Ling HAN, Zhiheng LIU, Yuming NING, Tingting Wu, Zhongyang ZHAO.
Application Number | 20220164960 16/627900 |
Document ID | / |
Family ID | |
Filed Date | 2022-05-26 |
United States Patent
Application |
20220164960 |
Kind Code |
A9 |
HAN; Ling ; et al. |
May 26, 2022 |
GEOLOGICAL LINEAR BODY EXTRACTION METHOD BASED ON TENSOR VOTING
COUPLED WITH HOUGH TRANSFORMATION
Abstract
The present disclosure provides geological linear body
extraction method based on tensor voting coupled with Hough
transformation, including pre-processing a remote sensing image to
obtain a pre-processed remote sensing image; selecting three
optimal wavebands from N multi-spectral wavebands of the
pre-processed remote sensing image, so as to obtain a remote
sensing image combined by the optimal wavebands, N being a natural
number greater than or equal to 3; using Gaussian high-pass
filtering to perform sharpening processing on the remote sensing
image combined by the optimal wavebands, so as to enhance
linearized edge information; performing edge detection on the
remote sensing image having enhanced linearized edge information,
so as to obtain all edge points in the remote sensing image; and
converting all the edge points in the remote sensing image from an
image coordinate system to a parameter coordinate system, and
extracting a geological linear body from the parameter coordinate
system.
Inventors: |
HAN; Ling; (Xi'an, Shaanxi
Province, CN) ; LIU; Zhiheng; (Xi'an, Shaanxi
Province, CN) ; Wu; Tingting; (Xi'an, Shaanxi
Province, CN) ; NING; Yuming; (Xi'an, Shaanxi
Province, CN) ; ZHAO; Zhongyang; (Xi'an, Shaanxi
Province, CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
CHANG'AN UNIVERSITY |
Xi'an, Shaanxi Province |
|
CN |
|
|
Prior
Publication: |
|
Document Identifier |
Publication Date |
|
US 20210287376 A1 |
September 16, 2021 |
|
|
Appl. No.: |
16/627900 |
Filed: |
September 17, 2018 |
PCT Filed: |
September 17, 2018 |
PCT NO: |
PCT/CN2018/105966 PCKC 00 |
371 Date: |
December 31, 2019 |
International
Class: |
G06T 7/13 20060101
G06T007/13; G06T 7/90 20060101 G06T007/90 |
Foreign Application Data
Date |
Code |
Application Number |
Feb 7, 2018 |
CN |
201810121330.4 |
Claims
1. A geological linear body extraction method based on tensor
voting coupled with Hough transformation, the method comprising:
pre-processing a remote sensing image to obtain a pre-processed
remote sensing image; selecting three optimal wavebands from N
multi-spectral wavebands of the pre-processed remote sensing image
to obtain a remote sensing image combined by the optimal wavebands,
N is a natural number greater than or equal to 3; performing
sharpening processing on the remote sensing image combined by the
optimal wavebands by using Gaussian high-pass filtering to enhance
linearized edge information; performing edge detection on the
remote sensing image having enhanced linearized edge information to
obtain all edge points in the remote sensing image; and converting
all the edge points in the remote sensing image from an image
coordinate system to a parameter coordinate system to extract a
geological linear body from the parameter coordinate system;
wherein the image coordinate system uses any angle of the remote
sensing image as an origin, a horizontal direction of the remote
sensing image as an x-axis, and a vertical direction of the remote
sensing image as a y-axis; and the parameter coordinate system is
expressed as .rho.=x cos .theta.+y sin .theta., wherein .theta. and
.rho. represent polar coordinates of the edge points in the
parameter coordinate system.
2. The geological linear body extraction method based on tensor
voting coupled with Hough transformation according to claim 1,
wherein pre-processing a remote sensing image comprises: selecting
any remote sensing image from a remote sensing image database as a
current remote sensing image, the current remote sensing image
containing a cloud amount of less than 5% and having rational
polynomial coefficients (RPC) and statistical image grayscale
information, the image grayscale information comprising a gray
variance and a standard deviation for each waveband; and performing
radiometric calibration, atmospheric correction, and image cropping
on the current remote sensing image.
