U.S. patent application number 16/205350 was filed with the patent office on 2019-06-06 for method of extracting warehouse in port from hierarchically screened remote sensing image.
This patent application is currently assigned to Transport Planning and Research Institute Ministry of Transport. The applicant listed for this patent is Transport Planning and Research Institute Ministry of Transport. Invention is credited to Min DONG, Zhuo FANG, Rui LI, Lei MEI, Xiangjun NIE, Yue QI, Lu SUN, Jia TIAN, Dachuan WANG, Jing YANG.
Application Number | 20190171879 16/205350 |
Document ID | / |
Family ID | 62209112 |
Filed Date | 2019-06-06 |
![](/patent/app/20190171879/US20190171879A1-20190606-D00000.png)
![](/patent/app/20190171879/US20190171879A1-20190606-D00001.png)
![](/patent/app/20190171879/US20190171879A1-20190606-D00002.png)
![](/patent/app/20190171879/US20190171879A1-20190606-D00003.png)
United States Patent
Application |
20190171879 |
Kind Code |
A1 |
QI; Yue ; et al. |
June 6, 2019 |
METHOD OF EXTRACTING WAREHOUSE IN PORT FROM HIERARCHICALLY SCREENED
REMOTE SENSING IMAGE
Abstract
A method of extracting a warehouse in a port from a
hierarchically screened remote sensing image includes the following
steps: first, recognizing a texture feature of a remote sensing
image and extracting edge lines of a coast of a port; then,
selecting a sample of an optional irregular texture region and
forming, through a CA transformation, principal component images of
different hierarchies by taking a ratio of a between-class
difference to an intra-class difference being maximum as an
optimization condition; sequentially, extracting a correlation
relationship of the warehouse in the port, and forming a feature
point set with recognized warehouses to be analyzed; and last,
extracting a feature of a visually sensitive image through a scene
image to obtain a feedback selection of a real scene image to
extract the warehouse in the port accurately.
Inventors: |
QI; Yue; (Beijing, CN)
; NIE; Xiangjun; (Beijing, CN) ; DONG; Min;
(Beijing, CN) ; WANG; Dachuan; (Beijing, CN)
; SUN; Lu; (Beijing, CN) ; TIAN; Jia;
(Beijing, CN) ; FANG; Zhuo; (Beijing, CN) ;
LI; Rui; (Beijing, CN) ; MEI; Lei; (Beijing,
CN) ; YANG; Jing; (Beijing, CN) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Transport Planning and Research Institute Ministry of
Transport |
Beijing |
|
CN |
|
|
Assignee: |
Transport Planning and Research
Institute Ministry of Transport
Beijing
CN
|
Family ID: |
62209112 |
Appl. No.: |
16/205350 |
Filed: |
November 30, 2018 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 2207/20016
20130101; G06K 9/00637 20130101; G06T 7/12 20170101; G06T 7/149
20170101; G06T 7/40 20130101; G06T 2207/10032 20130101; G06T
2207/30184 20130101; G06K 9/4676 20130101; G06T 2207/20116
20130101; G06K 9/6247 20130101; G06T 7/13 20170101; G06T 2207/20024
20130101 |
International
Class: |
G06K 9/00 20060101
G06K009/00; G06T 7/149 20060101 G06T007/149; G06T 7/40 20060101
G06T007/40; G06T 7/13 20060101 G06T007/13; G06K 9/62 20060101
G06K009/62; G06K 9/46 20060101 G06K009/46 |
Foreign Application Data
Date |
Code |
Application Number |
Dec 4, 2017 |
CN |
201711256477.6 |
Claims
1. A method of extracting a warehouse in a port from a
hierarchically screened remote sensing image, comprising the
following steps: S100: extracting a coastline of the port based on
an active contour model, successively performing a texture feature
recognition on any region in a remote sensing image to form a sea
area texture region and an irregular texture region, and extracting
edge lines of the coastline of the port; S200: extracting principal
component images of a plurality of hierarchies using insufficient
spectrum features, selecting a sample of the irregular texture
region, and forming, through a CA transformation, principal
component images of different hierarchies with different difference
values by taking a ratio of a between-class difference to an
intra-class difference being maximum as an optimization condition;
S300: accurately recognizing the warehouse in the port using a
spatial relationship feature, extracting a correlation relationship
of the warehouse in the port from the principle component images,
and forming a feature point set with recognized warehouses to be
analyzed; and S400: extracting a feature of a visually sensitive
image from the feature point set through a scene image based on WTA
visual rapid adaptation selection to obtain a feedback selection of
a real scene image to extract the warehouse in the port.
