CN106295699A - A kind of earthquake Damage assessment method and apparatus based on high-definition remote sensing data - Google Patents

Abstract

The invention discloses a kind of earthquake Damage assessment method and apparatus based on high-definition remote sensing data, according to monitoring edge feature change, it is thus achieved that the gradient image of BEFORE AND AFTER EARTHQUAKE remote sensing image；Obtain the similarity characteristic image of described BEFORE AND AFTER EARTHQUAKE gradient image respectively, extract the similarity feature of described similarity graph picture；Ruin rate according to assessment and draw local earthquake disaster rapid evaluation figure.Therefore, described earthquake Damage assessment method and apparatus based on high-definition remote sensing data can apply simultaneously to satellite image and aviation image, and can compatibility image before and after the disaster of variation monitoring be allos sensor.

Description

A kind of earthquake Damage assessment method and apparatus based on high-definition remote sensing data

Technical field

The present invention relates to rapid evaluation technical field, particularly relate to a kind of earthquakes based on high-definition remote sensing data damage Appraisal procedure and device.

Background technology

Earthquake disaster disaster-stricken scope rapid evaluation refers to (usually within 48 hours) after earthquake disaster just occurs, In the case of or deficiency limited in disaster area ground investigation result, use the Real-time Remote Sensing image data quick obtaining that disaster area obtains Involved area scope and degree of susceptibility in this earthquake disaster.In earthquake disaster later evaluation practical study in recent years, It has been shown that the earthquake damage situation of city scope can be obtained by satellite image or high-resolution remote sensing images analysis Arrive.

Correlational study it turned out method based on variation monitoring and high-resolution remote sensing image can effectively assess earthquake Damage situation in disaster, such as Keiko Saito, waits and contrasts with ground data for by remote sensing image visual interpretation result Demonstrate the high-resolution satellite image reliability to Damage assessment based on region.Charles K.Huyck etc. use QuickBird data and Neighborhood Edge Dissimilarities characterization method, be used for assessing bam earthquake again High-resolution data is for the feasibility of rapid evaluation.

But in the prior art, Damage assessment figure based on remote sensing can only be become by the remote sensing image before and after monitoring disaster Change and draw, and satellite-based Damage assessment figure can only pass through satellite data.It is to say, due to satellite remote-sensing image number According to different sensors generate image spatial resolution, registration bias, the difference of sensor incident angle image, change can be affected The accuracy of detection method result, existing method can only need sensors of the same race to obtain by two image datas before and after disaster.

Summary of the invention

In view of this, it is an object of the invention to propose a kind of earthquake Damage assessment side based on high-definition remote sensing data Method and device, solve the problem that the existing Damage assessment of shake over the ground can only use satellite image or aviation image.

Earthquake Damage assessment methods based on high-definition remote sensing data are provided, including step based on the above-mentioned purpose present invention Rapid:

According to monitoring edge feature change, it is thus achieved that the gradient image of BEFORE AND AFTER EARTHQUAKE remote sensing image；

Obtain the similarity characteristic image of described BEFORE AND AFTER EARTHQUAKE gradient image respectively, extract the similar of described similarity graph picture Degree feature；

Ruin rate according to assessment and draw local earthquake disaster rapid evaluation figure.

In some embodiments of the invention, described according to monitoring edge feature change, it is thus achieved that BEFORE AND AFTER EARTHQUAKE remote sensing image Gradient image, including:

Obtain disaster region BEFORE AND AFTER EARTHQUAKE remote sensing image, and carry out geographic registration；

According to the described BEFORE AND AFTER EARTHQUAKE remote sensing image after geographic registration, calculate the gradient map of BEFORE AND AFTER EARTHQUAKE remote sensing image respectively Picture.

