CN104239885B - A kind of earthquake disaster damage degree appraisal procedure based on unmanned plane - Google Patents
A kind of earthquake disaster damage degree appraisal procedure based on unmanned plane Download PDFInfo
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Abstract
A kind of earthquake disaster damage degree appraisal procedure based on unmanned plane, hovered using unmanned plane in earthquake disaster area overhead and IMAQ, house is directed to respectively, road, bridge, dykes and dams, massif, river course and the quasi-representative atural object of vegetation seven, the circle that disaster area interesting image regions are carried out according to man-machine interactively formula method selects, and selectively calculate the gray level co-occurrence matrixes textural characteristics of selected areas, Tamura textural characteristics, Gabor wavelet textural characteristics and straight line and circle feature, the final assessment that earthquake disaster atural object damage degree is carried out using support vector machine classifier.This method is more suitable for carrying out information and the assessment of early stage in earthquake and the secondary disaster generation area as caused by earthquake.
Description
Technical field
The present invention relates to a kind of earthquake disaster damage degree appraisal procedure based on unmanned plane.
Background technology
China's area is vast in territory, and mountain range rises and falls, and in length and breadth, geological conditions is complex in river.Due to climatic effect,
The reasons such as geological conditions difference, many areas are easily encroached on by earthquake and its secondary disaster throughout the year.As the Qinghai in China, Yunnan,
The area such as western Sichuan, SOUTH OF GANSU is located in the place that Alps-Himalaya earthquake zone is passed through, and repeatedly occurs in recent years
More than 4.5 grades of earthquake is crossed, causes larger casualties and economic loss.Under normal circumstances, when earthquake disaster occurs, most
Conventional mode is the assessment of earthquake magnitude and degree of splitting index progress disaster loss grade that different earthquake sampled point is collected using seismic detector, this
The advantages of kind method is that measurement result is authoritative, versatile；But shortcoming is that measurement result is more abstract, fuzzy or even inaccurate,
The degree that is damaged in a clear specific disaster area can not be described.If the definition of traditional earthquake destructiveness is according to the big of earthquake magnitude
It is small be divided into weak shock (earthquake magnitude be less than 3 grades), felt earthquake (earthquake magnitude is more than or equal to 3 grades, less than 4.5 grades), (earthquake magnitude is more than middle macroseism
4.5 grades, less than 6 grades), macroseism (earthquake magnitude be more than 6 grades), Giant Bullous (earthquake magnitude is more than 8 grades) etc.；This kind of evaluation index is carrying out ground
More obscured for the information that the rescue personnel of non-geology specialty provides when shaking disaster assistance, can not judge that earthquake is built to earth's surface
Build and structure caused by damaged condition, and due to seismic detector sampled point limited amount, have necessarily away from different earthquake disaster area
Distance, geology characteristic, the construction quality of each department differ, and is actually destroyed caused by earthquake and to injure situation also incomplete
It is corresponding with These parameters, therefore be difficult often the one specific real disaster-stricken situation information in earthquake areas of reflection, and then also very
Hardly possible judges the emergency degree of seismic disaster relief.
With the development of scientific and technological level, in Disaster Assessment technological means, disaster feelings are carried out using remote sensing or Aerial Images
The information of condition has the characteristics of data are directly perceived, credible with assessing, in recent years domestic all kinds of disaster alarms and rescue task
It is widely used.Chinese patent " remote sensing earthquake damage information extraction and method for digging based on pixel " (domestic patent publication No.
CN101788685A a kind of method using remote Sensing Image Analysis) is proposed, is made a variation by spectrum, principal component fusion is found automatically
Region of variation, then region of variation is split using dual threshold algorithm of region growing, the side explained finally by artificial visual
Formula extracts Earthquake damage information；Chinese patent " a kind of disaster situation obtaining system " (domestic patent publication No. CN201672918U) proposes to use
Unmanned plane carry video camera, digital camera, the method for forward looking infrared image collecting device, gather disaster-stricken image, to aid in calamity
Evil rescue and post-disaster reconstruction；A kind of Chinese patent " disaster monitoring electronics delineation system based on unmanned plane " (domestic patent
Publication number CN102419171A) propose by the way of the information that a kind of ground delineation system is passed back to unmanned plane uses writing pencil
It is labeled.However, it is not difficult to find out by the analysis to above-mentioned patent：The problem of disaster loss grade assessment being carried out using remotely-sensed data
Be remotely-sensed data acquisition and editor analytical cycle is longer, cost is higher, the issue and acquisition of data can only be by specific
Channel and authoritative department provide；And the acquisition of high-altitude Aerial Images needs to perform flight times using medium-sized or large-scale aircraft
Business, is not easy to the carrying and use of rescue group.
The content of the invention
The technical problems to be solved by the invention are to propose a kind of low earthquake disaster damage based on unmanned plane of cost
Degree of ruining appraisal procedure, the quick collection to earthquake-stricken area disaster-stricken situation can be realized with assessing.