2. The method of extracting the warehouse in the port from the
hierarchically screened remote sensing image according to claim 1,
wherein in step S100, a gray level co-occurrence matrix method is
used for the texture feature recognition, and the texture feature
recognition comprises the following steps: S101: selecting a region
of the remote sensing image, and setting that the region has L gray
level values, and a gray level co-occurrence matrix corresponding
to the region is a matrix having L.times.L orders; S102: selecting
a position (i, j) in the matrix, where (i, j=1, 2, . . . , L),
wherein, an element at the optional position is a first pixel at a
fixed distance from a second pixel having a gray level of i and a
gray level of j, wherein the following fixed positional
relationship exists between the first pixel and the second pixel:
.zeta.=(DX, DY), where .zeta. is a displacement, and DX and DY are
distances in two directions; S103: extracting, according to a
positional relationship between gray level co-occurrence matrixes,
a texture feature quantity including an Angular Second Moment (ASM)
and a contrast CON, wherein
ASM=.SIGMA..sub.i=1.sup.L.SIGMA..sub.j=1.sup.LP.sup.2 (i, j), and
CON=.SIGMA..sub.n=1.sup.Ln.sup.2[.SIGMA..sub.i=1.sup.L.SIGMA..sub.j=1.sup-
.LP(i, j)]|i-j|, where P is a feature vector at the position (i,j),
and n is the number of times the extraction is performed.
3. The method of extracting the warehouse in the ort from the
hierarchically screened remote sensing image according to claim 1,
wherein in step S100, extracting edge lines of the coast of the
port between the sea area texture region and the irregular texture
region using the filter algorithm and optimizing the edge lines of
the coast of the port using the filter algorithm comprises the
following steps: first, acquiring discrete data of a texture
feature and selecting a lowest center frequency when extracting an
image feature in a filter using a discretized Gabor template matrix
and an image data matrix convolution, and then carrying out a
frequency spectrum superposition calculation again to obtain a
filtered image.
4. The method of extracting the warehouse in the port from the
hierarchically screened remote sensing image according to claim 1,
wherein in step S200, before the CA transformation is executed, a
maximized ratio of a between-class variance of an optional data set
to an intra-class variance of the optional data set is extracted
according to the following linear transformation formula: Y=TX,
where T is an ideal transformation matrix, so as to ensure the
maximum separability of the optional data set to provide optimized
basic data for the CA transformation
5. The method of extracting the warehouse in the port from the
hierarchically screened remote sensing image according to claim 4,
wherein a specific algorithm of the ideal transformation matrix is
as follows: S201: .sigma..sub.A is set as a standard deviation of a
class 1 and a class 2 obtained after the CA transformation,
.sigma..sub.w1 and .sigma..sub.w2 are set as intra-class standard
deviations of the class 1 and the class 2, and .sigma..sub.w is set
as an average value of .sigma..sub.w1 and .sigma..sub.w2; S202: a
relationship between a transformed variance and an untransformed
variance is as follows: .sigma..sub.w.sup.2=t.sup.TS.sub.wt,
.sigma..sub.A.sup.2=t.sup.TS.sub.At, where S.sub.w and S.sub.A are
an intra-class scatter matrix and a between-class scatter matrix of
a given sample, and t is a mapping transformation vector; and S203:
the mapping transformation vector t is set as a special value of a
ratio .sigma..sub.A.sup.2/a.sigma..sub.W.sup.2, of the
between-class variance and the intra-class variance, where,
.lamda.=.sigma..sub.A.sup.2/.sigma..sub.W.sup.2=t.sup.TS.sub.At/t.sup.TS.-
sub.wt, when the mapping transformation vector t approximates a
maximum value, (S.sub.A-.LAMBDA.S.sub.W) T=0, where .LAMBDA.
represents a diagonal matrix consisting of all feature values
.lamda., and a matrix T composed of all column vectors t is a
desired ideal transformation matrix.
6. The method of extracting the warehouse in the port from the
hierarchically screened remote sensing image according to claim 1,
wherein in step S300, a correlation relationship of the warehouse
in the port is extracted, the correlation relationship includes a
point feature, a line feature and a plane feature included in
spatial features; a hierarchical relationship feature of the
correlation relationship is acquired by extracting a hierarchy
attribute of the remote sensing image based on a spectral feature
of the remote sensing image.
7. The method of extracting the warehouse in the port from the
hierarchically screened remote sensing image according to claim 6,
wherein the correlation relationship of the warehouse in the port
includes a road relationship, a transshipment square relationship
and an enclosure relationship of the warehouse in the port, and
attributes of a whole are extracted using a spatial correlation
relationship of the warehouse in the port.