In some embodiments of the invention, by equation below, the gradient map of BEFORE AND AFTER EARTHQUAKE remote sensing image is calculated respectively Picture:

$G(x,y)=\sqrt{{\left(f\right(x,y)-f(x+1,y+1\left)\right)}^2+{\left(f\right(x+1,y)-f(x,y+1\left)\right)}^2}$

$G(x^{\′},y^{\′})=\frac{G(x,y)-G_{\min }(x,y)}{G_{max}(x,y)-G_{min}(x,y)}$

Wherein, (x, y) is the single channel of source images to f, and G (x', y') is gradient image.

In some embodiments of the invention, obtain the similarity characteristic image of described BEFORE AND AFTER EARTHQUAKE gradient image respectively, Extract the similarity feature of described similarity graph picture, including:

By the gradient image gridding of BEFORE AND AFTER EARTHQUAKE remote sensing image；

The similarity characteristic image of the BEFORE AND AFTER EARTHQUAKE gradient image after computational gridding respectively；

The similarity feature of the similarity graph picture according to described BEFORE AND AFTER EARTHQUAKE gradient image, obtains change completely and serious change Change number of grid.

In some embodiments of the invention, according to equation below, the BEFORE AND AFTER EARTHQUAKE gradient image after computational gridding Similarity characteristic image:

$S\left(W\right)=\frac{{\mathrm{\Σ}}_{x=1}^k{\mathrm{\Σ}}_{y=1}^l\left(G_A\right(x,y)-G_{A-mean}(x,y\left)\right)*\left(G_B\right(x,y)-G_{B-mean}(x,y\left)\right)}{\sqrt{{\mathrm{\Σ}}_{x=1}^k{\mathrm{\Σ}}_{y=1}^l{\left(G_A\right(x,y)-G_{A-mean}(x,y\left)\right)}^2}*\sqrt{{\mathrm{\Σ}}_{x=1}^k{\mathrm{\Σ}}_{y=1}^l{\left(G_B\right(x,y)-G_{B-mean}(x,y\left)\right)}^2}}$

Wherein, G_A(x y) represents the Gradient Features image of T1 time (before calamity) grid W；G_B(x, after y) representing T2 time calamity The Gradient Features image of grid W；

Further, G_A-mean(x, y)=(∑ ∑ G_A(x, y))/H, represents the gradient meansigma methods of pixel in target a-quadrant. G_B-mean(x, y)=(∑ ∑ G_B(x, y))/H, represents the gradient meansigma methods of pixel in target B region；H is then pixel in grid Total number, k and l is respectively width and the height of target area.

On the other hand, present invention also offers a kind of earthquake Damage assessment device based on high-definition remote sensing data, Including:

Gradient image acquiring unit, for according to monitoring edge feature change, it is thus achieved that the gradient of BEFORE AND AFTER EARTHQUAKE remote sensing image Image；

Similarity feature acquiring unit, for obtaining the similarity graph picture of described BEFORE AND AFTER EARTHQUAKE gradient image respectively, extracts The similarity feature of described similarity graph picture；

Assessment unit, draws local earthquake disaster rapid evaluation figure for ruining rate according to assessment.

In some embodiments of the invention, gradient image acquiring unit, it is additionally operable to:

Obtain disaster region BEFORE AND AFTER EARTHQUAKE remote sensing image, and carry out geographic registration；

According to the described BEFORE AND AFTER EARTHQUAKE remote sensing image after geographic registration, calculate the gradient map of BEFORE AND AFTER EARTHQUAKE remote sensing image respectively Picture.

In some embodiments of the invention, by equation below, the gradient map of BEFORE AND AFTER EARTHQUAKE remote sensing image is calculated respectively Picture:

$G(x,y)=\sqrt{{\left(f\right(x,y)-f(x+1,y+1\left)\right)}^2+{\left(f\right(x+1,y)-f(x,y+1\left)\right)}^2}$

$G(x^{\′},y^{\′})=\frac{G(x,y)-G_{\min }(x,y)}{G_{max}(x,y)-G_{min}(x,y)}$

Wherein, (x, y) is the single channel of source images to f, and G (x', y') is gradient image.