3. The geological linear body extraction method based on tensor
voting coupled with Hough transformation according to claim 1,
wherein selecting three optimal wavebands from N multi-spectral
wavebands of the pre-processed remote sensing image comprises:
selecting three wavebands corresponding to a maximum optimum index
factor (OIF) from the multi-spectral wavebands of the pre-processed
remote sensing image as the optimal wavebands; wherein the OIF is
obtained based on Formula (1) as below: OIF = i = 1 3 .times. S i j
= 1 3 .times. R ij ( 1 ) ##EQU00020## where in Formula (1), S.sub.i
represents a standard deviation of an ith waveband, R.sub.ij
represents a correlation coefficient between the ith waveband and a
jth waveband, i.noteq.j, i=1, 2, . . . , N, j=1, 2, . . . , N; and
R ij = Cov .function. ( i , j ) D .function. ( i ) .times. D
.function. ( j ) , ##EQU00021## wherein D(i) and D(j) respectively
represent a variance of the ith waveband and a variance of the jth
waveband, and wherein Cov(i,j) represents a covariance between the
ith waveband and the jth waveband.
4. The geological linear body extraction method based on tensor
voting coupled with Hough transformation according to claim 1,
wherein performing sharpening processing on the remote sensing
image combined by the optimal wavebands by using Gaussian high-pass
filtering comprises: performing Gaussian high-pass filtering on the
remote sensing image combined by the optimal wavebands to obtain a
filtered remote sensing image H(u,v) based on Formula (2) as below:
H .function. ( u , v ) = 1 - e - D 2 .function. ( u , v ) 2 .times.
D 0 2 ( 2 ) ##EQU00022## where, in Formula (2), D(u,v) represents a
distance between a frequency domain midpoint (u,v) of the remote
sensing image combined by the optimal wavebands and a frequency
rectangle center, and D.sub.0 represents a constant.
5. The geological linear body extraction method based on tensor
voting coupled with Hough transformation according to claim 1,
wherein performing edge detection on the remote sensing image
having enhanced linearized edge information to obtain all edge
points in the remote sensing image comprises: arbitrarily selecting
a pixel from the remote sensing image having enhanced linearized
edge information as a current pixel point, and performing Laplacian
convolution on the current pixel to obtain a tensor matrix T; where
T = [ .differential. 2 .times. I .differential. x 2 .differential.
2 .times. I .differential. x .times. .differential. y
.differential. 2 .times. I .differential. y .times. .differential.
x .differential. 2 .times. I .differential. y 2 ] , I ##EQU00023##
represents the remote sensing image having enhanced linearized edge
information, and .differential. 2 .times. I .differential. x 2
##EQU00024## and .differential. 2 .times. I .differential. y 2
##EQU00025## respectively represent a second derivative of the
remote sensing image I along a direction x and a second derivative
of the remote sensing image I along a direction y, wherein x and y
respectively represent an x-axis and a y-axis of an image
coordinate system established by taking any angle of the remote
sensing image I as an origin, a horizontal direction of the remote
sensing image I as the x-axis, and a vertical direction of the
remote sensing image I as the y-axis; performing matrices spectrum
decomposition on the tensor matrix T to obtain a rod-shaped
component and a spherical component, where T = ( .lamda. 1 -
.lamda. 2 ) .times. e 1 .fwdarw. .times. e 1 .fwdarw. T + .lamda. 2
.function. ( e 1 .fwdarw. .times. e 1 .fwdarw. T + e 2 .fwdarw.
.times. e 2 .fwdarw. T ) , e 1 .fwdarw. .times. e 1 .fwdarw. T
##EQU00026## represents the rod-shaped component, and ( e 1
.fwdarw. .times. e 1 .fwdarw. T + e 2 .fwdarw. .times. e 2 .fwdarw.
T ) ##EQU00027## represents the spherical component; determining
the current pixel as an edge point, if
(.lamda..sub.1-.lamda..sub.2)>.lamda..sub.2, otherwise,
determining the current pixel as a non-edge point; and repeating
performing matrices spectrum decomposition and determining the
current pixel as an edge point until all pixels of the remote
sensing image having enhanced linearized edge information are
determined as current pixels to obtain all the edge points in the
remote sensing image.
6. The geological linear body extraction method based on tensor
voting coupled with Hough transformation according to claim 1,
wherein converting all the edge points in the remote sensing image
from an image coordinate system to a parameter coordinate system to
extract a geological linear body from the parameter coordinate
system comprises: traversing the parameter coordinate system to
search for a point with a local maximum value, determining the
point with the local maximum value as a peak point, and setting
coordinates of the peak point as (.rho., .theta.), wherein (.rho.,
.theta.) respectively represent a slope and an intercept of the
geological linear body in the remote sensing image; and converting
the coordinates corresponding to the peak point in the parameter
coordinate system into the image coordinate system, and connecting
the edge points according to the direction of the edge point and
distance of the end point to obtain an image of the geological
linear body, thereby completing the extraction of the geological
linear body.
