CN109635722A - A kind of high-resolution remote sensing image crossing automatic identifying method - Google Patents
A kind of high-resolution remote sensing image crossing automatic identifying method Download PDFInfo
- Publication number
- CN109635722A CN109635722A CN201811506319.6A CN201811506319A CN109635722A CN 109635722 A CN109635722 A CN 109635722A CN 201811506319 A CN201811506319 A CN 201811506319A CN 109635722 A CN109635722 A CN 109635722A
- Authority
- CN
- China
- Prior art keywords
- road
- crossing
- piecemeal
- angle
- center
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 20
- 238000000605 extraction Methods 0.000 claims abstract description 25
- 239000000284 extract Substances 0.000 claims abstract description 9
- 241000209094 Oryza Species 0.000 claims description 14
- 235000007164 Oryza sativa Nutrition 0.000 claims description 14
- 235000009566 rice Nutrition 0.000 claims description 14
- 238000001914 filtration Methods 0.000 claims description 11
- 230000006870 function Effects 0.000 claims description 9
- 230000001427 coherent effect Effects 0.000 claims description 8
- 230000002708 enhancing effect Effects 0.000 claims description 7
- 238000009792 diffusion process Methods 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 4
- 238000004458 analytical method Methods 0.000 claims description 3
- 238000004364 calculation method Methods 0.000 claims description 2
- 238000001514 detection method Methods 0.000 description 9
- 238000001228 spectrum Methods 0.000 description 8
- 230000003595 spectral effect Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000012706 support-vector machine Methods 0.000 description 4
- VMXUWOKSQNHOCA-UKTHLTGXSA-N ranitidine Chemical compound [O-][N+](=O)\C=C(/NC)NCCSCC1=CC=C(CN(C)C)O1 VMXUWOKSQNHOCA-UKTHLTGXSA-N 0.000 description 3
- 230000000007 visual effect Effects 0.000 description 3
- 235000013339 cereals Nutrition 0.000 description 2
- 238000012512 characterization method Methods 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 230000009466 transformation Effects 0.000 description 2
- 241000196324 Embryophyta Species 0.000 description 1
- 239000004035 construction material Substances 0.000 description 1
- 239000004744 fabric Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/182—Network patterns, e.g. roads or rivers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/56—Extraction of image or video features relating to colour
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Physics & Mathematics (AREA)
- Multimedia (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Data Mining & Analysis (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Bioinformatics & Cheminformatics (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Image Analysis (AREA)
Abstract
本发明公开一种高分辨率遥感影像路口自动识别方法，其包括以下步骤：S1，道路基元提取；S2，连通道路骨架：连接邻近道路基元，形成完整道路骨架；S3，生成候选路口坐标：对提取的道路骨架进行细化操作，获取道路骨架交叉点，并作为候选路口坐标；S4，构建路口相似度指数；S5，基于分块矩形旋转模型识别路口。本发明无需人工干预，能够自动提取路口，提取的准确性和完整性均优于现有同类研究成果。
The invention discloses a high-resolution remote sensing image intersection automatic identification method, which comprises the following steps: S1, road primitive extraction; S2, connected road skeleton: connecting adjacent road primitives to form a complete road frame; S3, generating candidate intersection coordinates : refine the extracted road skeleton, obtain the intersection of the road skeleton and use it as the candidate intersection coordinates; S4, construct the intersection similarity index; S5, identify the intersection based on the block rectangle rotation model. The invention does not need manual intervention, can automatically extract the intersection, and the accuracy and completeness of the extraction are superior to the existing similar research results.
Description
Technical field
Field is extracted the present invention relates to remote sensing image information more particularly to a kind of high-resolution remote sensing image crossing is known automatically
Other method.
Background technique
Extracting road information using high-resolution remote sensing image is a kind of economical and efficient means, can not only be compared by visual observation
To very intuitively and easily verifying extract as a result, and its cost be also significantly lower than based on field measured data or motion track
The road information acquisition modes of data.But object spectrum abundant information, complex distribution in remote sensing image, " the different spectrum of jljl " with
The phenomenon that " foreign matter is with spectrum ", is generally existing, and same type of spectral characteristic of ground difference becomes larger, different types of object spectrum phase
Seemingly, the Spectral divisibility of image reduces.Currently, extracted from high-resolution remote sensing image road information still face it is many it is difficult with
Challenge.
Crossing (plane crossing is most commonly seen, and " crossing " mentioned by the present invention all refers to plane crossing) is the weight of road network
Component part is wanted, road intersection is located at.Accurate intersection information is to Image registration, intelligent transportation, GIS data update and road
Variation detection etc. is significant.In general, urban road is more straight, therefore it can determine whether out urban road network using crossing
General configuration, such as: connection adjacent intersection can get the general picture of this area's road network.Due to road construction material and surrounding its
His atural object is similar, there is similar spectral signature, and on the other hand, crossing is easy by Adjacent Buildings, parking lot, shade, plant
The interference such as quilt, vehicle.It is not yet thoroughly solved currently, extracting crossing from high-resolution remote sensing image, using high-definition remote sensing
The business software system that image automatically extracts crossing not yet occurs.