In some embodiments of the invention, described similarity feature acquiring unit, it is additionally operable to:

By the gradient image gridding of BEFORE AND AFTER EARTHQUAKE remote sensing image；

The similarity characteristic image of the BEFORE AND AFTER EARTHQUAKE gradient image after computational gridding respectively；

The similarity feature of the similarity graph picture according to described BEFORE AND AFTER EARTHQUAKE gradient image, obtains change completely and serious change Change number of grid.

In some embodiments of the invention, according to equation below, the BEFORE AND AFTER EARTHQUAKE gradient image after computational gridding Similarity characteristic image:

$S\left(W\right)=\frac{{\mathrm{\Σ}}_{x=1}^k{\mathrm{\Σ}}_{y=1}^l\left(G_A\right(x,y)-G_{A-mean}(x,y\left)\right)*\left(G_B\right(x,y)-G_{B-mean}(x,y\left)\right)}{\sqrt{{\mathrm{\Σ}}_{x=1}^k{\mathrm{\Σ}}_{y=1}^l{\left(G_A\right(x,y)-G_{A-mean}(x,y\left)\right)}^2}*\sqrt{{\mathrm{\Σ}}_{x=1}^k{\mathrm{\Σ}}_{y=1}^l{\left(G_B\right(x,y)-G_{B-mean}(x,y\left)\right)}^2}}$

Wherein, G_A(x y) represents the Gradient Features image of T1 time (before calamity) grid W；G_B(x, after y) representing T2 time calamity The Gradient Features image of grid W；

Further, G_A-mean(x, y)=(∑ ∑ G_A(x, y))/H, represents the gradient meansigma methods of pixel in target a-quadrant. G_B-mean(x, y)=(∑ ∑ G_B(x, y))/H, represents the gradient meansigma methods of pixel in target B region；H is then pixel in grid Total number, k and l is respectively width and the height of target area.

From the above it can be seen that a kind of based on high-definition remote sensing data the earthquake Damage assessments that the present invention provides Method and apparatus, by variation monitoring analysis based on edge similar degree and ruin rate assess, it is achieved from pixel to object again to Assessment district extracts the assessment of damage information step by step, has reached built-up areas casualty loss rapid evaluation, and tentatively the sentencing of loss rate Fixed.Further, the present invention can apply simultaneously to satellite image and aviation image, and can be compatible before the disaster of variation monitoring Rear image is allos sensor.

Accompanying drawing explanation

Fig. 1 is that in first embodiment of the invention, the flow process of earthquake Damage assessment methods based on high-definition remote sensing data is shown It is intended to；

Fig. 2 is that the present invention refers to the flow process of earthquake Damage assessment methods based on high-definition remote sensing data in embodiment Schematic diagram；

Fig. 3 is the structural representation of earthquake Damage assessment devices based on high-definition remote sensing data in the embodiment of the present invention Figure；

Fig. 4 is remote sensing image data figure before and after earthquake disaster in the embodiment of the present invention；

Fig. 5 is the gradient image in the embodiment of the present invention on the basis of Fig. 4；

Fig. 6 is the similarity characteristic image in the embodiment of the present invention on the basis of Fig. 5；

Fig. 7 is change detecting method classification results figure based on Fig. 6 in the embodiment of the present invention；

Fig. 8 is rate of the ruining assessment figure in the embodiment of the present invention on the basis of Fig. 7.

Detailed description of the invention

For making the object, technical solutions and advantages of the present invention clearer, below in conjunction with specific embodiment, and reference Accompanying drawing, the present invention is described in more detail.

It should be noted that the statement of all uses " first " and " second " is for distinguishing two in the embodiment of the present invention The entity of individual same names non-equal or the parameter of non-equal, it is seen that " first " " second ", only for the convenience of statement, should not Being interpreted as the restriction to the embodiment of the present invention, this is illustrated by subsequent embodiment the most one by one.