Description
CROSS REFERENCE TO RELATED APPLICATIONS
[0001] This patent application is a National Stage Entry of
PCT/CN2018/105966 filed on Sep. 17, 2018, which claims the benefit
and priority of Chinese Patent Application No. 201810121330.4 filed
on Feb. 7, 2018, the disclosures of which are incorporated by
reference herein in their entirety as part of the present
application.
BACKGROUND
[0002] The present disclosure relates to the field of remote
sensing geology and image processing technologies, and more
particularly, to a geological linear body extraction method based
on tensor voting coupled with Hough transformation.
[0003] Geological structures such as fault regions and faults
belong to geologically weak regions, and they may form linear
landforms due to erosion effects and the like. Under the action of
the geological structures, these linear landforms generally present
obvious linear distribution on remote sensing images, which are
referred to as geological linear bodies. These geological linear
bodies control the migration of underground fluids (ore solutions,
groundwater, and oil and gas, etc.) and spatial occurrence of
mineral resources. Orientations and densities of the underground
fluids and mineral resources have far-reaching scientific
significance and practical value for analyzing regional tectonic
movement trends and activity levels.
[0004] Since the 1980s, many domestic and overseas remote sensing
geologists have used expert knowledge and experience and image
processing methods to extract geological linear bodies of remote
sensing images and digital elevation models (DEM), and analyze
regional geological structure trends and effect degree. For
example, Jansson et al. (2005) used Landsat 7 ETM+ and digital
elevation models to extract geological linear bodies and mapped
glacial landforms in the northeast of Wales. Wu Jing et al. (2011)
extracted fault structure information using Canny edge detection
and Hough transformation, and used ENVI+IDL and other programming
languages. Yuan Xiaoxiang et al. (2011) highlighted the contrast of
geological linear bodies on multi-source remote sensing data using
false color synthesis, principal component transform, tasseled cap
transformation, waveband ratio, landform rendering and the like,
and extracted active faults. Alaa, A M et al. (2011) extracted
geological linear bodies on images processed by mountain shadow
rendering using a clue tracing algorithm based on DEM data and
linear features of mountain shadow enhancement, used a B-spline
curve to provide an integrated expression, and finally evaluated
regional geological structural environment conditions. Yusof et al.
(2011) analyzed the relationship between landslide hazard
distribution around highways and geological linear body density.
Liu Zhirong et al. (2012) extracted and analyzed information and
distribution of active faults in Yinchuan using image enhancement
processing algorithms such as contrast enhancement, color
synthesis, directional filtering and image fusion. Bahiru et al.
(2016) extracted and mapped the geological linear structure
distribution in Uganda using Landsat ETM+ and SRTMDEM data, studied
the distribution of gold mines in this region, which had an
important research significance for mineral prediction.
[0005] Some achievements have been achieved in different degrees in
the above researches. However, there mainly are the following
deficiencies.
[0006] 1) The correctness of visual interpretation results relies
on experience and knowledge backgrounds of interpretation experts,
which is time-consuming, labor-consuming, and inefficient.
[0007] 2) The accuracy of computer interpretation is related to the
processing speed and the resolution of the data source. The larger
an image is, the slower the processing speed is. If the resolution
is too high, the image is easily affected by linear features such
as roads and land use boundaries, which may produce a large number
of wrong linear edges, and thus producing too much noise. Relying
too much on parameter settings may lead to poor general
universality.
BRIEF DESCRIPTION
[0008] In view of deficiencies of existing technologies,
embodiments of the present disclosure provide a geological linear
body extraction method based on tensor voting coupled with Hough
transformation, so as to solve the problem that the existing
technologies rely on experience and knowledge of interpretation
experts, and are time and labor consuming, low in efficiency, slow
in processing speed, high in noise, and poor in universality.
[0009] To solve the above technical problems, the present
disclosure is implemented by using the following technical
solutions.
[0010] A geological linear body extraction method based on tensor
voting coupled with Hough transformation includes following
steps:
[0011] Step 1, pre-processing a remote sensing image to obtain a
pre-processed remote sensing image;
[0012] Step 2, selecting three optimal wavebands from N
multi-spectral wavebands of the pre-processed remote sensing image
to obtain a remote sensing image combined by the optimal wavebands,
N being a natural number greater than or equal to 3;
[0013] Step 3, performing sharpening processing on the remote
sensing image combined by the optimal wavebands by using Gaussian
high-pass filtering to enhance linearized edge information;
[0014] Step 4, performing edge detection on the remote sensing
image having enhanced linearized edge information in Step 3 to
obtain all edge points in the remote sensing image; and
[0015] Step 5, converting all the edge points in the remote sensing
image from an image coordinate system to a parameter coordinate
system to extract a geological linear body from the parameter
coordinate system.