The present invention includes following technical scheme：
1st, a kind of earthquake disaster damage degree appraisal procedure based on unmanned plane, it is characterised in that realize that step is as follows：
(1) typical earthquake disaster image feature data collection is established, the typical earthquake disaster image feature data collection includes
House, road, bridge, dykes and dams, massif, river course and the earthquake disaster of vegetation image feature data collection；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, determined by way of user's hand labeled
Go out the image-region of the image-region acquisition more than 200 houses in house, the image-region in each house to determining calculates
The characteristics of image in house, and damage degree in house corresponding to the image-region in each house is provided, so as to obtain the earthquake disaster in house
Evil image feature data collection；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, determined by way of user's hand labeled
Go out image-region of the image-region acquisition more than 200 roads of road, the image-region of each road to determining calculates
The characteristics of image of road, and road damage degree corresponding to the image-region of each road is provided, so as to obtain the earthquake disaster of road
Evil image feature data collection；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, determined by way of user's hand labeled
Go out image-region of the image-region acquisition more than 200 bridges of bridge, the figure of bridge is calculated to the image-region of each bridge
As feature, and bridge damage degree corresponding to the image-region of each bridge is provided, so as to obtain the earthquake disaster image of bridge spy
Levy data set；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, determined by way of user's hand labeled
Go out image-region of the image-region acquisition more than 200 dykes and dams of dykes and dams, the figure of dykes and dams is calculated to the image-region of each dykes and dams
As feature, and dykes and dams damage degree corresponding to the image-region of each dykes and dams is provided, so as to obtain the earthquake disaster image of dykes and dams spy
Levy data set；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, determined by way of user's hand labeled
Go out image-region of the image-region acquisition more than 200 massifs of massif, the figure of massif is calculated to the image-region of each massif
As feature, and massif damage degree corresponding to the image-region of each massif is provided, so as to obtain the earthquake disaster image of massif spy
Levy data set；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, determined by way of user's hand labeled
Go out the image-region of the image-region acquisition more than 200 river courses in river course, the figure in river course is calculated to the image-region in each river course
As feature, and damage degree in river course corresponding to the image-region in each river course is provided, so as to obtain the earthquake disaster image in river course spy
Levy data set；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, determined by way of user's hand labeled
Go out image-region of the image-region acquisition more than 200 vegetation of vegetation, the figure of vegetation is calculated to the image-region of each vegetation
As feature, and vegetation damage degree corresponding to the image-region of each vegetation is provided, so as to obtain the earthquake disaster image of vegetation spy
Levy data set；
The characteristics of image in house includes gray level co-occurrence matrixes angular second moment index GLCMASM, gray level co-occurrence matrixes index of correlation
GLCMCOR, gray level co-occurrence matrixes entropy index GLCMENT, contrast index T in Tamura textural characteristicscon, Gabor direction characters
Index fgabor, utilize Hough transform carry out straight-line detection straight line number index HlLoop truss is carried out with using Hough transform
Circle number index Hc；
Road, bridge, the characteristics of image in dykes and dams and river course include gray level co-occurrence matrixes angular second moment index GLCMASM, ash
Spend co-occurrence matrix the moment of inertia index GLCMCON, gray level co-occurrence matrixes entropy index GLCMENT, gray level co-occurrence matrixes inverse difference moment index
GLCMIDM, contrast index T in Tamura textural characteristicscon, Gabor direction character indexs fgaborEnter with using Hough transform
The straight line number index H of row straight-line detectionl；
Massif and the characteristics of image of vegetation include gray level co-occurrence matrixes angular second moment index GLCMASM, gray level co-occurrence matrixes
The moment of inertia index GLCMCON, gray level co-occurrence matrixes inverse difference moment index GLCMIDM, contrast index T in Tamura textural characteristicscon
With Gabor direction character indexs fgabor；
(2) house, road, bridge, dykes and dams, massif, river course and the training of the earthquake disaster of vegetation image feature data are utilized
Practice respective support vector machine classifier；
(3) new earthquake disaster image is obtained based on unmanned plane；
(4) house, road, bridge are directed to respectively by way of user's hand labeled to the new earthquake disaster image of acquisition
Beam, dykes and dams, massif, river course, the quasi-representative atural object of vegetation 7 determine corresponding image-region, according to the image-region meter determined
Characteristics of image corresponding to every quasi-representative atural object is calculated, the last characteristics of image according to corresponding to per quasi-representative atural object uses corresponding support
The grader of vector machine carries out the assessment of all kinds of atural object damage degree.
The present invention has the beneficial effect that compared with prior art：
(1) present invention devises a kind of earthquake disaster damage degree appraisal procedure using unmanned plane shooting image, direct root
The damage degree that earthquake and its secondary disaster are carried out according to the destroyed degree of earth's surface culture and Images of Natural Scenery is assessed, and is had
The characteristics of accurate, directly perceived, is described to disaster area disaster-stricken situation.
(2) the damage degree appraisal procedure designed by the present invention, model is carried out using the image gathered in conventional earthquake disaster
The extraction and calculating of feature, accumulation of knowledge is realized with reusing, there is higher assessment reliability and the degree of accuracy.
(3) the disaster damage degree appraisal procedure designed by the present invention, view data is carried out using unmanned aerial vehicle platform near the ground
Collection, and the processing that software carries out view data is assessed using damage degree, with system is easy to use, flexible, cost is low, intelligence
The advantages of degree is high can be changed, be suitable for equipping recovery force on a large scale.