8. The method of extracting the warehouse in the port from the
hierarchically screened remote sensing image according to claim 1,
wherein in step S400, the visually sensitive image includes a gray
level, colors, edges, textures and a motion, a visual saliency map
of each position in a scene image is obtained according to
synthesized features, and a mutual competition of a plurality of
the visual saliency maps transfers an inhibition of return of
focus.
9. The method of extracting the warehouse in the port from the
hierarchically screened remote sensing image according to claim 8,
wherein the mutual competition and the inhibition of the visual
saliency map comprises the following steps: S401: selecting a
plurality of parallel and separable feature maps from the feature
point set, and recording a hierarchy attribute of each position in
a feature dimension on a feature map to obtain a saliency of each
position in different feature dimensions; S402: merging saliencies
of different feature maps to obtain a total saliency measure, and
guiding a visual attention process; and S403: dynamically
selecting, through a WTA network, a position with the highest
saliency from the saliency map as a Focus Of Attention (FOA), and
then performing the processing circularly through the inhibition of
return until a real scene image is obtained.
10. The method of extracting the warehouse in the port from the
hierarchically screened remote sensing image according to claim 1,
further comprising: a step S500 of tracking a nonlinear filtering
feature, comprising: separately extracting a filtering feature
obtained in step S100 using hierarchical image attributes extracted
through S100, S200, S300 and S400, and performing a tracking in a
remote sensing analysis image according to a texture feature
extracted according to a hierarchical analysis to compensate for an
attribute that cannot be directly extracted by tracking the texture
feature to form an interpreted remote sensing image, and comparing
the interpreted remote sensing image after being formed with a real
scene image in step S400 to remove an inaccurate tracked texture
and keep a rational tracked texture.
Description
CROSS REFERENCE TO THE RELATED APPLICATIONS
[0001] The present application claims priority to Chinese Patent
Application No. 2017112564776 filed on Dec. 4, 2017, the entire
content of which is incorporated herein by reference.
TECHNICAL FIELD
[0002] The present invention relates to the field of remote sensing
technologies, and more particularly to a method of extracting a
warehouse in a port from a hierarchically screened remote sensing
image.
BACKGROUND
[0003] Remote sensing images have been widely used in various
aspects, and will be used further as remote sensing image
recognition technologies develop further. In applications of remote
sensing, information is collected without directly contacting a
related target, and the collected information can be interpreted,
classified and recognized. With the use of a remote sensing
technology, a great quantity of earth observation data can be
acquired rapidly, dynamically, and accurately.
[0004] As a hub for marine transportation, port plays an extremely
important role and is therefore received more and more attentions,
becoming an important research direction in marine transportation
traffic planning. In the establishment and planning of a port, port
data should be collected first, that is, the various ground objects
in a port and their positions should be acquired, a logistics
warehouse behind a storage yard is an important ground object in a
port, and moreover, logistics warehouses are also crucial for a
port.
[0005] However, it is somewhat difficult to recognize, based on a
remote sensing image, a warehouse in the rear of a port, in the
prior art, for example, a method of extracting an image of a
logistics warehouse behind a storage yard in a port, which is
disclosed in Patent Application No. 201610847354.9, includes: (1)
applying a lee sigma edge extraction algorithm to a waveband of a
remote sensing image, the algorithm using a specific edge filter to
create two independent edge images: a bright-edged image and a
dim-edged image, from the original image; (2) carrying out a
multi-scale segmentation for the bright-edged image and the
dim-edged image together with the remote sensing image to obtain an
image object; (3) classifying the ones of the obtained image
objects having a big blue waveband ratio into a class A, and
removing, using a brightness mean feature, the ones in the class A
having a relative low brightness mean from the class A; (4) and
removing the objects smaller than a specified threshold from the
class A using the Normalized Difference Vegetation Index (NDVI) to
obtain the category of a warehouse with a blue roof. Based on
features of data and those of a logistics warehouse behind a
storage yard in a port, an image can be extracted accurately at a
high processing efficiency.