[0016] The image coordinate system uses any angle of the remote
sensing image as an origin, a horizontal direction of the remote
sensing image as an x-axis, and a vertical direction of the remote
sensing image as a y-axis.
[0017] The parameter coordinate system is expressed as .rho.=x cos
.theta.+y sin .theta., where .theta. and .rho. represent polar
coordinates of the edge points in the parameter coordinate system
respectively.
[0018] Further, pre-processing a remote sensing image in step 1
includes selecting any remote sensing image from a remote sensing
image database as a current remote sensing image, the current
remote sensing image containing a cloud amount of less than 5%, and
having rational polynomial coefficients (RPC) and statistical image
grayscale information, the image grayscale information including a
gray variance and a standard deviation for each waveband, and
performing radiometric calibration, atmospheric correction, and
image cropping on the current remote sensing image.
[0019] Further, selecting three optimal wavebands from N
multi-spectral wavebands of the pre-processed remote sensing image
in step 2 includes:
[0020] selecting three wavebands corresponding to a maximum optimum
index factor (OIF) from the multi-spectral wavebands of the
pre-processed remote sensing image as the optimal wavebands;
[0021] wherein the OIF is obtained based on Formula (1) as
below:
O .times. .times. I .times. .times. F = i = 1 3 .times. s i j = 1 3
.times. R i .times. j ( 1 ) ##EQU00001##
[0022] where in Formula (1), S.sub.i represents a standard
deviation of an ith waveband, R.sub.ij represents a correlation
coefficient between the ith waveband and a jth waveband, i.noteq.j,
i=1, 2, . . . , N, j=1, 2, . . . , N; and
R i .times. j = C .times. o .times. v .function. ( i , j ) D
.function. ( i ) .times. D .function. ( j ) , ##EQU00002##
where D(i) and D(j) respectively represent a variance of the ith
waveband and a variance of the jth waveband, and Cov(i,j)
represents a covariance between the ith waveband and the jth
waveband.
[0023] Further, performing sharpening processing on the remote
sensing image combined by the optimal wavebands by using Gaussian
high-pass filtering in step 3 includes:
[0024] performing Gaussian high-pass filtering on the remote
sensing image combined by the optimal wavebands to obtain a
filtered remote sensing image H(u,v) based on Formula (2) as
below:
H .function. ( u , v ) = 1 - e - D 2 .function. ( u , v ) 2 .times.
D 0 2 ( 2 ) ##EQU00003##
[0025] where, in Formula (2), D(u,v) represents a distance between
a frequency domain midpoint (u,v) of the remote sensing image
combined by the optimal wavebands and a frequency rectangle center,
and D.sub.0 represents a constant.
[0026] Further, performing edge detection on the remote sensing
image having enhanced linearized edge information in Step 3 to
obtain all edge points in the remote sensing image in step 4
includes:
[0027] Step 41: arbitrarily selecting a pixel from the remote
sensing image having enhanced linearized edge information as a
current pixel point, and performing Laplacian convolution on the
current pixel to obtain a tensor matrix T;
[0028] where
T = [ .differential. 2 .times. I .differential. x 2 .differential.
2 .times. I .differential. x .times. .differential. y
.differential. 2 .times. I .differential. y .times. .differential.
x .differential. 2 .times. I .differential. y 2 ] ,
##EQU00004##
I represents the remote sensing image having enhanced linearized
edge information, and
.differential. 2 .times. I .differential. x 2 ##EQU00005##
and
.differential. 2 .times. I .differential. y 2 ##EQU00006##
respectively represent a second derivative of the remote sensing
image I along a direction x and a second derivative of the remote
sensing image I along a direction y, wherein x and y respectively
represent an x-axis and a y-axis of an image coordinate system
established by taking any angle of the remote sensing image I as an
origin, a horizontal direction of the remote sensing image I as the
x-axis, and a vertical direction of the remote sensing image I as
the y-axis.
[0029] Step 42: performing matrices spectrum decomposition on the
tensor matrix T to obtain a rod-shaped component and a spherical
component;
[0030] where
T = ( .lamda. 1 - .lamda. 2 ) .times. e .fwdarw. 1 .times. e
.fwdarw. 1 T + .lamda. 2 .function. ( e .fwdarw. 1 .times. e
.fwdarw. 1 T + e .fwdarw. 2 .times. e .fwdarw. 2 T ) , .times. e
.fwdarw. 1 .times. e .fwdarw. 1 T ##EQU00007##
represents the rod-shaped component, and
( e .fwdarw. 1 .times. e .fwdarw. 1 T + e .fwdarw. 2 .times. e
.fwdarw. 2 T ) ##EQU00008##
represents the spherical component;
[0031] Step 43: determining the current pixel as an edge point, if
(.lamda..sub.1-.lamda..sub.2)>.lamda..sub.2, otherwise,
determining the current pixel as a non-edge point; and
[0032] Step 44: repeating the Step 41 to Step 43 until all pixels
of the remote sensing image having enhanced linearized edge
information are determined as current pixels to obtain all the edge
points in the remote sensing image.