(4) present invention carry out atural object damage degree assessment when used gray level co-occurrence matrixes image texture characteristic,
Tamura textural characteristics, Gabor wavelet textural characteristics, straight line and circular image feature, close to the visual perception of human visual system,
Damage degree of the earthquake to atural object can be preferably described, there is the advantages of calculating explicit physical meaning that is simple, calculating etc..
Brief description of the drawings
Fig. 1 is the method calculation flow chart of the present invention；
Fig. 2 is UAS operating diagram of the present invention.
Embodiment
As shown in figure 1, the assessment system that the inventive method is based on includes multi-rotor unmanned aerial vehicle subsystem, unmanned plane during flying
Ground controls and output display subsystem composition, wherein：
Multi-rotor unmanned aerial vehicle subsystem is by multi-rotor unmanned aerial vehicle, two-freedom rotary motion camera, d GPS locating module, sky
Middle wireless data transceiving terminal composition；Installed in multi-rotor unmanned aerial vehicle two-freedom rotary motion camera, d GPS locating module and
On-air radio data transceiver terminal；Multi-rotor unmanned aerial vehicle be 4 rotors, 6 rotors or 8 rotors unmanned plane during flying device, more rotors without
Man-machine to use battery powered, the non-stop flight time is not less than 30 minutes, and flying height is not less than 15 meters；Two-freedom rotary motion
Camera can realize the rotation of -5 ° of pitching~185 °, 0 °~300 ° angular ranges of going off course；D GPS locating module is used to obtain nobody
The current spatial coordinated information of machine；On-air radio data transceiver terminal is used to receive the control of unmanned plane during flying ground and output display
The flight control instruction that subsystem is sent, and the image that the camera of multi-rotor unmanned aerial vehicle is shot sends back the unmanned plane on ground and flown
Row ground controls and output display subsystem；
Unmanned plane during flying ground controls and output display subsystem is by terrestrial wireless data transceiver terminal and portable computing
Machine forms；Terrestrial wireless data transceiver terminal be used for and multi-rotor unmanned aerial vehicle enter row data communication, control, monitor more rotors nobody
The normal flight of machine；Portable computer is used for the picture of output display multi-rotor unmanned aerial vehicle shooting, and portable computer utilizes
The grader of typical earthquake disaster image feature data set pair SVMs is trained, and according to the image of shooting using branch
The grader for holding vector machine interacts the estimation of formula earthquake damage degree, the work shape of simultaneous real-time monitoring multi-rotor unmanned aerial vehicle
State.
The present invention uses IEEE 802.11G wireless network transmissions agreement, and according to MPEG4 compressed format, will revolve more
The image of wing unmanned plane camera shooting is controlled to unmanned plane during flying ground and output display subsystem is returned.Two-freedom revolves
It is Visible Light Camera to turn moving camera.
As shown in Fig. 2 the earthquake disaster damage degree appraisal procedure based on unmanned plane of the present invention realizes step such as
Under：
(1) typical earthquake disaster image feature data collection is established, the typical earthquake disaster image feature data collection includes
House, road, bridge, dykes and dams, massif, river course and the earthquake disaster of vegetation image feature data collection：
To the multiple earthquake disaster images obtained based on unmanned plane in the past, determined by way of user's hand labeled
Go out the image-region of the image-region acquisition more than 200 houses in house, the image-region in each house to determining calculates
The characteristics of image in house, and damage degree in house corresponding to the image-region in each house is provided, so as to obtain the earthquake disaster in house
Evil image feature data collection；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, determined by way of user's hand labeled
Go out image-region of the image-region acquisition more than 200 roads of road, the image-region of each road to determining calculates
The characteristics of image of road, and road damage degree corresponding to the image-region of each road is provided, so as to obtain the earthquake disaster of road
Evil image feature data collection；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, determined by way of user's hand labeled
Go out image-region of the image-region acquisition more than 200 bridges of bridge, the figure of bridge is calculated to the image-region of each bridge
As feature, and bridge damage degree corresponding to the image-region of each bridge is provided, so as to obtain the earthquake disaster image of bridge spy
Levy data set；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, determined by way of user's hand labeled
Go out image-region of the image-region acquisition more than 200 dykes and dams of dykes and dams, the figure of dykes and dams is calculated to the image-region of each dykes and dams
As feature, and dykes and dams damage degree corresponding to the image-region of each dykes and dams is provided, so as to obtain the earthquake disaster image of dykes and dams spy
Levy data set；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, determined by way of user's hand labeled
Go out image-region of the image-region acquisition more than 200 massifs of massif, the figure of massif is calculated to the image-region of each massif
As feature, and massif damage degree corresponding to the image-region of each massif is provided, so as to obtain the earthquake disaster image of massif spy
Levy data set；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, determined by way of user's hand labeled
Go out the image-region of the image-region acquisition more than 200 river courses in river course, the figure in river course is calculated to the image-region in each river course
As feature, and damage degree in river course corresponding to the image-region in each river course is provided, so as to obtain the earthquake disaster image in river course spy
Levy data set；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, determined by way of user's hand labeled
Go out image-region of the image-region acquisition more than 200 vegetation of vegetation, the figure of vegetation is calculated to the image-region of each vegetation
As feature, and vegetation damage degree corresponding to the image-region of each vegetation is provided, so as to obtain the earthquake disaster image of vegetation spy
Levy data set.
It is as follows for above-mentioned 7 quasi-representative image category, the mode of user's hand labeled：
For road, bridge, dykes and dams and river course, corresponding image-region is determined by way of user's hand labeled
Method is as follows：The edge placement of road, bridge, dykes and dams and river course is marked by the way of straight line and Drawing of Curve, is then extracted
Image-region is as the image-region determined between straight line and/or curve；For house, by way of user's hand labeled
The method for determining the image-region in house is as follows：Draw a circle to approve out the position in house by hand using quadrangle, then extract quadrangle
Internal image-region is as the image-region determined；For massif and vegetation, determined by way of user's hand labeled
The method for going out corresponding image-region is as follows：Draw a circle to approve out the region of massif and vegetation by hand using polygon, then extract polygon
Image-region inside shape is as the image-region determined.
Each characteristics of image of the present invention is defined as follows：
I) gray level co-occurrence matrixes angular second moment GLCMASM, the index characterization image texture intensity profile uniformity coefficient and line
The metric of fineness is managed, its calculating formula is as shown in formula (1)：
Wherein, p (i, j) is the gray level co-occurrence matrixes of piece image, and coordinate (i, j) represents image location information.
Ii) gray level co-occurrence matrixes the moment of inertia GLCMCON, the index characterization definition of image texture and the depth of texture
Metric, its calculating formula is as shown in formula (2)：
Iii) gray level co-occurrence matrixes correlation GLCMCOR, the index characterization measurement of texture similar area directionality size
Value, its calculating formula is as shown in formula (3)：
Wherein,Respectively
WithAverage and mean square deviation, NgFor the maximum of single pixel gray level.
Iv) gray level co-occurrence matrixes entropy GLCMENT, the complexity or mixed and disorderly degree of index characterization gradation of image distribution, its
Calculating formula is as shown in formula (4)：
V) gray level co-occurrence matrixes inverse difference moment GLCMIDM, the uniformity coefficient of the index characterization image texture, its calculating formula such as formula
(5) shown in：
Vi) the contrast T in Tamura textural characteristicscon, the intensity of the index characterization variation of image grayscale, it is counted
Formula is as shown in formula (6) and (7)：
Wherein μ4The fourth central for being image patch away from, σ is the mean square deviation for the image-region determined, α4For intermediate variable.
When actually calculating, the rectangular window region of fixed size in the image-region determined, i.e. image patch are taken first, and according to
From top to bottom, order from left to right determines region using the rectangular window area coverage image, and carries out in image quadravalence
The heart is away from the calculating with mean square deviation.
Vii) Gabor direction characters fgabor：If g (x, y) represents Gabor functions, generating function is used as using g (x, y), is passed through
Yardstick expansion and rotation transformation are carried out to g (x, y), one group of self similarity wave filter g can be obtainedmn(x, y), Gabor wavelet conversion can be determined
Justice is formula (12), and corresponding textural characteristics can be calculated as formula (13) and (14).Gabor wavelet feature vector may be defined as formula
(15)。
gmn(x, y)=a-mg(x′,y′) (9)
X '=a-m(xcosθ+ysinθ) (10)
Y '=a-m(-xsinθ+ycosθ) (11)
μmn=∫ ∫ | Wmn(x,y)|dxdy (13)
Wherein, g (x, y) represents Two-Dimensional Gabor Wavelets basic function, and W is Gaussian function multiple modulation frequency, σx、σyFor variable x
With y mean square deviation；gmn(x, y) is self similarity wave filter, a-mIt is scale factor, a>1, x ', y ' are the calculating after being converted to x, y
As a result, m and n is integer, represents corresponding yardstick and direction, and m ∈ [0, M-1], M are all dimensions, n ∈ [0, K-1], θ
=n π K, K are all direction numbers, and subscript " * " represents conjugate complex number, x1、y1Expression Gabor wavelet integral transformation offset, I (x,
Y) image-region selected out, W are representedmnThe result that (x, y) converts for Gabor wavelet, μmn、σmnFor Gabor wavelet average with
Mean square deviation feature.
Viii) in the artificial structure of unmanned plane, be easier to occur the edge of building rule, as straight line,
The basic elements such as circle, therefore the straight line number H of straight-line detection can be realized using Hough transform technologylWith the circle of loop truss
Number HcFeature description calculate.With other lines compared with loop truss technology, the detection technique of Hough transform have amount of calculation it is small,
Calculate the characteristics of accuracy is high.
Above-mentioned calculated disaster characteristic is formed into typical earthquake disaster image feature data collection, and it is special according to earthquake disaster
The opinion of family, the grade destroyed according to disaster carry out the mark of disaster damage degree.A kind of typical earthquake disaster image feature data
Collect sample as shown in 1 table.Damage degree concept proposed by the invention, mainly from the angle of graphical analysis come describe house,
The destructiveness that road, bridge, massif etc. are subjected to when by earthquake or the influence of its secondary disaster.The index should be with being evaluated
Ground artificial building or natural scene construction quality or geologic structure it is relevant, therefore with than indexs such as earthquake magnitude, degree of splitting more
Specifically, accurately description effect more directly perceived.For example the place A and place B in somewhere simultaneously shake, what seismic detector measured
Earthquake magnitude is similarly 6.0 grades；But the geologic structure on A ground is more firm, and culture's mass is preferable, when 6.0 grades of earthquakes occur, ground
The damage that table is subjected to be not it is very serious, caused by casualties it is naturally smaller, the image result taken photo by plane is earth's surface culture
It is little with natural scene damage degree；And B be located in mountain region along the river, earth's surface culture is second-rate, when 6.0 grades of ground occur
There occurs serious earth's surface during shake to cave in, and culture all damages, and casualties is serious, the result taken photo by plane be culture with
Natural scene damage degree is huge.The definition of damage degree be according to earthquake to caused by atural object destructiveness according to seismological expert and
The artificial experience of rescue expert is defined.It is enumerated a kind of 5 grades of appraisal procedures of building construction damage degree in table 2, other 6
It is similar therewith that the damage degree of type atural object defines method.When the damage degree of reality is assessed, it is necessary to which seismological expert and rescue are special
Family observes substantial amounts of historical earthquake and taken photo by plane data, and according to the content defined in table 2, atural object damage degree is carried out according to subjective judgement
Differentiate and give a mark.Therefore, by the definition of image damage degree, the different Disaster degree in description two places that can be vivid and quantitative,
Guidance is provided for follow-up rescue work.
The earthquake disaster damage degree of table 1 assesses image feature data collection sample
The house damage degree of table 2 defines method
| Sequence number | Damage situation describes | Grade |
| 1 | Crackle occurs for surface structures, but does not cave in | One-level |
| 2 | Severe crack occurs for surface structures, but does not cave in | Two level |
| 3 | Surface structures occurs part and caved in, but major structural damage does not occur | Three-level |
| 4 | Surface structures caves in, but structure is generation gross distortion | Level Four |
| 5 | Surface structures caves in completely, and comminuted destruction occurs for building structure | Pyatyi |
When the Earthquake Secondary calamity that landslide secondary disaster, mud-rock flow secondary disaster, surface collapse, sedimentation and cracking occurs
During evil, calculation and the training data and monitoring data that are obtained during above-mentioned generation earthquake disaster are completely the same；And due to all kinds of
Disaster destructiveness to caused by ground is different, the span difference, therefore pass through above-mentioned side of corresponding each feature
Method can realize the differentiation to all kinds of disasters.
(2) typical earthquake disaster image feature data collection training grader is used.
According to the data set accumulated in above-mentioned steps (1), the training of SVM classifier is carried out.SVM classifier is based on system
The mathematical tool of the theories of learning is counted, compared with other grader instruments, SVM is more suitable for solving small sample, non-linear, higher-dimension
Degree, the data of local minimum problem, the present invention determine that SVM classifier is more adapted to solve herein by attempting various graders
Disaster damage degree classification problem.Need to train 7 graders, respectively house, road, bridge altogether in the implementation procedure of training
Beam, dykes and dams, massif, river course, the image analysis result of vegetation damage situation, the indicator combination mode of graphical analysis can be found in table 1
In content.The training data of each grader is the various combination (referring to table 1) of image texture and edge feature, the prison of grader
Superintend and direct the evaluation grade that data integrate damage degree for each disaster that seismological expert provides according to professional knowledge (referring to table 2).Each SVM
The sample size of classifier training data should be more than 200 groups of data (positive and negative sample number is respectively more than 100), and final each SVM divides
The classification precision of prediction of class device should be greater than 95%.Once classifier training is completed, then the output of the grader be then to earthquake and
The description of its secondary disaster damage degree.
(3) new earthquake disaster image is obtained based on unmanned plane
Remote control multi-rotor unmanned aerial vehicle is flown or hovered in disaster area overhead, and during flight, carry is in nothing
The camera of man-machine lower end carries out captured in real-time to the ground in disaster area, among the result of shooting is stored in into the internal memory of system,
And as desired by the data taken photo by plane using IEEE 802.11G wireless network transmissions agreement and according to MPEG4 compressed format
Among sending back the control of unmanned plane during flying ground and the output display subsystem on ground in real time, new earthquake disaster image is obtained.
(4) assessment of damage degree is carried out
To the new earthquake disaster image of acquisition, respectively for house, road, bridge by way of user's hand labeled
Beam, dykes and dams, massif, river course, the quasi-representative atural object of vegetation 7 determine corresponding image-region, according to the image-region meter determined
Characteristics of image corresponding to every quasi-representative atural object is calculated, the last characteristics of image according to corresponding to per quasi-representative atural object uses corresponding support
The grader of vector machine carries out the assessment of damage degree.
The inventive method be more suitable for occur earthquake and secondary landslide, mud-rock flow, surface collapse, sedimentation with
The disaster information that early stage is carried out during the disaster that ftractures is collected and assessed.
Unspecified part of the present invention belongs to general knowledge as well known to those skilled in the art.