It is less for the research of crossing extraction in known references, not yet occur being suitable for high-resolution remote sensing image crossing
Antihunt means and business software.Need to solve two big technical problems: first, global automatic acquisition high-resolution remote sensing image crossing
Position candidate point；Second, using the local detection crossing of piecemeal rectangle rotating model of design, improve crossing identification accuracy and
Integrality.
Summary of the invention
The purpose of the present invention is to provide a kind of high-resolution remote sensing image crossing automatic identifying methods.
The technical solution adopted by the present invention is that:
A kind of high-resolution remote sensing image crossing automatic identifying method comprising following steps:
S1, road primitives extract: first to remote sensing image carry out road class and non-rice habitats class coarse extraction, then to road class into
Row non-rice habitats noise removal, finally using the form of binary morphology operation repairing road primitive.
S2 is connected to road skeleton: connecting neighbouring road primitives, forms complete road skeleton.
S3 generates candidate crossing coordinate: carrying out Refinement operation to the road skeleton of extraction, is figured using Rutoviz intersection
Method obtains road skeleton crosspoint, and as candidate crossing coordinate.(point methods are intersected by above-mentioned global detection skeleton, can be obtained
To candidate crossing center point coordinate.But the local nonlinearity deformation as caused by thinning algorithm generates the non-crossing crosspoint in part,
In the case where guaranteeing recall ratio, the crosspoint at increased non-crossing need to be filtered out by part detection.)
S4 constructs crossing index similarity, specific steps are as follows:
S4.1 constructs piecemeal rectangle template.The present invention devises a kind of novel piecemeal rectangle rotating model identification crossing,
For the model using piecemeal rectangle as template, template is divided into center and the part of frontier district two, and center counts the letter at crossing center
Breath, marginal zone count the road section information being connected with crossing center.The length L of piecemeal rectangle template center1It is set as road
Mean breadth, boundary section length L2It is set as K*L1, K is threshold value constant.
S4.2, mean value angle.
S4.2.1, coherent filtering enhance image edge.
Coherence enhancing diffusion filtering (hereinafter referred to as coherent filtering) be one is improve image border respectively to
The effective ways of anisotropic non-linear tensor diffusion.Linear character is that the important feature of road is also the weight that road distinguishes other atural objects
Will foundation, to crossing image implement coherent filtering not only can smooth image, but also the contrast and road at edge can be enhanced
Directional information.Coherent filtering is carried out to image to be processed, filtered image road linear edge is enhanced, in road surface
Pixel difference be greatly improved.
S4.2.2 calculates mean value angle.
Enhancing image edge is filtered using coherence enhancing diffusion, counts each item in each piecemeal rectangle template frontier district
The angular distribution situation at edge.In the window W of M*N, the edge for having M angle different, each edge is remembered respectively with X-axis angle
Make: θ1,θ2,...,θM, wherein θi∈[0,π).It is A that current rectangle template i, which rotates angle and the angle of X-axis,i, i in the present invention
Value range is [1,36].Current rectangle template i angle mean value SIIt is defined as follows:
Wherein τ is threshold value constant, AiIt calculates as follows:
S4.3, spatial autocorrelation express homogeney.
Texture is the important feature of remote sensing image, the resolution of the areal image usually obtained from dissimilar sensor
Rate and texture characteristics are not quite similar, and the homogeneous feature of road is relatively stable in image.Spatial autocorrelation indicators
Moran ' I can rationally characterize the homogeneous feature (one of important feature of road) of image texture.Compared to other types of texture
Feature (such as variance, energy, entropy, contrast, the degree of correlation), Moran ' the I index calculating of characterization homogeneity characteristic is simple, the time is multiple
Miscellaneous degree is low, is influenced by sensor type smaller.The present invention is using Moran ' the I Mean value of index of rectangle Statistical Area as local grain spy
Sign, specific calculating are as follows:
Wherein I (i, j) indicates Moran ' the I index value of pixel (i, j), and Ω is statistical regions, and N is picture in statistical regions
The sum of element.
S4.4, color away from.
Color is the most intuitive visual signature of image, and color moment is that one kind of descriptive statistics field color distribution situation is succinct
Effective method.Have that calculating is simple, construction color feature vector dimension is small, table using the color characteristic of color moment expression piecemeal
Up to clear succinct advantage.In the present invention, the color feature vector of piecemeal rectangle template includes nine components, may be expressed as: fi
=[μR,σR,SR,μG,σG,SG,μB,σB,SB], it is assumed that characteristic component meets Gaussian Profile, normalization color characteristic component to [- 1,
1] section.