[0033] Further, converting all the edge points in the remote
sensing image from an image coordinate system to a parameter
coordinate system to extract a geological linear body from the
parameter coordinate system in step 5 includes:
[0034] Step 51, traversing the parameter coordinate system to
search for a point with a local maximum value, determining the
point with the local maximum value as a peak point, and setting
coordinates of the peak point as (.rho., .theta.), where (.rho.,
.theta.) respectively represent a slope and an intercept of the
geological linear body in the remote sensing image; and
[0035] Step 52: converting the coordinates corresponding to the
peak point in the parameter coordinate system into the image
coordinate system, and connecting the edge points according to the
direction of the edge points and distance of the endpoint to obtain
an image of the geological linear body, thereby completing the
extraction of the geological linear body. Compared with the
existing technologies, the present disclosure has the following
technical effects.
[0036] 1. Combining algorithms and rules such as waveband
selection, image enhancement, boundary detection, and linear
extraction, the present disclosure provides a geological linear
body extraction method based on tensor voting coupled with Hough
transformation. Compared with simple visual interpretation methods,
this method relies less on knowledge and experience of
interpretation experts, thereby considerably shortening the time of
processing, saving a vast amount of manpower, and thus having a
greater practical value and a promotion significance.
[0037] 2. Compared with the Canny edge detection algorithm, the
edge detection algorithm based on tensor voting can provide a
boundary detection on the basis of an edge detection, and also can
provide tensor voting on a grayscale image directly using a
two-dimensional circular voting domain, then provide a voting
interpretation and provide a boundary extraction based on the
saliency of boundary characteristics. This edge detection algorithm
based on tensor voting has robustness.
[0038] 3. The present disclosure can process multi-source remote
sensing data, provide more balanced parameter setting, have better
universality, and have a greater indicative effect on regional
tectonic evolution and plate movement direction.
BRIEF DESCRIPTION OF THE DRAWINGS
[0039] FIG. 1 is an overall flowchart of a geological linear body
extraction method based on tensor voting coupled with Hough
transformation according to the present disclosure;
[0040] FIG. 2 is a remote sensing image of waveband combination
7-5-4 in a research region according to an embodiment of the
present disclosure;
[0041] FIG. 3 is an enhanced image processed by Gaussian high-pass
filtering in research region according to an embodiment of the
present disclosure;
[0042] FIG. 4 is a schematic diagram of tensor voting according to
the present disclosure;
[0043] FIG. 5 is an image obtained by an edge detection after
tensor voting in a research region according to an embodiment of
the present disclosure;
[0044] FIG. 6 is a schematic diagram of Hough transformation
according to the present disclosure; and
[0045] FIG. 7 is an extracted image of a geological linear body
according to the present disclosure.
[0046] Specific contents of the present disclosure are further
described below in detail with reference to the accompanying
drawings.
DETAILED DESCRIPTION
[0047] The remote sensing image in the present disclosure is a
Landsat 8 OLI winter multispectral remote sensing image.
[0048] Specific embodiments of the present disclosure are provided
hereinafter. It is to be noted that the present disclosure is not
limited to the following specific embodiments, and all equivalent
modifications made based on the technical solutions of the present
disclosure shall fall within the scope for protection of the
present disclosure.
[0049] As shown in FIG. 1, this embodiment provides a geological
linear body extraction method based on tensor voting coupled with
Hough transformation, which includes following steps.
[0050] In Step 1, a remote sensing image is pre-processed to obtain
a pre-processed remote sensing image.
[0051] Specifically, in this embodiment, any remote sensing image
is selected from a remote sensing image database as a current
remote sensing image. The current remote sensing image contains a
cloud amount of less than 5%, and has rational polynomial
coefficients (RPC) and statistical image grayscale information,
wherein the image grayscale information includes a gray variance
and a standard deviation for each waveband.
[0052] Furthermore, radiometric calibration, atmospheric
correction, and image cropping are performed on the current remote
sensing image.
[0053] The remote sensing image in this embodiment is a Landsat 8
OLI winter multispectral remote sensing image since a vegetation
coverage is less in winter, and a linear edge is prominent in the
image. The cloud amount in the image should be less than 5%, and
the image has rational polynomial coefficients (RPC) and
statistical image grayscale information, so as to be compared with
geological data, and verify the accuracy of preprocessing.