Claims (8)
1. a kind of earthquake disaster damage degree appraisal procedure based on unmanned plane, it is characterised in that realize that step is as follows：
(1) establish typical earthquake disaster image feature data collection, the typical earthquake disaster image feature data collection include house,
Road, bridge, dykes and dams, massif, river course and the earthquake disaster of vegetation image feature data collection；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, room is determined by way of user's hand labeled
The image-region in room obtains the image-region more than 200 houses, and the image-region in each house to determining calculates house
Characteristics of image, and damage degree in house corresponding to the image-region in each house is provided, so as to obtain the earthquake disaster figure in house
As characteristic data set；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, determine to engage in this profession by way of user's hand labeled
The image-region on road obtains the image-region more than 200 roads, and the image-region of each road to determining calculates road
Characteristics of image, and road damage degree corresponding to the image-region of each road is provided, so as to obtain the earthquake disaster figure of road
As characteristic data set；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, bridge is determined by way of user's hand labeled
The image-region of beam obtains the image-region more than 200 bridges, and the image spy of bridge is calculated to the image-region of each bridge
Sign, and bridge damage degree corresponding to the image-region of each bridge is provided, so as to obtain the earthquake disaster characteristics of image number of bridge
According to collection；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, dike is determined by way of user's hand labeled
The image-region on dam obtains the image-region more than 200 dykes and dams, and the image spy of dykes and dams is calculated to the image-region of each dykes and dams
Sign, and dykes and dams damage degree corresponding to the image-region of each dykes and dams is provided, so as to obtain the earthquake disaster characteristics of image number of dykes and dams
According to collection；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, determine to come out of retirement and take up an official post by way of user's hand labeled
The image-region of body obtains the image-region more than 200 massifs, and the image spy of massif is calculated to the image-region of each massif
Sign, and massif damage degree corresponding to the image-region of each massif is provided, so as to obtain the earthquake disaster characteristics of image number of massif
According to collection；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, river is determined by way of user's hand labeled
The image-region in road obtains the image-region more than 200 river courses, and the image spy in river course is calculated to the image-region in each river course
Sign, and damage degree in river course corresponding to the image-region in each river course is provided, so as to obtain the earthquake disaster characteristics of image number in river course
According to collection；
To the multiple earthquake disaster images obtained based on unmanned plane in the past, determine to plant by way of user's hand labeled
The image-region of quilt obtains the image-region more than 200 vegetation, and the image spy of vegetation is calculated to the image-region of each vegetation
Sign, and vegetation damage degree corresponding to the image-region of each vegetation is provided, so as to obtain the earthquake disaster characteristics of image number of vegetation
According to collection；
The characteristics of image in house includes gray level co-occurrence matrixes angular second moment index GLCMASM, gray level co-occurrence matrixes index of correlation
GLCMCOR, gray level co-occurrence matrixes entropy index GLCMENT, contrast index T in Tamura textural characteristicscon, Gabor direction characters
Index fgabor, utilize Hough transform carry out straight-line detection straight line number index HlLoop truss is carried out with using Hough transform
Circle number index Hc；
Road, bridge, the characteristics of image in dykes and dams and river course include gray level co-occurrence matrixes angular second moment index GLCMASM, gray scale it is common
Raw matrix inertia square index GLCMCON, gray level co-occurrence matrixes entropy index GLCMENT, gray level co-occurrence matrixes inverse difference moment index GLCMIDM、
Contrast index T in Tamura textural characteristicscon, Gabor direction character indexs fgaborStraight line is carried out with using Hough transform
The straight line number index H of detectionl；
Massif and the characteristics of image of vegetation include gray level co-occurrence matrixes angular second moment index GLCMASM, gray level co-occurrence matrixes inertia
Square index GLCMCON, gray level co-occurrence matrixes inverse difference moment index GLCMIDM, contrast index T in Tamura textural characteristicsconWith
Gabor direction character indexs fgabor；
(2) it is each using house, road, bridge, dykes and dams, massif, river course and the training of the earthquake disaster of vegetation image feature data collection
From support vector machine classifier；
(3) new earthquake disaster image is obtained based on unmanned plane；
(4) to the new earthquake disaster image of acquisition by way of user's hand labeled respectively for house, road, bridge,
Dykes and dams, massif, river course, the quasi-representative atural object of vegetation 7 determine corresponding image-region, are calculated according to the image-region determined every
Characteristics of image corresponding to quasi-representative atural object, supporting vector corresponding to the last characteristics of image use according to corresponding to per quasi-representative atural object
The grader of machine carries out the assessment per quasi-representative atural object damage degree.
2. a kind of earthquake disaster damage degree appraisal procedure based on unmanned plane according to claim 1, its feature exist
In：The earthquake disaster includes earthquake and its secondary disaster, and secondary disaster includes landslide, mud-rock flow, surface collapse, sedimentation
With cracking.
3. a kind of earthquake disaster damage degree appraisal procedure based on unmanned plane according to claim 1, its feature exist
In：For road, bridge, dykes and dams and river course, the method that corresponding image-region is determined by way of user's hand labeled
It is as follows：The edge placement of road, bridge, dykes and dams and river course is marked by the way of straight line or Drawing of Curve, then extracts two
Straight line, the image-region between two curves or straight line and a curve is as the image-region determined.