S4.5 constructs crossing index similarity.
This patent counts crossing index similarity (Intersection Similarity using piecemeal rectangle rotating model
Index, ISI), by ISI, locally detection and identification crossing, ISI are defined as follows:
In formulaIndicate the normalization average value angle factor of the i-th piecemeal rectangle template frontier district,Indicate the i-th piecemeal square
Shape form boundary area and the normalized color moment Euclidean distance in center,Indicate the i-th piecemeal rectangle template frontier district and center
Normalized Moran ' the I Mean value of index in area is poor, w1、w2And w3The respectively weight of above three component.
S5, piecemeal rectangle rotating model identify crossing, and the road width for being generally proximal to crossing can gradually broaden, and crossing is presented
The shape feature of similar round.Meanwhile existing at crossing compared with stable image feature, specific manifestation are as follows: (1) crossing center and therewith
Connected road has similar spectrum, textural characteristics；(2) the section internal edge connecting with crossing center is evenly distributed,
Edge has very strong directionality, and the direction that the direction at edge and road extend is almost the same.In view of above-mentioned image road
Mouthful important feature, the invention proposes a kind of novel piecemeal rectangle rotating model identification crossing specifically includes the following steps:
S5.1 counts ISI using piecemeal rectangle rotating model；Using candidate crossing coordinate as the center of circle, with fixed angle interval
It rotates piecemeal rectangle template and counts ISI, using angle as abscissa, ISI is ordinate, forms ISI figure.
S5.2, during locally identification, some crossings will appear multiple place's approximation ISI point situations, can simply merge
Similitude in ISI figure.ISI figure after merging similitude eliminates partial redundance point and retains important local maximum or minimum letter
Breath, Wave crest and wave trough are more obvious.
S5.3, counts the trough point quantity of ISI figure, and angle corresponding to trough indicates the side of the connected road component in crossing
To the trough number n of extraction is to judge crossing important indicator.
When trough number n=>5 or n<2 judge that candidate point is not crossing.
When trough number n belongs to [2,4], also need through the Distance Judgment between analysis trough, criterion is as follows:
1) work as n=2, corresponding two angles of trough, respectively θiAnd θj, and θi<θj.(θ if it existsj–θi) belong to [π/3,
5 π/6] or [7 π/6,5 π/3], then determine that the candidate point for road turn in the road, can mark when necessary in road-map；
2) work as n=3, corresponding 3 trough angle, θsi, θj, θkThere are a pair of of angular distances to belong to [17 π/18,19 π/18],
Then it is determined as T-shape crossing；Otherwise, it is determined that being Y-shaped crossing；
3) work as n=4, be determined as " four troubles " crossing, corresponding four angles of trough, respectively θi、θj、θmAnd θn, and θi<θj
<θm<θn.In the presence of (θm-θi) belong to [π/18 17 π/18,19] and (θn-θj) belong to [17 π/18,19 π/18], then it is determined as " ten "
Shape crossing.
Further, step S1 the following steps are included:
S1.1, road primitives coarse extraction:
S1.1.1, feature extraction, using NSCT (Non-Subsampled Contourlet Transform, no down-sampling
Little profile transformation) image is successively decomposed, texture is constructed using characteristic parameters such as the mean value of low frequency sub-band, standard deviation, homogeneity degree
Feature vector F1 and band logical subband gradient energy, sub-band coefficients variance construct texture feature vector F2, and utilize color histogram
Figure calculates the characteristic parameters such as standard deviation, mean value, gradient, kurtosis value and energy and constructs color feature vector F3.
The study of S1.1.2, multicore SVM (Support Vector Machine, support vector machines) classifier, selects Gauss
Core Radial basis kernel function, the road sample and about 10% non-rice habitats sample for taking in image about 8% respectively carry out classification learning, obtain
Two discriminant classification function of multicore SVM.
S1.1.3, road primitives coarse extraction, image after scanning whole picture pretreatment as center windowing using pixel utilize
The discriminant classification function that S1.1.2 is obtained judges center pel, is divided into two class of road and non-rice habitats, realizes road primitives coarse extraction.
S1.2, Road Base primordial essence are extracted:
S1.2.1, road in the real world are the reticular structure being formed by connecting by different road primitives, usual roadway area
The area in domain will not be too small, and small patches can be considered as to non-rice habitats noise removal, in conjunction with the long and narrow property and compactness of road shape, leads to
It crosses threshold value and filters out non-long and narrow block.
S1.2.2, using the form of binary morphology operation repairing road primitive.