[0054] The statistical image grayscale information mainly includes
a grayscale variance Di and a standard deviation Si for each
waveband, and the statistical information is as shown in the
following table for subsequent analysis.
TABLE-US-00001 TABLE 1 Landsat 8 OLI multispectral remote sensing
image Minimum Maximum Mean Standard Waveband value value value
deviation Variance Waveband 2 7969 18420 8996.892 430.374
185221.780 Waveband 3 6865 20017 8329.576 617.682 381531.053
Waveband 4 6383 21132 8411.123 869.402 755859.838 Waveband 5 6001
24660 11162.029 2467.527 6088689.496 Waveband 6 5462 41540
11723.583 1649.024 2719280.153 Waveband 7 5432 52154 10169.226
1527.724 2333940.620
[0055] In Step 2, three optimal wavebands are selected from N
multi-spectral wavebands of the pre-processed remote sensing image
to obtain a remote sensing image combined by the optimal wavebands,
wherein N is a natural number greater than or equal to 3.
[0056] Specifically, in this embodiment, three wavebands
corresponding to a maximum optimum index factor (OIF) are selected
from the multi-spectral wavebands of the pre-processed remote
sensing image as the optimal wavebands.
[0057] The OIF is obtained based on Formula (1) as below:
O .times. .times. I .times. .times. F = i = 1 3 .times. s i j = 1 3
.times. R i .times. j ( 1 ) ##EQU00009##
[0058] in Formula (1), S.sub.i represents a standard deviation of
an ith waveband, R.sub.ij represents a correlation coefficient
between the ith waveband, and a jth waveband, i.noteq.j, i=1, 2, .
. . , N, j=1, 2, . . . , N; and
R i .times. j = C .times. o .times. v .function. ( i , j ) D
.function. ( i ) .times. D .function. ( j ) , ##EQU00010##
D(i) and D(j) respectively represent a variance of the ith waveband
and a variance of the jth waveband, and Cov(i,j) represents a
covariance between the ith waveband and the jth waveband.
[0059] In this embodiment, a waveband correlation coefficient
matrix is calculated using six multi-spectral wavebands of the
Landsat 8 OLI remote sensing image, as shown in Table 2.
TABLE-US-00002 TABLE 2 Correlation Coefficient Matrix of Landsat 8
OLI Wavebands in Study Region Wave- Wave- Wave- Wave- Wave- Wave-
Correlation band band band band band band coefficient 2 3 4 5 6 7
Waveband 2 1 0.971 0.916 0.555 0.679 0.702 Waveband 3 0.971 1 0.954
0.629 0.727 0.734 Waveband 4 0.916 0.954 1 0.447 0.821 0.867
Waveband 5 0.555 0.629 0.447 1 0.417 0.235 Waveband 6 0.679 0.727
0.821 0.417 1 0.955 Waveband 7 0.702 0.734 0.867 0.235 0.955 1
[0060] The greater the correlation coefficient is, the more
redundant information between the wavebands is. An analysis of the
correlation coefficient between the wavebands of the image is
disadvantageous to highlighting information of different geological
bodies. In Table 2, the correlation coefficient between Waveband5
and other wavebands is generally small, so first priority may be
given to Waveband5 for image synthesis. As a short-wave infrared
waveband, Waveband7 is sensitive to rocks and specific minerals for
differentiating between major rock types, and detect hydrothermal
rock alteration, and related clay minerals. Second priority may be
given to Waveband7 for image synthesis.
[0061] The waveband combination of the remote sensing image is as
shown in Table 3. As can be seen from Table 3, the combination of
7-5-4 and the combination 7-5-6 respectively have the largest OIF
and the second largest OIF. However, the correlation coefficient
between Waveband5 and Waveband4 is 0.447, and the correlation
coefficient between Waveband5 and Waveband6 is 0.417. Therefore,
the waveband combination 7-5-4 should be selected. It is finally
determined that Waveband7, Waveband5, and Waveband4 respectively
correspond to a red image, a green image, and a blue image obtained
by synthesis, as shown in FIG. 2, to enhance the contrast between
various ground objects. That is, FIG. 2 shows the remote sensing
image combined by the optimal wavebands.
TABLE-US-00003 TABLE 3 Landsat 8 OLI Waveband Combination and
Corresponding OIF No. Waveband combination OIF 1 754 3645.006 2 756
3514.872 3 752 2967.049 4 546 2959.37 5 753 2886.44 6 625 2755.296
7 635 2669.887 8 542 1964.237 9 543 1947.813 10 235 1631.404 11 637
1570.491 12 627 1544.165 13 746 1531.262 14 634 1253.385 15 624
1220.422 16 743 1180.008 17 742 1137.641 18 623 1134.523 19 237
1069.798 20 234 674.891
[0062] In addition, the removal of extra wavebands is intended to
resist the interference to the synthesized image by the other
wavebands. If all wavebands are used, data redundancy will be
resulted in once the correlation coefficient between any two
wavebands is too high.