Refering to shown in Fig. 1, for earthquake Damage assessment methods based on high-definition remote sensing data in the embodiment of the present invention Schematic flow sheet, described earthquake Damage assessment methods based on high-definition remote sensing data include:

Step 101, according to monitoring edge feature change, it is thus achieved that the gradient image of BEFORE AND AFTER EARTHQUAKE remote sensing image.

As embodiment, the gradient map of BEFORE AND AFTER EARTHQUAKE remote sensing image according to BEFORE AND AFTER EARTHQUAKE remote sensing image, can be calculated respectively Picture.It is preferred that by equation below, the gradient image of calculating BEFORE AND AFTER EARTHQUAKE remote sensing image respectively:

$G(x,y)=\sqrt{{\left(f\right(x,y)-f(x+1,y+1\left)\right)}^2+{\left(f\right(x+1,y)-f(x,y+1\left)\right)}^2}$

$G(x^{\′},y^{\′})=\frac{G(x,y)-G_{\min }(x,y)}{G_{max}(x,y)-G_{min}(x,y)}$

Wherein, (x, y) is the single channel of source images to f, and G (x', y') is gradient image.

Preferably, before calculating the gradient image of BEFORE AND AFTER EARTHQUAKE remote sensing image, need the disaster region earthquake obtained Front and back remote sensing image carries out geographic registration.Wherein it is possible to utilize Arcgis software to be carried out by the BEFORE AND AFTER EARTHQUAKE remote sensing image of acquisition Geographic registration.

Furthermore it is also possible to the gradient image of BEFORE AND AFTER EARTHQUAKE remote sensing image is carried out gridding.It is preferred that can be according to locality The average length L of assessment target (building), and remote sensing image spatial resolution R obtained, select sizing grid.Preferably Ground, uses aromatic sampling principle, chooses sizing grid.In this embodiment, close to S=0.5*L/R, certainly according to practical situation Can be adjusted.

Step 102, obtains the similarity graph picture of described BEFORE AND AFTER EARTHQUAKE gradient image respectively, extract described similarity graph as Similarity feature.

In this embodiment, the similarity graph picture of the BEFORE AND AFTER EARTHQUAKE gradient image after computational gridding, passes through equation below:

$S\left(W\right)=\frac{{\mathrm{\Σ}}_{x=1}^k{\mathrm{\Σ}}_{y=1}^l\left(G_A\right(x,y)-G_{A-mean}(x,y\left)\right)*\left(G_B\right(x,y)-G_{B-mean}(x,y\left)\right)}{\sqrt{{\mathrm{\Σ}}_{x=1}^k{\mathrm{\Σ}}_{y=1}^l{\left(G_A\right(x,y)-G_{A-mean}(x,y\left)\right)}^2}*\sqrt{{\mathrm{\Σ}}_{x=1}^k{\mathrm{\Σ}}_{y=1}^l{\left(G_B\right(x,y)-G_{B-mean}(x,y\left)\right)}^2}}$

Wherein, G_A(x y) represents the Gradient Features image of T1 time (before calamity) grid W.G_B(x y) represents T2 time (calamity The Gradient Features image of grid W afterwards).Further, G_A-mean(x, y)=(∑ ∑ G_A(x, y))/H, represents pixel in target a-quadrant Gradient meansigma methods.G_B-mean(x, y)=(∑ ∑ G_B(x, y))/H, represents the gradient meansigma methods of pixel in target B region.H is then Total number of pixel in grid, k and l is respectively width and the height of target area.

Step 103, ruins rate according to assessment and draws local earthquake disaster rapid evaluation figure.

In the embodiment referred to of the present invention, as in figure 2 it is shown, described is based on high-definition remote sensing data Earthquake Damage assessment method includes:

Step 201, obtains disaster region BEFORE AND AFTER EARTHQUAKE remote sensing image, and carries out geographic registration.

Wherein it is possible to utilize Arcgis software that the BEFORE AND AFTER EARTHQUAKE remote sensing image of acquisition is carried out geographic registration.In embodiment In, geographic registration is according to same projection mode by two scape images front and back, projects in the same coordinate system, and this process is by The basis of geocomputation.