The invention adopts the above technical scheme.The present invention is not necessarily to manual intervention, can automatically extract crossing, extraction it is accurate
Property and integrality are superior to existing similar research achievement, and main advantage is as follows: (1) present invention can obtain candidate crossing automatically
Point initializes road-center seed point without manual intervention mode.(2) the effective part for remaining crossing of the present invention is thin
Information is saved, erroneous detection or the missing inspection of road primitives are avoided.(3) present invention takes full advantage of the spectral information at image crossing, edge letter
Breath and texture information.(4) present invention proposes that piecemeal rectangle rotating model makes full use of the stable image feature at crossing: (a) leading to
It can gradually broaden very close to the road width at crossing, the shape feature of similar round is presented in crossing；(b) crossing center and therewith phase
Road even has similar spectrum, textural characteristics；(c) the section internal edge connecting with crossing center is evenly distributed, side
Edge has very strong directionality, and the direction that the direction at edge and road extend is almost the same.
Detailed description of the invention
The present invention is described in further details below in conjunction with the drawings and specific embodiments:
Fig. 1 is a kind of flow diagram of high-resolution remote sensing image crossing of the invention automatic identifying method；
Fig. 2 is the piecemeal rectangle template schematic diagram of step S4.1 of the invention；
Fig. 3 is the edge statistics window W schematic diagram of step S4.2.2 of the invention；
Fig. 4 is the piecemeal rectangle rotating model structural schematic diagram of step S5.1 of the invention.
Specific embodiment
Shown in one of picture 1-4, the invention discloses a kind of high-resolution remote sensing image crossing automatic identifying method, packets
Include following steps:
S1, road primitives extract: first to remote sensing image carry out road class and non-rice habitats class coarse extraction, then to road class into
Row non-rice habitats noise removal, finally using the form of binary morphology operation repairing road primitive.
Specifically, comprising the following steps:
S1.1, road primitives coarse extraction, specific steps are as follows:
Feature extraction: S1.1.1 uses NSCT (Non-Subsampled Contourlet Transform, no down-sampling
Little profile transformation) image is successively decomposed, texture is constructed using characteristic parameters such as the mean value of low frequency sub-band, standard deviation, homogeneity degree
Feature vector F1 and band logical subband gradient energy, sub-band coefficients variance construct texture feature vector F2, and utilize color histogram
Figure calculates the characteristic parameters such as standard deviation, mean value, gradient, kurtosis value and energy and constructs color feature vector F3.
The study of S1.1.2, multicore SVM (Support Vector Machine, support vector machines) classifier, selects Gauss
Core Radial basis kernel function, the road sample and about 10% non-rice habitats sample for taking in image about 8% respectively carry out classification learning, obtain
Two discriminant classification function of multicore SVM.
S1.1.3, road primitives coarse extraction, image after scanning whole picture pretreatment as center windowing using pixel utilize
The discriminant classification function that S1.1.2 is obtained judges center pel, is divided into two class of road and non-rice habitats, realizes road primitives coarse extraction.
S1.2, Road Base primordial essence are extracted, specific steps are as follows:
S1.2.1, road in the real world are the reticular structure being formed by connecting by different road primitives, usual roadway area
The area in domain will not be too small, and small patches can be considered as to non-rice habitats noise removal；In conjunction with the long and narrow property and compactness of road shape, lead to
It crosses threshold value and filters out non-long and narrow block.
S1.2.2, using the form of binary morphology operation repairing road primitive.
S2 is connected to road skeleton: connecting neighbouring road primitives, forms complete road skeleton.
S3 generates candidate crossing coordinate: carrying out Refinement operation to the road skeleton of extraction, is figured using Rutoviz intersection
Method obtains road skeleton crosspoint, and as candidate crossing coordinate；Intersect point methods by above-mentioned global detection skeleton, can be obtained
Candidate crossing center point coordinate.But the local nonlinearity deformation as caused by thinning algorithm generates the non-crossing crosspoint in part,
In the case where guaranteeing recall ratio, the crosspoint at increased non-crossing need to be filtered out by part detection.
S4 constructs crossing index similarity:
S4.1 constructs piecemeal rectangle template, devises a kind of novel piecemeal rectangle rotating model identification crossing, the model
Using piecemeal rectangle as template；As shown in Fig. 2, piecemeal rectangle template is divided into center and the part of frontier district two, center statistics
The information at crossing center, frontier district count the road section information being connected with crossing center；The length L of piecemeal rectangle template center1
It is set as the mean breadth of road, boundary section length L2It is set as K*L1, K is threshold value constant.
S4.2 calculates the mean value angle of piecemeal rectangle template.
S4.2.1 carries out coherent filtering to image to be processed, enhances image road linear edge, and coherent filtering is a kind of
It is the effective ways for improving the diffusion of image border Anisotropic Nonlinear tensor.Linear character is that the important feature of road is also
The important evidence of other atural objects is distinguished on road, to crossing image implement coherent filtering not only can smooth image, but also can enhance
The contrast at edge and the directional information of road.