[0063] In Step 3, sharpening processing is performed on the remote
sensing image combined by the optimal wavebands using Gaussian
high-pass filtering to enhance linearized edge information.
[0064] Specifically, Gaussian high-pass filtering is performed on
the remote sensing image combined by the optimal wavebands to
obtain a filtered remote sensing image H(u,v) based on Formula (2)
as below:
H .function. ( u , v ) = 1 - e - D 2 .function. ( u , v ) 2 .times.
D 0 2 ( 2 ) ##EQU00011##
[0065] where in Formula (2), D(u,v) represents a distance between a
frequency domain midpoint (u,v) of the remote sensing image
combined by the optimal wavebands and a frequency rectangle center,
and D.sub.0 represents a constant. In this embodiment, D.sub.0
represents a cutoff frequency, which is specifically equal to
20.
[0066] In this embodiment, a sharpening effect on a linear edge of
the remote sensing image by Gaussian high-pass filtering is
employed to process the remote sensing image combined by the
optimal wavebands obtained in the Step 2, so as to highlight the
linear edge, and obtain the remote sensing image having enhanced
linearized edge information, which is extracted by the geological
linear body.
[0067] The advantage lies in that as a typical image sharpening
enhancement operator, the Gaussian high-pass filtering can allow
high-frequency components to pass smoothly but suppress
low-frequency components, that is, to enhance edge features but
suppress non-edge noise. As a specific high-pass filtering,
Gaussian high-pass filtering enhances the contrast of images and
makes them more distinguishable.
[0068] FIG. 3 shows a remote sensing image processed by Gaussian
high-pass filtering. Geological and geomorphic lines such as valley
lines and ridge lines are highlighted in the image, presenting
distinct linear landforms.
[0069] In Step 4, edge detection is performed on the remote sensing
image having enhanced linearized edge information in Step 3 to
obtain all edge points in the remote sensing image.
[0070] Further, a boundary point vector and superposed saliency
characteristics of the Step 4 are defined as follows:
DF .function. ( L , k ) = e - ( L 2 + c .times. k 2 ) .sigma. 2
##EQU00012## c = - 1 .times. 6 .times. ( .sigma. - 1 ) .times. log
.function. ( 0.1 ) .pi. 2 ##EQU00012.2##
[0071] wherein DF(L, k) represents a saliency function, L
represents a length of a curve, k represents a curvature, a
represents a voting neighborhood range, and c represents a
coefficient for controlling curvature attenuation.
[0072] Specifically, in Step 41, a pixel is arbitrarily selected,
from the remote sensing image having enhanced linearized edge
information, as a current pixel point, and Laplacian convolution is
performed on the current pixel to obtain a tensor matrix T;
[0073] wherein,
T = [ .differential. 2 .times. I .differential. x 2 .differential.
2 .times. I .differential. x .times. .differential. y
.differential. 2 .times. I .differential. y .times. .differential.
x .differential. 2 .times. I .differential. y 2 ] , I
##EQU00013##
represents the remote sensing image having enhanced linearized edge
information, and
.differential. 2 .times. I .differential. x 2 ##EQU00014##
and
.differential. 2 .times. I .differential. y 2 ##EQU00015##
respectively represent a second derivative of the remote sensing
image I along an x direction and a second derivative of the remote
sensing image I along a y direction, wherein x and y respectively
represent an x-axis and a y-axis of an image coordinate system
established by taking any angle of the remote sensing image I as an
origin, a horizontal direction of the remote sensing image I as the
x-axis, and a vertical direction of the remote sensing image I as
the y-axis.
[0074] In this embodiment, the second derivative in the tensor
matrix T is calculated using a Laplace operator as follows:
L = 0 1 0 1 - 4 1 0 1 0 ##EQU00016##
[0075] By performing singular value decomposition, the following
formula may be obtained:
T = [ e 1 .fwdarw. e 2 .fwdarw. ] .function. [ .lamda. 1 0 0
.lamda. 2 ] .function. [ e 1 .fwdarw. T e 2 .fwdarw. T ] = .lamda.
1 .times. e 1 .fwdarw. .times. e 1 .fwdarw. T + .lamda. 2 .times. e
2 .fwdarw. .times. e 2 .fwdarw. T ##EQU00017##
[0076] In Step 42, matrices spectrum decomposition is performed on
the tensor matrix T to obtain a rod-shaped component and a
spherical component.