Step 202, according to the described BEFORE AND AFTER EARTHQUAKE remote sensing image after geographic registration, calculates BEFORE AND AFTER EARTHQUAKE remote sensing image respectively Gradient image.

Preferably, by equation below, the gradient image of calculating BEFORE AND AFTER EARTHQUAKE remote sensing image respectively:

$G(x,y)=\sqrt{{\left(f\right(x,y)-f(x+1,y+1\left)\right)}^2+{\left(f\right(x+1,y)-f(x,y+1\left)\right)}^2}$

$G(x^{\′},y^{\′})=\frac{G(x,y)-G_{\min }(x,y)}{G_{max}(x,y)-G_{min}(x,y)}$

Wherein, (x, y) is the single channel of source images to f, and G (x', y') is gradient image.

Step 203, by the gradient image gridding of BEFORE AND AFTER EARTHQUAKE remote sensing image.

It is preferred that the average length L of target (building) can be assessed according to locality, and the remote sensing image space obtained Resolution R, selects sizing grid.Preferably, use aromatic sampling principle, choose sizing grid.In this embodiment, close to S =0.5*L/R, can be adjusted according to practical situation certainly.Such as: local building average length is 20 meters, remote sensing image Spatial resolution is 0.5 meter, then mesh scale is chosen as 20.

Step 204, respectively the similarity characteristic image of the BEFORE AND AFTER EARTHQUAKE gradient image after computational gridding.

Preferably, according to equation below, the similarity characteristic image of the BEFORE AND AFTER EARTHQUAKE gradient image after computational gridding:

$S\left(W\right)=\frac{{\mathrm{\Σ}}_{x=1}^k{\mathrm{\Σ}}_{y=1}^l\left(G_A\right(x,y)-G_{A-mean}(x,y\left)\right)*\left(G_B\right(x,y)-G_{B-mean}(x,y\left)\right)}{\sqrt{{\mathrm{\Σ}}_{x=1}^k{\mathrm{\Σ}}_{y=1}^l{\left(G_A\right(x,y)-G_{A-mean}(x,y\left)\right)}^2}*\sqrt{{\mathrm{\Σ}}_{x=1}^k{\mathrm{\Σ}}_{y=1}^l{\left(G_B\right(x,y)-G_{B-mean}(x,y\left)\right)}^2}}$

Wherein, G_A(x y) represents the Gradient Features image of T1 time (before calamity) grid W.G_B(x y) represents T2 time (calamity The Gradient Features image of grid W afterwards).

Further, G_A-mean(x, y)=(∑ ∑ G_A(x, y))/H, represents the gradient meansigma methods of pixel in target a-quadrant. G_B-mean(x, y)=(∑ ∑ G_B(x, y))/H, represents the gradient meansigma methods of pixel in target B region.H is then pixel in grid Total number, k and l is respectively width and the height of target area.

Step 205, according to the similarity feature of the similarity graph picture of described BEFORE AND AFTER EARTHQUAKE gradient image, obtains and changes completely Seriously change number of grid.Concrete implementation process includes:

Choose appropriate training sample by ENVI software, and use Maximum likelihood classification similarity graph picture to be carried out point Class.Wherein, when choosing sample, 30 to 50 training samples can be chosen, referring additionally to building according to EMS-1998 standard, By Grades 3,4 are divided into and change class completely, and by Grades1,2 are divided into and seriously change class, and Grade5 is divided into slight change Class.

Step 206, ruins rate according to assessment and draws local earthquake disaster rapid evaluation figure.

Preferably, utilize Arcgis software according to administrative division vector data and formula:

D_i=A_i/B_i

Calculate rate of the ruining assessment result in each administrative area, in above formula, A_iThe completely change corresponding for administrative area i and serious Change number of grid, B_iThe completely change corresponding for administrative area i and seriously change number of grid, D_iFall for administrative estimation corresponding for i Ruin rate.It is to say, use ArcGis software, give different colors, system by administrative area each in administrative division according to Di value Become local earthquake disaster rapid evaluation figure.