S4.2.2 calculates mean value angle: using coherence enhancing diffusion filtering enhancing image edge, counting each piecemeal
The angular distribution situation at each of rectangle template frontier district edge；As shown in figure 3, in the window W of M*N, the side that has M angle different
Edge, each edge are denoted as respectively with X-axis angle: θ1,θ2,...,θM, wherein θi∈[0,π)；Current piecemeal rectangle template i rotation
The angle of angle and X-axis is Ai, wherein the value range of i is [1,36]；Current piecemeal rectangle template i angle mean value SIDefinition is such as
Under:
Wherein τ is threshold value constant, AiIt calculates as follows:
S4.3, spatial autocorrelation express homogeney: using Moran ' the I Mean value of index of rectangle Statistical Area as local grain spy
Sign, specific calculating are as follows:
Wherein I (i, j) indicates Moran ' the I index value of pixel (i, j), and Ω is statistical regions, and N is picture in statistical regions
The sum of element.
Specifically, texture is the important feature of remote sensing image, the areal usually obtained from dissimilar sensor
The resolution ratio and texture characteristics of image are not quite similar, and the homogeneous feature of road is relatively stable in image.Space is from phase
The homogeneous feature (one of important feature of road) of image texture can rationally be characterized by closing index M oran ' I.Compared to other types
Textural characteristics (such as variance, energy, entropy, contrast, the degree of correlation), Moran ' the I index of characterization homogeneity characteristic calculate it is simple,
Time complexity is low, is influenced by sensor type smaller.
S4.4, color is away from processing: color is the most intuitive visual signature of image, and color moment is descriptive statistics field color point
A kind of succinct effective method of cloth situation.Have using the color characteristic of color moment expression piecemeal and calculates simple, construction color
Feature vector dimension is small, the clear succinct advantage of expression.In the present invention, the color feature vector of piecemeal rectangle template includes nine
Component may be expressed as: fi=[μR,σR,SR,μG,σG,SG,μB,σB,SB], it is assumed that characteristic component meets Gaussian Profile, normalizes face
Color characteristic component is to [- 1,1] section.
S4.5 constructs crossing index similarity: counting crossing index similarity using piecemeal rectangle rotating model
(Intersection Similarity Index, ISI) is locally detected and is identified crossing, crossing index similarity by ISI
The calculation formula of ISI is as follows:
In formulaIndicate the normalization average value angle factor of the i-th piecemeal rectangle template frontier district,Indicate the i-th piecemeal square
Shape form boundary area and the normalized color moment Euclidean distance in center,Indicate the i-th piecemeal rectangle template frontier district and center
Normalized Moran ' the I Mean value of index in area is poor, w1、w2And w3The respectively weight of above three component.
S5 identifies crossing based on piecemeal rectangle rotating model, and the road width for being generally proximal to crossing can gradually broaden, crossing
The shape feature of similar round is presented.Meanwhile at crossing exist compared with stable image feature, specific manifestation are as follows: (1) crossing center and
The road being attached thereto has similar spectrum, textural characteristics；(2) the section internal edge distribution connecting with crossing center is equal
Even, edge has very strong directionality, and the direction that the direction at edge and road extend is almost the same.In view of above-mentioned image
The important feature at crossing, S5 specifically includes the following steps:
S5.1, as shown in figure 4, counting ISI using piecemeal rectangle rotating model；Using candidate crossing coordinate as the center of circle, with solid
Determine angle interval rotation piecemeal rectangle template statistics ISI, and using angle as abscissa, ISI is ordinate, forms ISI figure.
S5.2, during locally identification, during locally identification, it is approximate that some crossings will appear multiple places
ISI point situation, can simply merge similitude in ISI figure, and eliminates the partial redundance point of the ISI figure after merging similitude and protect
Stay important local maximum or the prominent Wave crest and wave trough of minimum information.
S5.3, counts the trough point quantity of ISI figure, and angle corresponding to trough indicates the side of the connected road component in crossing
To, and the trough number n by extracting judges crossing:
When trough number n=>5 or n<2, then judge that candidate point is not crossing.
When trough number n belongs to [2,4], classification judgement is carried out by the distance between analysis trough:
1) work as n=2, corresponding two angles of trough, respectively θiAnd θj, and θi<θj；When in the presence of (θj–θi) belong to [π/3,
5 π/6] or when belonging to [7 π/6,5 π/3], then determine the candidate point for road turn in the road；
2) work as n=3, corresponding 3 trough angle, θsi、θj、θk；When there are any pair of angular distances to belong to [17 π/18,19
π/18] when, then it is determined as T-shape crossing；Otherwise, it is determined that being Y-shaped crossing；
3) work as n=4, corresponding four angles of trough, respectively θi、θj、θmAnd θn, and θi<θj<θm<θn；When in the presence of (θm-
θi) belong to [π/18 17 π/18,19] and (θn-θj) when belonging to [17 π/18,19 π/18], then it is determined as " ten " shape crossing.