[0077] Wherein,
T = ( .lamda. 1 - .lamda. 2 ) .times. e 1 .fwdarw. .times. e 1
.fwdarw. T + .lamda. 2 .function. ( e 1 .fwdarw. .times. e 1
.fwdarw. T + e 2 .fwdarw. .times. e 2 .fwdarw. T ) , e 1 .fwdarw.
.times. e 1 .fwdarw. T ##EQU00018##
represents the rod-shaped component, and
( e 1 .fwdarw. .times. e 1 .fwdarw. T + e 2 .fwdarw. .times. e 2
.fwdarw. T ) ##EQU00019##
represents the spherical component.
[0078] In Step 43, the current pixel is determined as an edge point
if (.lamda..sub.1-.lamda..sub.2)>.lamda..sub.2, otherwise, the
current pixel is determined as a non-edge point.
[0079] Specifically, if .lamda..sub.1.apprxeq..lamda..sub.2, the
current pixel is located at an internal point or intersection of an
region; if both .lamda..sub.1 and .lamda.2 take a very small value,
the current pixel is determined as a non-edge point.
[0080] In Step 44, the Step 41 to the Step 43 are repeated until
all pixels of the remote sensing image having enhanced linearized
edge information are determined as current pixels to obtain all the
edge points in the remote sensing image.
[0081] FIG. 4 is a schematic diagram of tensor voting. Due to the
gray-scale mutation of the linear edge points, after being voted
through 8 neighborhood points, the vectors of the linear edge
points have significant changes, specifically embodied in that
their saliency functions are greater than 0. Therefore, the linear
edge points are determined as edge points and are saved. Because
neighborhood points of the nonlinear edge points are of the same
substance, after being voted through neighborhood points, the
vector sum of the nonlinear edge points has no significant change,
specifically embodied in that their saliency functions are
approximately equal to 0. Therefore, the nonlinear edge points are
determined as non-edge points and are deleted.
[0082] In Step 4 of this embodiment, features to be extracted which
are represented by tensor are subjected to sparse and multi-scale
intensive voting, the vector sum is superimposed, and the
non-geological linear body boundary points are voted in different
directions, and the vector interaction is counteracted, the
boundary points are enhanced because they are only voted from a
certain side, and finally, the boundary of this region is obtained
by voting interpretation. Saliency characteristics of the vector
sum processed by boundary point superposition are calculated using
a saliency function, and the edge detection of the tensor voting
method is realized to obtain binary black and white images, as
shown in FIG. 5.
[0083] In Step 5, all the edge points in the remote sensing image
are converted from an image coordinate system to a parameter
coordinate system to extract a geological linear body from the
parameter coordinate system.
[0084] In this embodiment, the edge points are converted from the
image coordinate system to the parameter coordinate system by using
Hough transformation, and then peak values of the parameter
coordinate system are calculated, and locations corresponding to
the peak values are recorded, such that the geological linear body
is extracted.
[0085] The image coordinate system uses any angle of the remote
sensing image as an origin, a horizontal direction of the remote
sensing image as an x-axis, and a vertical direction of the remote
sensing image as a y-axis.
[0086] The parameter coordinate system is expressed as .rho.=x cos
.theta.+y sin .theta., wherein I and I represent polar coordinates
of the edge points in the parameter coordinate system.
[0087] Specifically, in Step 51, the parameter coordinate system is
traversed to search for a point of a local maximum value, the point
of the local maximum value is determined as a peak point, and
coordinates of the peak point are set as (.rho., .theta.), wherein
(.rho., .theta.) respectively represent a slope and an intercept of
the geological linear body in the remote sensing image.
[0088] In this embodiment, the edge points are converted from the
image coordinate system to the parameter coordinate system by using
the Hough transformation, and the original edge point coordinates
are converted to the parameter coordinate system based on (.rho.,
.theta.), and are continuously accumulated. The .rho.-.theta. space
is traversed to find the point with the local maximum value
(extreme value), which is referred to as the peak point.
[0089] FIG. 6 respectively illustrates, from left to right,
schematic diagrams where pixel points in the image coordinate
system are converted to the parameter coordinate system by using
the Hough transformation, wherein a peak point in the rightmost in
schematic diagram of FIG. 6 is obtained based on continuous
accumulation.
[0090] In Step 52, the coordinates corresponding to the peak point
in the parameter coordinate system are converted into the image
coordinate system, and the edge points are connected into lines
according to the direction of the edge points and the distance of
the endpoint to obtain an image of the geological linear body,
thereby completing the extraction of the geological linear body.
FIG. 7 is a distribution diagram of geological linear bodies
obtained by superimposed Hough transformation in this region.
* * * * *