In another aspect of this invention, a kind of earthquake Damage assessments based on high-definition remote sensing data dress is additionally provided Put, as it is shown on figure 3, described earthquake Damage assessment device based on high-definition remote sensing data includes the gradient image being sequentially connected with Acquiring unit 301, similarity feature acquiring unit 302 and assessment unit 303.Wherein, gradient image acquiring unit 301 basis Monitoring edge feature change, it is thus achieved that the gradient image of BEFORE AND AFTER EARTHQUAKE remote sensing image.Similarity feature acquiring unit 302 obtains respectively The similarity graph picture of described BEFORE AND AFTER EARTHQUAKE gradient image, extracts the similarity feature of described similarity graph picture.Assessment unit 303 Ruin rate according to assessment and draw local earthquake disaster rapid evaluation figure.

It should be noted that at the tool of earthquake Damage assessment devices based on high-definition remote sensing data of the present invention Body implementation content, is described in detail in the earthquake Damage assessment method being based on high-definition remote sensing data described above , therefore no longer illustrate at this duplicate contents.

The present invention is according to methods described above and device, and on April 14th, 2010, Tibetan Autonomous Prefecture of Yushu of Qinghai Province was beautiful Tree county occurs Ms7.1 level earthquake to carry out rapid evaluation.Fig. 4 is shown that the earthquake of a specific embodiment according to the present invention Remote sensing image data figure before and after disaster, the ikonos image obtained before wherein left side is earthquake, obtain after the shake of right side QuickBird image；Fig. 5 is the gradient image calculated respectively on the basis of Fig. 4；Fig. 6 is the similarity characteristic pattern on the basis of Fig. 5 Picture；Fig. 7 is change detecting method classification results based on Fig. 6.Fig. 8 is rate of the ruining assessment figure on the basis of Fig. 7.

Wherein, as shown in Figure 4, QuickBird remote sensing after Ikonos remote sensing image data and earthquake before local earthquake is obtained Image data, and carry out spatial registration by Arcgis software.As it is shown in figure 5, the gradient map of image before and after calculating disaster respectively Picture, is 20 meters according to local building average length, and image spatial resolution is 0.6 meter, determines that sizing grid is 17.Further, As shown in Figure 8, ruin rate according to assessment and draw local earthquake disaster rapid evaluation figure, different colours can be used by the extent of damage Show.

In sum, a kind of present invention provides earthquake Damage assessment method being based on high-definition remote sensing data and Device, can be after the earthquake, and the urban architecture thing of building that rapid evaluation is caused by earthquake ruins situation, meets an urgent need in advance to selecting to start The Emergency response level of case has relatively high reference and is worth, and specifies the rescue of the victims of the disaster and rehousing programme to provide foundation for policymaker；Enter One step ground, the present invention by first extracting characteristics of image, after carry out the mode of similarity comparison, and then can compatibility be used for changing prison Before and after the disaster surveyed, image is allos sensor；Thus, the present invention has extensive, great dissemination；Finally, whole described Earthquake Damage assessment method and apparatus based on high-definition remote sensing data are compact, it is easy to control.

Those of ordinary skill in the field are it is understood that the discussion of any of the above embodiment is exemplary only, not It is intended to imply that the scope of the present disclosure (including claim) is limited to these examples；Under the thinking of the present invention, above example Or can also be combined between the technical characteristic in different embodiments, step can realize with random order, and exists such as Other change of the many of the different aspect of the upper described present invention, in order to concisely they do not provide in details.

It addition, for simplifying explanation and discussing, and in order to obscure the invention, can in the accompanying drawing provided To illustrate or can not illustrate and integrated circuit (IC) chip and the known power supply/grounding connection of other parts.Furthermore, it is possible to Device is shown in block diagram form, in order to avoid obscuring the invention, and this have also contemplated that following facts, i.e. about this The details of the embodiment of a little block diagram arrangements be the platform that depends highly on and will implement the present invention (that is, these details should In the range of being completely in the understanding of those skilled in the art).Elaborating that detail (such as, circuit) is to describe the present invention's In the case of exemplary embodiment, it will be apparent to those skilled in the art that can there is no these details In the case of or these details change in the case of implement the present invention.Therefore, these descriptions are considered as explanation Property rather than restrictive.

Although invention has been described to have been incorporated with the specific embodiment of the present invention, but according to retouching above Stating, a lot of replacements, amendment and the modification of these embodiments will be apparent from for those of ordinary skills.Example As, other memory architecture (such as, dynamic ram (DRAM)) can use discussed embodiment.

Embodiments of the invention be intended to fall into all such replacement within the broad range of claims, Amendment and modification.Therefore, all within the spirit and principles in the present invention, any omission of being made, amendment, equivalent, improvement Deng, should be included within the scope of the present invention.

Claims (10)

1. an earthquake Damage assessment method based on high-definition remote sensing data, it is characterised in that include step:

According to monitoring edge feature change, it is thus achieved that the gradient image of BEFORE AND AFTER EARTHQUAKE remote sensing image；

Obtaining the similarity characteristic image of described BEFORE AND AFTER EARTHQUAKE gradient image respectively, the similarity extracting described similarity graph picture is special Levy；

Ruin rate according to assessment and draw local earthquake disaster rapid evaluation figure.

Method the most according to claim 1, it is characterised in that described according to monitoring edge feature change, it is thus achieved that before earthquake The gradient image of rear remote sensing image, including:

Obtain disaster region BEFORE AND AFTER EARTHQUAKE remote sensing image, and carry out geographic registration；

According to the described BEFORE AND AFTER EARTHQUAKE remote sensing image after geographic registration, calculate the gradient image of BEFORE AND AFTER EARTHQUAKE remote sensing image respectively.

Method the most according to claim 2, it is characterised in that by equation below, calculates BEFORE AND AFTER EARTHQUAKE remote sensing shadow respectively The gradient image of picture:

$G(x,y)=\sqrt{{\left(f\right(x,y)-f(x+1,y+1\left)\right)}^2+{\left(f\right(x+1,y)-f(x,y+1\left)\right)}^2}$

$G(x^{\′},y^{\′})=\frac{G(x,y)-G_{\min }(x,y)}{G_{\max }(x,y)-G_{\min }(x,y)}$

Wherein, (x, y) is the single channel of source images to f, and G (x', y') is gradient image.

Method the most according to claim 2, it is characterised in that obtain the similarity of described BEFORE AND AFTER EARTHQUAKE gradient image respectively Characteristic image, extracts the similarity feature of described similarity graph picture, including:

By the gradient image gridding of BEFORE AND AFTER EARTHQUAKE remote sensing image；

The similarity characteristic image of the BEFORE AND AFTER EARTHQUAKE gradient image after computational gridding respectively；

The similarity feature of the similarity graph picture according to described BEFORE AND AFTER EARTHQUAKE gradient image, obtains change completely and seriously changes net Lattice quantity.

Method the most according to claim 4, it is characterised in that according to equation below, the BEFORE AND AFTER EARTHQUAKE after computational gridding The similarity characteristic image of gradient image:

$S\left(W\right)=\frac{{\mathrm{\Σ}}_{x=1}^k{\mathrm{\Σ}}_{y=1}^l(G_A(x,y)-G_{A-mean}(x,y))*(G_B(x,y)-G_{B-mean}(x,y))}{\sqrt{{\mathrm{\Σ}}_{x=1}^k-{\mathrm{\Σ}}_{y=1}^l{\left(G_A\right(x,y)-G_{A-mean}(x,y\left)\right)}^2}*\sqrt{{\mathrm{\Σ}}_{x=1}^k-{\mathrm{\Σ}}_{y=1}^l{\left(G_B\right(x,y)-G_{B-mean}(x,y\left)\right)}^2}}$

Wherein, G_A(x y) represents the Gradient Features image of T1 time (before calamity) grid W；G_B(x y) represents grid W after T2 time calamity Gradient Features image；

Further, G_A-mean(x, y)=(∑ ∑ G_A(x, y))/H, represents the gradient meansigma methods of pixel in target a-quadrant.G_B-mean(x, Y)=(∑ ∑ G_B(x, y))/H, represents the gradient meansigma methods of pixel in target B region；H is then total number of pixel in grid, k With width and the height that l is respectively target area.