The invention adopts the above technical scheme.The present invention is not necessarily to manual intervention, can automatically extract crossing, extraction it is accurate
Property and integrality are superior to existing similar research achievement, and main advantage is as follows: (1) present invention can obtain candidate crossing automatically
Point initializes road-center seed point without manual intervention mode.(2) the effective part for remaining crossing of the present invention is thin
Information is saved, erroneous detection or the missing inspection of road primitives are avoided.(3) present invention takes full advantage of the spectral information at image crossing, edge letter
Breath and texture information.(4) present invention proposes that piecemeal rectangle rotating model makes full use of the stable image feature at crossing: (a) leading to
It can gradually broaden very close to the road width at crossing, the shape feature of similar round is presented in crossing；(b) crossing center and therewith phase
Road even has similar spectrum, textural characteristics；(c) the section internal edge connecting with crossing center is evenly distributed, side
Edge has very strong directionality, and the direction that the direction at edge and road extend is almost the same.
Claims (5)
1. a kind of high-resolution remote sensing image crossing automatic identifying method, it is characterised in that: itself the following steps are included:
S1, road primitives extract: first carrying out the coarse extraction of road class and non-rice habitats class to remote sensing image, then carry out to road class non-
Road noise removes, finally using the form of binary morphology operation repairing road primitive；
S2 is connected to road skeleton: connecting neighbouring road primitives, forms complete road skeleton；
S3 generates candidate crossing coordinate: carrying out Refinement operation to the road skeleton of extraction, is obtained using Rutoviz crossing number algorithm
By way of road skeleton crosspoint, and as candidate crossing coordinate；
S4 constructs crossing index similarity:
S4.1, constructs piecemeal rectangle template, and piecemeal rectangle template is divided into center and the part of frontier district two, center statistics road
The information at mouth center, frontier district count the road section information being connected with crossing center；
S4.2 calculates the mean value angle of piecemeal rectangle template；
S4.2.1 carries out coherent filtering to image to be processed, enhances image road linear edge；
S4.2.2 calculates mean value angle: using coherence enhancing diffusion filtering enhancing image edge, counting each piecemeal rectangle
The angular distribution situation at each of form boundary area edge；In the window W of M*N, the edge for having M angle different, each edge and X
Axle clamp angle is denoted as respectively: θ1,θ2,...,θM, wherein θi∈[0,π)；The angle of current piecemeal rectangle template i rotation angle and X-axis
For Ai, wherein the value range of i is [1,36]；Current piecemeal rectangle template i angle mean value SIIt is defined as follows:
Wherein τ is threshold value constant, AiIt calculates as follows:
S4.3, spatial autocorrelation express homogeney: using Moran ' the I Mean value of index of rectangle Statistical Area as Local textural feature,
Specific calculating is as follows:
Wherein I (i, j) indicates Moran ' the I index value of pixel (i, j), and Ω is statistical regions, and N is pixel in statistical regions
Sum；
S4.4, color is away from processing: the color feature vector of piecemeal rectangle template is normalized to [- 1,1] section；
S4.5 constructs crossing index similarity, and the calculation formula of crossing index similarity ISI is as follows:
In formulaIndicate the normalization average value angle factor of the i-th piecemeal rectangle template frontier district,Indicate the i-th piecemeal rectangular mold
Edges of boards battery limit (BL) and the normalized color moment Euclidean distance in center,Indicate that the i-th piecemeal rectangle template frontier district is returned with center
Moran ' the I Mean value of index of one change is poor, w1、w2And w3The respectively weight of above three component；
S5 identifies crossing based on piecemeal rectangle rotating model, specifically includes the following steps:
S5.1 counts ISI using piecemeal rectangle rotating model；Using candidate crossing coordinate as the center of circle, with the rotation of fixed angle interval
Piecemeal rectangle template counts ISI, and using angle as abscissa, ISI is ordinate, forms ISI figure；
S5.2 is simple to merge similitude in ISI figure and eliminate partial redundance point and retain important during locally identification
Local maximum or the prominent Wave crest and wave trough of minimum information；
S5.3 counts the trough point quantity of ISI figure, and angle corresponding to trough indicates the direction of the connected road component in crossing, and
Crossing is judged by the trough number n of extraction:
When trough number n=>5 or n<2, then judge that candidate point is not crossing；
When trough number n belongs to [2,4], classification judgement is carried out by the distance between analysis trough:
1) work as n=2, corresponding two angles of trough, respectively θiAnd θj, and θi<θj；When in the presence of (θj–θi) belong to [π of π/3,5/
When 6] or belonging to [7 π/6,5 π/3], then determine the candidate point for road turn in the road；
2) work as n=3, corresponding 3 trough angle, θsi、θj、θk；When belonging to [π/18 17 π/18,19] there are any pair of angular distance
When, then it is determined as T-shape crossing；Otherwise, it is determined that being Y-shaped crossing；
3) work as n=4, corresponding four angles of trough, respectively θi、θj、θmAnd θn, and θi<θj<θm<θn；When in the presence of (θm-θi) belong to
In [π/18 17 π/18,19] and (θn-θj) when belonging to [17 π/18,19 π/18], then it is determined as " ten " shape crossing.