4. a kind of earthquake disaster damage degree appraisal procedure based on unmanned plane according to claim 1, its feature exist
In：For house, the method that the image-region in house is determined by way of user's hand labeled is as follows：Using quadrangle hand
Work draws a circle to approve out the position in house, then extracts the image-region inside quadrangle as the image-region determined.
5. a kind of earthquake disaster damage degree appraisal procedure based on unmanned plane according to claim 1, its feature exist
In：For massif and vegetation, the method that corresponding image-region is determined by way of user's hand labeled is as follows：Using more
Side shape draws a circle to approve out the region of massif and vegetation by hand, then extracts the image-region of polygonal internal as the image district determined
Domain.
6. a kind of earthquake disaster damage degree appraisal procedure based on unmanned plane according to claim 1, its feature exist
In：
<mrow>
<msub>
<mi>GLCM</mi>
<mi>ASM</mi>
</msub>
<mo>=</mo>
<munder>
<mi>&Sigma;</mi>
<mi>i</mi>
</munder>
<munder>
<mi>&Sigma;</mi>
<mi>j</mi>
</munder>
<mi>p</mi>
<msup>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
<mrow>
<msub>
<mi>GLCM</mi>
<mi>CON</mi>
</msub>
<mo>=</mo>
<munder>
<mi>&Sigma;</mi>
<mi>i</mi>
</munder>
<munder>
<mi>&Sigma;</mi>
<mi>j</mi>
</munder>
<msup>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>-</mo>
<mi>j</mi>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mi>p</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<msub>
<mi>GLCM</mi>
<mi>COR</mi>
</msub>
<mo>=</mo>
<mfrac>
<mrow>
<munder>
<mi>&Sigma;</mi>
<mi>i</mi>
</munder>
<munder>
<mi>&Sigma;</mi>
<mi>j</mi>
</munder>
<mrow>
<mo>(</mo>
<mi>ij</mi>
<mo>)</mo>
</mrow>
<mi>p</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
<mo>)</mo>
</mrow>
<mo>-</mo>
<msubsup>
<mi>&mu;</mi>
<mi>x</mi>
<mi>GLCM</mi>
</msubsup>
<msubsup>
<mi>&mu;</mi>
<mi>y</mi>
<mi>GLCM</mi>
</msubsup>
</mrow>
<mrow>
<msubsup>
<mi>&sigma;</mi>
<mi>x</mi>
<mi>GLCM</mi>
</msubsup>
<msubsup>
<mi>&sigma;</mi>
<mi>y</mi>
<mi>GLCM</mi>
</msubsup>
</mrow>
</mfrac>
</mrow>
<mrow>
<msub>
<mi>GLCM</mi>
<mi>ENT</mi>
</msub>
<mo>=</mo>
<munder>
<mrow>
<mo>-</mo>
<mi>&Sigma;</mi>
</mrow>
<mi>i</mi>
</munder>
<munder>
<mi>&Sigma;</mi>
<mi>j</mi>
</munder>
<mi>p</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
<mo>)</mo>
</mrow>
<mi>log</mi>
<mo>[</mo>
<mi>p</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
<mo>)</mo>
</mrow>
<mo>]</mo>
</mrow>
<mrow>
<msub>
<mi>GLCM</mi>
<mi>IDM</mi>
</msub>
<mo>=</mo>
<munder>
<mi>&Sigma;</mi>
<mi>i</mi>
</munder>
<munder>
<mi>&Sigma;</mi>
<mi>j</mi>
</munder>
<mfrac>
<mn>1</mn>
<mrow>
<mn>1</mn>
<mo>+</mo>
<msup>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>-</mo>
<mi>j</mi>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</mfrac>
<mi>p</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
<mo>)</mo>
</mrow>
</mrow>
Wherein, p (i, j) is the gray level co-occurrence matrixes for the image-region determined, coordinate (i, j) represents the position 2 for the image-region determined
Confidence ceases, and is respectively
<mrow>
<msub>
<mi>p</mi>
<mi>x</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<munder>
<mi>&Sigma;</mi>
<mi>k</mi>
</munder>
<mi>p</mi>
<mrow>
<mo>(</mo>
<mi>i</mi>
<mo>,</mo>
<mi>k</mi>
<mo>)</mo>
</mrow>
</mrow>
Sum
Average and mean square deviation, i=1,2 ..., maximum that Ng, j=1,2 ..., Ng, Ng are single pixel gray level.
7. a kind of earthquake disaster damage degree appraisal procedure based on unmanned plane according to claim 1, its feature exist
In：
<mrow>
<msub>
<mi>T</mi>
<mi>con</mi>
</msub>
<mo>=</mo>
<mfrac>
<mi>&sigma;</mi>
<msubsup>
<mi>&alpha;</mi>
<mn>4</mn>
<mrow>
<mn>1</mn>
<mo>/</mo>
<mn>4</mn>
</mrow>
</msubsup>
</mfrac>
</mrow>
<mrow>
<msub>
<mi>&alpha;</mi>
<mn>4</mn>
</msub>
<mo>=</mo>
<mfrac>
<msub>
<mi>&mu;</mi>
<mn>4</mn>
</msub>
<msup>
<mi>&sigma;</mi>
<mn>4</mn>
</msup>
</mfrac>
</mrow>
Wherein μ4It is the fourth central square of image patch, σ is the mean square deviation for the image-region determined, α4For intermediate variable.