6. an earthquake Damage assessment device based on high-definition remote sensing data, it is characterised in that including:

Gradient image acquiring unit, for according to monitoring edge feature change, it is thus achieved that the gradient image of BEFORE AND AFTER EARTHQUAKE remote sensing image；

Similarity feature acquiring unit, for obtaining the similarity graph picture of described BEFORE AND AFTER EARTHQUAKE gradient image respectively, extracts described The similarity feature of similarity graph picture；

Assessment unit, draws local earthquake disaster rapid evaluation figure for ruining rate according to assessment.

Device the most according to claim 6, it is characterised in that gradient image acquiring unit, is additionally operable to:

Obtain disaster region BEFORE AND AFTER EARTHQUAKE remote sensing image, and carry out geographic registration；

According to the described BEFORE AND AFTER EARTHQUAKE remote sensing image after geographic registration, calculate the gradient image of BEFORE AND AFTER EARTHQUAKE remote sensing image respectively.

Device the most according to claim 7, it is characterised in that by equation below, calculates BEFORE AND AFTER EARTHQUAKE remote sensing shadow respectively The gradient image of picture:

$G(x,y)=\sqrt{{\left(f\right(x,y)-f(x+1,y+1\left)\right)}^2+{\left(f\right(x+1,y)-f(x,y+1\left)\right)}^2}$

$G(x^{\′},y^{\′})=\frac{G(x,y)-G_{\min }(x,y)}{G_{\max }(x,y)-G_{\min }(x,y)}$

Wherein, (x, y) is the single channel of source images to f, and G (x', y') is gradient image.

Device the most according to claim 7, it is characterised in that described similarity feature acquiring unit, is additionally operable to:

By the gradient image gridding of BEFORE AND AFTER EARTHQUAKE remote sensing image；

The similarity characteristic image of the BEFORE AND AFTER EARTHQUAKE gradient image after computational gridding respectively；

The similarity feature of the similarity graph picture according to described BEFORE AND AFTER EARTHQUAKE gradient image, obtains change completely and seriously changes net Lattice quantity.

Device the most according to claim 9, it is characterised in that according to equation below, the BEFORE AND AFTER EARTHQUAKE after computational gridding The similarity characteristic image of gradient image:

$S\left(W\right)=\frac{{\mathrm{\Σ}}_{x=1}^k{\mathrm{\Σ}}_{y=1}^l(G_A(x,y)-G_{A-mean}(x,y))*(G_B(x,y)-G_{B-mean}(x,y))}{\sqrt{{\mathrm{\Σ}}_{x=1}^k-{\mathrm{\Σ}}_{y=1}^l{\left(G_A\right(x,y)-G_{A-mean}(x,y\left)\right)}^2}*\sqrt{{\mathrm{\Σ}}_{x=1}^k-{\mathrm{\Σ}}_{y=1}^l{\left(G_B\right(x,y)-G_{B-mean}(x,y\left)\right)}^2}}$

Wherein, G_A(x y) represents the Gradient Features image of T1 time (before calamity) grid W；G_B(x y) represents grid W after T2 time calamity Gradient Features image；

Further, G_A-mean(x, y)=(∑ ∑ G_A(x, y))/H, represents the gradient meansigma methods of pixel, G in target a-quadrant_B-mean(x, Y)=(∑ ∑ G_B(x, y))/H, represents the gradient meansigma methods of pixel in target B region；H is then total number of pixel in grid, k With width and the height that l is respectively target area.