[0006] Taking an overall view of the foregoing technical solution,
actually existing problems and the currently widely used technical
solutions, the following major defects are found:
[0007] (1) first, no special method is currently available for the
remote sensing reorganization of a logistics warehouse in the rear
of a port, because existing methods are applicable to recognize
other ground objects and incapable of accurately recognizing a
warehouse in a port according to features of the warehouse in the
port, moreover, because of the lack of pertinence, the processing
of remotely sensed big data is low in efficiency;
[0008] (2) second, most of existing data processing methods are
based on the direct extraction of a remote sensing image, the
biggest defect of this extraction mode is heave original data
processing workload, and some undesired data or data out of this
scope are usually taken into consideration during this calculation
process, thus further increasing the complicity of data processing;
and
[0009] (3) last, in an existing data processing process, most of
feature processing operations are based on spectral features,
although full-color remote sensing images have been developed,
spectral feature is still disadvantaged in insufficient spectral
information, making it necessary to conduct an advanced computation
and an interpolation operation for an approximation recovery during
a recognition process, however, this process usually triggers a
correction algorithm, thus, to obtain a recognized feature that is
close to reality, a large amount of calculation needs to be
executed, furthermore, an algorithm correction is circulated during
this process, leading to a larger computation load.
SUMMARY
[0010] A technical solution adopted by the present invention to
solve the technical problems is a method of extracting a warehouse
in a port from a hierarchically screened remote sensing image,
comprising the following steps:
[0011] S100: extracting a coastline of the port based on an active
contour model, successively performing texture feature recognition
on any region in a remote sensing image to form a sea area texture
region and an irregular texture region, and extracting edge lines
of the coastline of the port;
[0012] S200: extracting principal component images of a plurality
of hierarchies using insufficient spectrum features, optionally
selecting a sample of the irregular texture region, and forming,
through a CA transformation, principal component images of
different hierarchies with different difference values by taking
the ratio of a between-class difference to an intra-class
difference being maximum as an optimization condition;
[0013] S300: accurately recognizing the warehouse in the port using
a spatial relationship feature, extracting a correlation
relationship of the warehouse in the port from the principle
component images, and forming a feature point set with recognized
warehouses to be analyzed;
[0014] S400: extracting a feature of a visually sensitive image
from the feature point set through a scene image based on WTA
visual rapid adaptation selection to obtain a feedback selection of
a real scene image to extract the warehouse in the port.
[0015] As a preferred technical solution of the present invention,
in step S100, a gray level co-occurrence matrix is used to
recognize a texture feature, and the recognition includes the
following steps:
[0016] S101: optionally selecting a region of the remote sensing
image, and setting that the region has L gray level values, in this
case, a gray level co-occurrence matrix corresponding to the region
is a matrix having LXL orders;
[0017] S102: selecting an optional position (i,j) in the matrix,
where (i, j=1, 2, . . . , L), in this case, an element at the
optional position is a pixel at a fixed distance from a pixel
having a gray level of i and has a gray level of j, wherein the
following fixed positional relationship exists between the two
pixels: .zeta.=(DX, DY), where .zeta. is a displacement, and DX and
XY are distances in two directions;
[0018] S103: extracting, according to a positional relationship
between the gray level co-occurrence matrixes, a texture feature
quantity such as an Angular Second Moment (ASM) and a contrast CON,
wherein ASM=.SIGMA..sub.i=1.sup.L.SIGMA..sub.j=1.sup.LP.sup.2 (i,
j) and
CON=.SIGMA..sub.n=1.sup.Ln.sup.2[.SIGMA..sub.i=1.sup.L.SIGMA..sub.j=1.sup-
.LP(i, j)]|i-j|, where P is a feature vector at the position (i,j),
and n is the number of times extraction is performed.
[0019] As a preferred technical solution of the present invention,
in step S100, extracting edge lines of the coast of the port
between the sea area texture region and the irregular texture
region using the filter algorithm and optimizing the edge lines of
the coast of the port using the filter algorithm specifically
includes the following steps: first, acquiring discrete data of a
texture feature and selecting a lowest center frequency when
extracting an image feature in a filter using a discretized Gabor
template matrix and an image data matrix convolution, and then
carrying out a frequency spectrum superposition calculation again
to obtain a filtered image.
[0020] As a preferred technical solution of the present invention,
in step S200, before the CA transformation is executed, the
maximized ratio of a between-class variance of an optional data set
to an intra-class variance of the data set is extracted according
to the following linear transformation formula: Y=TX, where T is an
ideal transformation matrix, so as to ensure the maximum
separability of the data set to provide optimized basic data for
the CA transformation.
[0021] As a preferred technical solution of the present invention,
a specific algorithm of the ideal transformation matrix is as
follows:
[0022] S201: .sigma..sub.A is set as a standard deviation of a
class 1 and a class 2 obtained after the transformation,
.sigma..sub.w1 and .sigma..sub.w2 are set as intra-class standard
deviations of the class 1 and the class 2, and .sigma..sub.w is set
as the average value of .sigma..sub.w1 and .sigma..sub.w2;
[0023] S202: the relationship between a transformed variance and an
untransformed variance is as follows:
[0024] .sigma..sub.w.sup.2=t.sup.TS.sub.wt,
.sigma..sub.A.sup.2=t.sup.TS.sub.At, where S.sub.w and S.sub.A are
an intra-class scatter matrix and a between-class scatter matrix of
a given sample, and t is a mapping transformation vector; and
[0025] S203: the mapping transformation vector t is set as a
special value of the ratio .sigma..sub.A.sup.2/.sigma..sub.W.sup.2
of the between-class variance and the intra-class variance, that
is,
.lamda.=.sigma..sub.A.sup.2/.sigma..sub.W.sup.2=t.sup.TS.sub.At/t.sup.TS.-
sub.wt, when the mapping transformation vector t approximates a
maximum value, (S.sub.A-.LAMBDA.S.sub.W) T=0, where A represents a
diagonal matrix consisting of all feature values .lamda., and a
matrix T composed of all column vectors t is a desired ideal
transformation matrix.
[0026] As a preferred technical solution of the present invention,
in step S300, a correlation relationship of the warehouse in the
port is extracted, the correlation relationship includes a point
feature, a line feature and a plane feature included in spatial
features; a hierarchical relationship feature of the correlation
relationship is acquired by extracting a hierarchy attribute of the
remote sensing image based on a spectral feature of the remote
sensing image.
[0027] As a preferred technical solution of the present invention,
the correlation relationship of the warehouse in the port includes
a road relationship, a transshipment square relationship and an
enclosure relationship of the warehouse in the port, and attributes
of a whole are extracted using a spatial correlation relationship
of the warehouse in the port.
[0028] As a preferred technical solution of the present invention,
in step S400, the visually sensitive image includes a gray level,
colors, edges, textures and a motion, a visual saliency map of each
position in a scene image is obtained according to synthesized
features, and the mutual competition of a plurality of the visual
saliency maps transfers the inhibition of return of focus.
[0029] As a preferred technical solution of the present invention,
the competition and inhibition of the visual saliency map includes
the following steps:
[0030] S401: selecting a plurality of parallel and separable
feature maps from the feature point set, and recording a hierarchy
attribute of each position in a feature dimension on a feature map
to obtain the saliency of each position in different feature
dimensions;
[0031] S402: merging saliencies of different feature maps to obtain
a total saliency measure, and guiding a visual attention process;
and
[0032] S403: dynamically selecting, through a WTA network, the
position with the highest saliency from the saliency map as the
Focus Of Attention (FOA), and then performing the processing
circularly through the inhibition of return until a real scene
image is obtained.
[0033] As a preferred technical solution of the present invention,
the method further includes a step S500 of tracking a nonlinear
filtering feature, including: separately extracting the filtering
feature obtained in step S100 using hierarchical image attributes
extracted through the execution of the foregoing four steps, and
performing a tracking in a remote sensing analysis image according
to a texture feature extracted according to a hierarchical analysis
to compensate for an attribute that cannot be directly extracted by
tracking a texture feature, so as to form an interpreted remote
sensing image, and comparing the formed remote sensing image with a
real scene image in step S400 so as to remove an inaccurate tracked
texture and keep a rational tracked texture.
[0034] Compared with the prior art, the present invention has the
following beneficial effects: by extracting the original attribute
using a texture feature and dividing edge lines of a coast of a
port using a filter, the present invention timely removes data that
does not need to be processed and thus reduces the amount of the
data sequentially processed; after performing the foregoing
processing, the present invention performs a CA transformation to
cause the remote sensing image data to be capable of being
hierarchized, and then hierarchizes the remote sensing image data
using insufficient spectrum information according to a spatial
position feature of a warehouse to obtain principal component
images of different hierarchies, obtains attributes of a single
entity using a spatial relationship among the principal component
images and thereby obtains attributes of a whole, thus, the present
invention has a high pertinence; moreover, the present invention
accurately acquires attributes of a warehouse in a port using a WTA
visual rapid adaptation selection algorithm after forming a feature
point set, and uses a track optimization algorithm to compensate
for distorted data or data that is not acquired through remote
sensing to form a complete interpreted image, thus avoiding the use
of a correction algorithm and the calculation of an approximate
interpolation.