8. a kind of earthquake disaster damage degree appraisal procedure based on unmanned plane according to claim 1, its feature exist
In：Gabor direction character indexs fgaborCalculation formula it is as follows：
<mrow>
<mi>g</mi>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>,</mo>
<mi>y</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<mrow>
<mn>2</mn>
<mi>&pi;</mi>
<msub>
<mi>&sigma;</mi>
<mi>x</mi>
</msub>
<msub>
<mi>&sigma;</mi>
<mi>y</mi>
</msub>
</mrow>
</mfrac>
<mi>exp</mi>
<mo>[</mo>
<mo>-</mo>
<mfrac>
<mn>1</mn>
<mn>2</mn>
</mfrac>
<mrow>
<mo>(</mo>
<mfrac>
<msup>
<mi>x</mi>
<mn>2</mn>
</msup>
<msubsup>
<mi>&sigma;</mi>
<mi>x</mi>
<mn>2</mn>
</msubsup>
</mfrac>
<mo>+</mo>
<mfrac>
<msup>
<mi>y</mi>
<mn>2</mn>
</msup>
<msubsup>
<mi>&sigma;</mi>
<mi>y</mi>
<mn>2</mn>
</msubsup>
</mfrac>
<mo>)</mo>
</mrow>
<mo>+</mo>
<mn>2</mn>
<mi>&pi;jWx</mi>
<mo>]</mo>
</mrow>
gmn(x, y)=a-mg(x′,y′)
X '=a-m(xcosθ+ysinθ)
Y '=a-m(-xsinθ+ycosθ)
<mrow>
<msub>
<mi>W</mi>
<mi>mn</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>,</mo>
<mi>y</mi>
<mo>)</mo>
</mrow>
<mo>=</mo>
<mo>&Integral;</mo>
<mo>&Integral;</mo>
<mi>I</mi>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>,</mo>
<mi>y</mi>
<mo>)</mo>
</mrow>
<msubsup>
<mi>g</mi>
<mi>mn</mi>
<mo>*</mo>
</msubsup>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>-</mo>
<msub>
<mi>x</mi>
<mn>1</mn>
</msub>
<mo>,</mo>
<mi>y</mi>
<mo>-</mo>
<msub>
<mi>y</mi>
<mn>1</mn>
</msub>
<mo>)</mo>
</mrow>
<mi>d</mi>
<msub>
<mi>x</mi>
<mn>1</mn>
</msub>
<mi>d</mi>
<msub>
<mi>y</mi>
<mn>1</mn>
</msub>
</mrow>
μmn=∫ ∫ | Wmn(x,y)|dxdy
<mrow>
<msub>
<mi>&sigma;</mi>
<mi>mn</mi>
</msub>
<mo>=</mo>
<msqrt>
<mo>&Integral;</mo>
<mo>&Integral;</mo>
<msup>
<mrow>
<mo>[</mo>
<mo>|</mo>
<msub>
<mi>W</mi>
<mi>mn</mi>
</msub>
<mrow>
<mo>(</mo>
<mi>x</mi>
<mo>,</mo>
<mi>y</mi>
<mo>)</mo>
</mrow>
<mo>|</mo>
<mo>-</mo>
<msub>
<mi>&mu;</mi>
<mi>mn</mi>
</msub>
<mo>]</mo>
</mrow>
<mn>2</mn>
</msup>
<mi>dxdy</mi>
</msqrt>
</mrow>
Wherein, g (x, y) represents Two-Dimensional Gabor Wavelets basic function, and W is Gaussian function multiple modulation frequency, σx、σyFor variable x and y
Mean square deviation；gmn(x, y) is self similarity wave filter, a-mIt is scale factor, a>1, x ', y ' are to the result of calculation after x, y conversion, m
It is integer with n, represents corresponding yardstick and direction, m ∈ [0, M-1], M are all dimensions, n ∈ [0, K-1], θ=n π/K,
K is all direction numbers, and subscript " * " represents conjugate complex number, x1、y1Gabor wavelet integral transformation offset is represented, I (x, y) is represented
The image intensity of (x, y) opening position, WmnThe result that (x, y) converts for Gabor wavelet, μmn、σmnFor Gabor wavelet average with
Mean square deviation feature.
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| CN105096315B (en) * | 2015-06-19 | 2018-03-06 | 西安电子科技大学 | Heterogeneous super-pixel SAR image segmentation method based on Gamma distributions |
| WO2017173563A1 (en) * | 2016-04-05 | 2017-10-12 | 盛玉伟 | Method and system for detecting real estate quality |
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| CN112381060B (en) * | 2020-12-04 | 2022-05-20 | 哈尔滨工业大学 | Building earthquake damage level classification method based on deep learning |
| CN113283280B (en) * | 2021-02-22 | 2023-05-02 | 西北工业大学 | Ground target cloud model damage effect evaluation method based on visible light image processing |
| CN114927002B (en) * | 2022-04-28 | 2023-03-14 | 浙江中裕通信技术有限公司 | Road induction method and equipment for post-disaster rescue |
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