2. a kind of high-resolution remote sensing image crossing automatic identifying method according to claim 1, it is characterised in that: step
S1 road primitives coarse extraction specific steps are as follows:
Feature extraction: S1.1.1 successively decomposes image using NSCT, utilizes the mean value of low frequency sub-band, standard deviation, homogeneity degree etc.
Characteristic parameter constructs texture feature vector F1 and band logical subband gradient energy, sub-band coefficients variance construct texture feature vector
F2, and the characteristic parameters such as standard deviation, mean value, gradient, kurtosis value and energy building color spy is calculated using color histogram
Levy vector F3；
S1.1.2, multicore SVM classifier study, selects Gaussian kernel Radial basis kernel function, takes in image about 8% road sample respectively
This and about 10% non-rice habitats sample carry out classification learning, obtain two discriminant classification function of multicore SVM；
S1.1.3, road primitives coarse extraction, image after scanning whole picture pretreatment as center windowing using pixel are obtained using S1.1.2
The discriminant classification function arrived judges center pel, is divided into two class of road and non-rice habitats, realizes road primitives coarse extraction.
3. a kind of high-resolution remote sensing image crossing automatic identifying method according to claim 1, it is characterised in that: step
S1 Road Base primordial essence extracts specific steps are as follows:
S1.2.1 filters out non-rice habitats noise by threshold value in conjunction with path area feature, the long and narrow property of shape and compactness；
S1.2.2, using the form of binary morphology operation repairing road primitive.
4. a kind of high-resolution remote sensing image crossing automatic identifying method according to claim 1, it is characterised in that: step
The length L of piecemeal rectangle template center in S4.11It is set as the mean breadth of road, boundary section length L2It is set as K*L1, K
For threshold value constant.
5. a kind of high-resolution remote sensing image crossing automatic identifying method according to claim 1, it is characterised in that: step
The color feature vector of piecemeal rectangle template indicates in S4.4 are as follows: fi=[μR,σR,SR,μG,σG,SG,μB,σB,SB]。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811506319.6A CN109635722B (en) | 2018-12-10 | 2018-12-10 | A method for automatic identification of intersections in high-resolution remote sensing images |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811506319.6A CN109635722B (en) | 2018-12-10 | 2018-12-10 | A method for automatic identification of intersections in high-resolution remote sensing images |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109635722A true CN109635722A (en) | 2019-04-16 |
| CN109635722B CN109635722B (en) | 2022-10-25 |
Family
ID=66072736
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811506319.6A Active CN109635722B (en) | 2018-12-10 | 2018-12-10 | A method for automatic identification of intersections in high-resolution remote sensing images |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109635722B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110021178A (en) * | 2019-04-18 | 2019-07-16 | 浙江工业大学 | A kind of signal time distributing conception recommended method based on crossing similarity |
| CN110443816A (en) * | 2019-08-08 | 2019-11-12 | 重庆市地理信息和遥感应用中心(重庆市测绘产品质量检验测试中心) | Urban road extracting method on remote sensing image based on intersection detection |
| CN111738094A (en) * | 2020-05-28 | 2020-10-02 | 中国国土勘测规划院 | Automatic remote sensing image road extraction method and device |
| CN113340824A (en) * | 2021-06-11 | 2021-09-03 | 湖南农业大学 | Detection method for hyperspectral information redundant wave band |
| CN114612797A (en) * | 2022-03-10 | 2022-06-10 | 航天宏图信息技术股份有限公司 | A method and device for road connectivity enhancement based on remote sensing images |
| CN116434173A (en) * | 2023-04-12 | 2023-07-14 | 腾讯科技（深圳）有限公司 | Road image detection method, device, electronic equipment and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007328573A (en) * | 2006-06-08 | 2007-12-20 | Nissan Motor Co Ltd | Intersection condition detection apparatus and intersection condition detection method |
| CN104240260A (en) * | 2014-10-09 | 2014-12-24 | 武汉大学 | Junction identification based intelligent road extraction method |
| CN106778605A (en) * | 2016-12-14 | 2017-05-31 | 武汉大学 | Remote sensing image road net extraction method under navigation data auxiliary |
| CN107958183A (en) * | 2017-12-02 | 2018-04-24 | 中国地质大学（北京） | A kind of city road network information automation extraction method of high-resolution remote sensing image |
-
2018