BRIEF DESCRIPTION OF THE DRAWINGS
[0035] FIG. 1 is a schematic diagram illustrating a flow according
to the present invention;
[0036] FIG. 2 is a diagram illustrating a flow of extracting a
feature by a Gabor filter according to the present invention;
[0037] FIG. 3 is a schematic diagram illustrating a structure of a
visual model according to the present invention; and
[0038] FIG. 4 is a schematic diagram illustrating a competition and
inhibition structure of a visual saliency map according to the
present invention.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[0039] Technical solutions of the present invention will be
described clearly and completely below in conjunction with
accompanying drawings set forth therein, and apparently, the
embodiments described herein are merely a part of, but not all of
the embodiments of the present invention. All other embodiments
devised by those of ordinary skill without any creative work based
on the embodiments described herein should fall within the scope of
the present invention.
Embodiment
[0040] As shown in FIG. 1-FIG. 3, the present invention provides a
method of extracting a warehouse in a port from a hierarchically
screened remote sensing image, comprising the following steps:
[0041] S100: extracting a coastline of the port based on an active
contour model, successively performing texture feature recognition
on any region in a remote sensing image to form a sea area texture
region and an irregular texture region, and extracting edge lines
of a coastline of the port using a filter algorithm.
[0042] In step S100, a gray level co-occurrence matrix is used to
recognize a texture feature, the specific recognition includes the
following steps:
[0043] S101: optionally selecting a region of the remote sensing
image, and setting that the region has L gray level values, in this
case, a gray level co-occurrence matrix corresponding to the region
is a matrix having LXL orders;
[0044] S102: selecting an optional position (i,j) in the matrix,
wherein (I, j=1, 2 . . . . , L), an element at the optional
position is a pixel at a fixed distance from a pixel having a gray
level of i and has a gray level of j, wherein the following fixed
positional relationship exists between the two pixels: .zeta.=(DX,
DY), where .zeta. is a displacement, and DX and XY are distances in
two directions; and
[0045] S103: extracting, according to a positional relationship
between the gray level co-occurrence matrixes, a texture feature
quantity such as an Angular Second Moment (ASM) and a contrast CON,
wherein ASM=.SIGMA..sub.i=1.sup.L.SIGMA..sub.j=1.sup.LP (i, j) and
CON=.SIGMA..sub.n=1.sup.Ln.sup.2[.SIGMA..sub.i=1.sup.L.SIGMA..sub.j=1.sup-
.LP(i, j)]|i-j|, where P is a feature vector at the position (i,j),
and n is the number of times extraction is performed.
[0046] The gray level co-occurrence matrix mentioned in the
foregoing steps is a common means for processing a texture feature
of a remote sensing image, and a gray level co-occurrence matrix is
used herein for processing a texture feature mainly for the
following reasons:
[0047] 1: texture features of local image regions should be counted
before the gray level co-occurrence matrix performs a texture
feature analysis, and the extraction method provided herein needs
to perform an extraction operation for a plurality of times using
local features, thus, the extraction method provided herein is
capable of performing an extraction operation in the original
remote sensing image and directly using the extracted information
in subsequent steps;
[0048] 2: in the use of the gray level co-occurrence matrix,
generally, more than one texture feature is extracted in the gray
level co-occurrence matrix, thus, a plurality of texture features
can be used herein as a basis for a multi-hierarchical screening,
moreover, it is common that some hierarchies lose image information
during a multi-hierarchical screening, however, the extraction of a
plurality of texture features can compensate for the lost
information to a certain extent, thus further improving the quality
of image extraction;
[0049] 3: the most important point is that most of the texture
features extracted using a gray level co-occurrence matrix are
related with each other, that is, the texture features extracted
using a gray level co-occurrence matrix can visually reflect a
spatial relationship whose application is emphasized herein and
which is even a basis for the accurate recognition of a warehouse
in a port; in addition to facilitating the direct implementation of
a subsequent operation, decreasing unnecessary calculation, and
increasing the speed of calculation, the pre-analysis of a
corresponding texture feature is also advantaged in being
independent from a spatial relationship in an actual operation
although capable of visually embodying a spatial relationship of
textures, therefore, as an earlier data processing, the
pre-analysis, although average in texture feature recognition,
reduces the amount of the data processed, increases the speed of
operation, and can lay a foundation for a subsequent accurate
recognition.
[0050] For the filter algorithm used in step S100, it should also
be noted that in this step, due to the selection of a common Gabor
filter as a filter algorithm, optimizing edge lines of a coast in a
port using a filter algorithm refers specifically to: first,
obtaining the discrete data obtained in the foregoing step,
selecting, using a discretized Gabor template matrix and an image
data matrix convolution, the lowest center frequency when
extracting an image feature using a filter, and performing a
frequency spectrum superposition calculation again to calculate a
filtered image.
[0051] In the actual application of the Gabor filter algorithm, it
should be emphasized herein that it is well known that a big
convolution matrix will increase a computation burden sharply, this
problem exists in the present invention as well, for this sake, a
convolution matrix needs to be optimized further to conquer the
problem of heave computation burden, and as shown in FIG. 2, the
optimization is specifically realized by:
[0052] first, setting Fourier transformations of two convolution
matrixes f.sub.1 and f.sub.2 as F.sub.1 and F.sub.2, in this case,
the following equations are obtained: F.sub.1=fft (f.sub.1), and
F.sub.2=fft (f.sub.2);
[0053] according to the convolution theorem, the following equation
is obtained: conv (f.sub.1, f.sub.2)=ifft (F.sub.1*F.sub.2), where
cony represents a convolution, fft represents a Fourier
transformation, ifft represents an inverse transformation of a
Fourier transformation, and F.sub.1*F.sub.2 represents the
multiplying of corresponding elements in two matrixes F.sub.1 and
F.sub.2.
[0054] By performing an optimization operation through the
execution of the foregoing steps, the amount of calculation
conducted to extract multi-hierarchical data is remarkably reduced,
thus significantly increasing the efficiency of calculation,
improving the actual handling capacity, and preventing calculation
from being circularly repeated redundantly.
[0055] S200: extracting principal component images of a plurality
of hierarchies using insufficient spectrum features, optionally
selecting a sample of the irregular texture region, and forming,
through a CA transformation, principal component images of
different hierarchies with different difference values by taking
the ratio of a between-class difference to an intra-class
difference being maximum as an optimization condition;
[0056] The CA transformation specifically refers to a method for
the discriminant analysis of feature extraction, which is applied
to extracting a feature and capable of maximizing the ratio of a
between-class variance of any data set to an intra-class variance
of the data set to ensure the maximum separability of the data
set.
[0057] As a canonical analysis transformation (that is, a method
for the discriminant analysis of feature extraction) is an
orthogonal linear transformation based on a classified statistic
feature obtained through a sample analysis, in step S200, before
the CA transformation is executed, the maximized ratio of a
between-class variance of an optional data set to an intra-class
variance of the data set is extracted according to the following
linear transformation formula: Y=TX, where T is an ideal
transformation matrix, so as to ensure the maximum separability of
the data set to provide optimized basic data for the CA
transformation.
[0058] A specific algorithm of the ideal transformation matrix is
as follows:
[0059] S201: .sigma..sub.A is set as a standard deviation of a
class 1 and a class 2 obtained after the transformation,
.sigma..sub.w1 and .sigma..sub.w2 are set as intra-class standard
deviations of the class 1 and the class 2, and .sigma..sub.w is set
as the average value of .sigma..sub.w1 and .sigma..sub.w2;
[0060] S202: the relationship between a transformed variance and an
untransformed variance is as follows:
[0061] .sigma..sub.w.sup.2=t.sup.TS.sub.wt,
.sigma..sub.A.sup.2=t.sup.TS.sub.At, where S.sub.w and S.sub.A are
an intra-class scatter matrix and a between-class scatter matrix of
a given sample, and t is a mapping transformation vector; and
[0062] S203: the mapping transformation vector t is set as a
special value of the ratio .sigma..sub.A.sup.2/.sigma..sub.W.sup.2
of the between-class variance and the intra-class variance, that
is,
.lamda.=.sigma..sub.A.sup.2/.sigma..sub.W.sup.2=t.sup.TS.sub.At/t.sup.TS.-
sub.wt, when the mapping transformation vector t approximates a
maximum value, (S.sub.A-.LAMBDA.S.sub.W) T=0, where A represents a
diagonal matrix consisting of all feature values .lamda., and a
matrix T composed of all column vectors t is a desired ideal
transformation matrix.
[0063] To sum up, by taking the ratio of a between-class difference
to an intra-class difference being maximum as an optimization
condition, the CA transformation allows a first model corresponding
to a maximum feature value to contain maximum separable
information, and so on and so forth, a plurality of separable
information axes can be obtained through the CA transformation in a
plurality of dimensions, in this way, principle component images of
a plurality of hierarchies can be extracted using insufficient
spectrum features, moreover, it also should be noted that the CA
transformation also decreases the number of the dimensions of a
data space while increasing the separability of a class and thus
reduces the complexity of an actual operation.
[0064] In the foregoing steps, the use of the CA transformation
causes data concentrated in a remote sensing image to be separable,
that is, the data subjected to the CA transformation have an
excellent separability so that data can be hierarchically extracted
without loss in subsequent transformations, resulting in that
principle component images of the original remote sensing image can
be hierarchically extracted.
[0065] Step S300: accurately recognizing the warehouse in the port
using a spatial relationship feature, extracting a correlation
relationship of the warehouse in the port from the principle
component images, the correlation relationship of the warehouse in
the port includes a road relationship, a transshipment square
relationship and an enclosure relationship of the warehouse in the
port, extracting attributes of a whole using the spatial
correlation relationship of the warehouse in the port, and forming
a feature point set with recognized warehouses to be analyzed.
[0066] Spatial feature, which is seldom used in remote sensing
image processing, is mainly realized as a relationship pattern in a
remote sensing image, that is, in a specific remote sensing image
analysis, the final recognition of a target is realized using
correlated features, in a remote sensing image, it is not easy to
recognize attributes of an entity in a certain relationship by
separately observing the entity, however, when a correlation of
spatial relationship is introduced, attributes of an entity can be
known using the correlated spatial relationship, and even
attributes of a whole consisting of entities can be recognized
using a structural feature and a relationship feature.
[0067] In step S300, a correlation relationship of the warehouse in
the port is extracted, the correlation relationship includes a
point feature, a line feature and a plane feature included in
spatial features; a hierarchical relationship feature of the
correlation relationship is acquired by extracting a hierarchy
attribute of the remote sensing image based on a spectral feature
of the remote sensing image.
[0068] S400: extracting a feature of a visually sensitive image
from a scene image based on WTA visual rapid adaptation selection
to obtain a feedback selection of a real scene image to extract the
warehouse in the port.
[0069] As shown in FIG. 4, in step S400, the visually sensitive
image includes a gray level, colors, edges, textures and a motion,
a visual saliency map of each position in a scene image is obtained
according to synthesized features, and the mutual competition of a
plurality of the visual saliency maps transfers the inhibition of
return of focus.
[0070] the competition and inhibition of the visual saliency map
includes the following steps:
[0071] S401: selecting a plurality of parallel and separable
feature maps from the feature point set, and recording a hierarchy
attribute of each position in a feature dimension on a feature map
to obtain the saliency of each position in different feature
dimensions;
[0072] S402: merging saliencies of different feature maps to obtain
a total saliency measure, and guiding a visual attention process;
and
[0073] S403: dynamically selecting, through a WTA network, the
position with the highest saliency from the saliency map as the
Focus Of Attention (FOA), and then performing the processing
circularly through the inhibition of return until a real scene
image is obtained.
[0074] Moreover, in the present invention, it also should be noted
that the method further includes a step S500 of tracking a
nonlinear filtering feature, including: separately extracting the
filtering feature obtained in step S100 using hierarchical image
attributes extracted through the foregoing four steps, and
performing a tracking in a remote sensing analysis image according
to a texture feature extracted according to a hierarchical analysis
to compensate for an attribute that cannot be directly extracted by
tracking a texture feature, so as to form an interpreted remote
sensing image, and comparing the formed remote sensing image with a
real scene image in step S400 after forming the interpreted remote
sensing image so as to remove an inaccurate tracked texture and
keep a rational tracked texture.
[0075] In conclusion, the main features of the present invention
lie in that: by extracting the original attribute using a texture
feature and dividing edge lines of a coast of a port using a
filter, the present invention timely removes data that does not
need to be processed and thus reduces the amount of the data
sequentially processed; after performing the foregoing processing,
the present invention performs a CA transformation to cause the
remote sensing image data to be capable of being hierarchized, and
then hierarchizes the remote sensing image data using insufficient
spectrum information according to a spatial position feature of a
warehouse to obtain principal component images of different
hierarchies, obtains attributes of a single entity using a spatial
relationship among the principal component images and thereby
obtains attributes of a whole, thus, the present invention has a
high pertinence; moreover, the present invention accurately
acquires attributes of a warehouse in a port using a WTA visual
rapid adaptation selection algorithm after forming a feature point
set, and uses a track optimization algorithm to compensate for
distorted data or data that is not acquired through remote sensing
to form a complete interpreted image, thus avoiding the use of a
correction algorithm and the calculation of an approximate
interpolation.
[0076] It is apparent for those skilled in the art that the present
invention is not limited to details of the foregoing exemplary
embodiments and the present invention can be realized in other
specific forms without departing from the spirit or basic features
of the present invention. Thus, the embodiments should be regarded
as exemplary but not limitative in any aspect; because the scope of
the present invention is defined by appended claims but not the
foregoing description, the present invention is intended to cover
all the variations falling within the meaning and scope of an
equivalent of the claims. Any reference symbol in the claims should
not be construed as limiting a relevant claim.
* * * * *