- 2018-12-10 CN CN201811506319.6A patent/CN109635722B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007328573A (en) * | 2006-06-08 | 2007-12-20 | Nissan Motor Co Ltd | Intersection condition detection apparatus and intersection condition detection method |
| CN104240260A (en) * | 2014-10-09 | 2014-12-24 | 武汉大学 | Junction identification based intelligent road extraction method |
| CN106778605A (en) * | 2016-12-14 | 2017-05-31 | 武汉大学 | Remote sensing image road net extraction method under navigation data auxiliary |
| CN107958183A (en) * | 2017-12-02 | 2018-04-24 | 中国地质大学（北京） | A kind of city road network information automation extraction method of high-resolution remote sensing image |
Non-Patent Citations (6)
| Title |
|---|
| 程江华 等: "高分辨率SAR图像道路交叉口检测与识别新方法", 《雷达学报》 * |
| 蔡红 等: "高分辨率遥感图像道路交叉口自动提取", 《国土资源遥感》 * |
| 许锐: "高分辨率遥感影像道路目标识别与提取研究", 《中国博士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 陈建华 等: "基于边缘的自适应相关跟踪算法", 《电视技术》 * |
| 陈昌黎: "基于角度纹理特征和岔路口探测模型自动提取遥感影像道路网", 《测绘与空间地理信息》 * |
| 高庆吉 等: "机器人视觉定位中的路口场景识别方法研究", 《中国图象图形学报》 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110021178A (en) * | 2019-04-18 | 2019-07-16 | 浙江工业大学 | A kind of signal time distributing conception recommended method based on crossing similarity |
| CN110443816A (en) * | 2019-08-08 | 2019-11-12 | 重庆市地理信息和遥感应用中心(重庆市测绘产品质量检验测试中心) | Urban road extracting method on remote sensing image based on intersection detection |
| CN111738094A (en) * | 2020-05-28 | 2020-10-02 | 中国国土勘测规划院 | Automatic remote sensing image road extraction method and device |
| CN111738094B (en) * | 2020-05-28 | 2023-08-25 | 中国国土勘测规划院 | Automatic extraction method and device for remote sensing image road |
| CN113340824A (en) * | 2021-06-11 | 2021-09-03 | 湖南农业大学 | Detection method for hyperspectral information redundant wave band |
| CN113340824B (en) * | 2021-06-11 | 2022-04-22 | 湖南农业大学 | Detection method for hyperspectral information redundant wave band |
| CN114612797A (en) * | 2022-03-10 | 2022-06-10 | 航天宏图信息技术股份有限公司 | A method and device for road connectivity enhancement based on remote sensing images |
| CN116434173A (en) * | 2023-04-12 | 2023-07-14 | 腾讯科技（深圳）有限公司 | Road image detection method, device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109635722B (en) | 2022-10-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109635722A (en) | A kind of high-resolution remote sensing image crossing automatic identifying method | |
| CN102999753B (en) | License plate locating method | |
| CN103578110B (en) | Multiband high-resolution remote sensing image dividing method based on gray level co-occurrence matrixes | |
| CN109815807B (en) | A detection method for berthing ships based on edge line analysis and aggregated channel features | |
| CN103473571B (en) | Human detection method | |
| CN103268470B (en) | Object video real-time statistical method based on any scene | |
| CN109635733B (en) | Parking lot and vehicle target detection method based on visual saliency and queue correction | |
| CN101551863A (en) | Method for extracting roads from remote sensing image based on non-sub-sampled contourlet transform | |
| CN103295232B (en) | Based on the SAR image registration method in straight line and region | |
| CN103927758B (en) | Saliency detection method based on contrast ratio and minimum convex hull of angular point | |
| CN108564814A (en) | A kind of parking position detection method and device based on image | |
| CN102663357A (en) | Color characteristic-based detection algorithm for stall at parking lot | |
| CN106778633B (en) | Pedestrian identification method based on region segmentation | |
| CN102073873B (en) | Method for selecting SAR (spaceborne synthetic aperture radar) scene matching area on basis of SVM (support vector machine) | |
| CN104063882B (en) | Vehicle video speed measuring method based on binocular camera | |
| CN105205489A (en) | License plate detection method based on color texture analyzer and machine learning | |
| CN104199823B (en) | A dynamic detection method for fabric defects driven by visual data | |
| CN106778742B (en) | Car logo detection method based on Gabor filter background texture suppression | |
| CN110321855A (en) | A kind of greasy weather detection prior-warning device | |
| CN105894513B (en) | Take the remote sensing image variation detection method and system of imaged object change in time and space into account | |
| CN103679196A (en) | Method for automatically classifying people and vehicles in video surveillance | |
| CN107862262A (en) | A kind of quick visible images Ship Detection suitable for high altitude surveillance | |
| CN107992856A (en) | High score remote sensing building effects detection method under City scenarios | |
| CN104268509B (en) | The method and system of dump truck car plate detection | |
| CN109410233A (en) | A kind of accurate extracting method of high-definition picture road of edge feature constraint |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |