CN102967850A - Disaster remote sensing automatic identification method based on multi-scale grid and fractal dimensional changes - Google Patents
Disaster remote sensing automatic identification method based on multi-scale grid and fractal dimensional changes Download PDFInfo
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Abstract
The invention aims at designing a disaster remote sensing automatic identification method based on a multi-scale grid and fractal dimensional changes so as to meet the requirements of an earth surface disaster monitoring mechanism, a satellite earth receive station and a satellite operating on track for identifying earth surface disasters automatically. The method includes the steps: constructing a remote-sensing image pair automatically; intercepting an image block in an area of interest automatically; dividing the multi-scale grid of the image block automatically; calculating fractal dimensional changes of sub-image-blocks automatically; and identifying earth surface disasters automatically. The method is characterized in that a disaster identification process is finished automatically through a computer without manual intervention and the method can provide technical support for the earth surface disaster monitoring mechanism, the satellite earth receive station and the satellite operating on track to identify the earth surface disasters automatically and conduct track monitoring.
Description
Technical field
The present invention relates to disaster remote sensing field, be specifically related to the automatically method of identification of earth's surface disaster remote sensing.
Background technology
Remote sensing refers to use sensor that electromagenetic wave radiation, the reflection characteristic of object are carried out non-contacting, long-range detection, and the theory of character, feature and the state of object being analyzed according to its characteristic, the science and technology of methods and applications.Along with the development of sensor technology, aeronautical and space technology and data communication technology, modern Remote Sensing Technical has entered that energy is dynamic, quick, multi-platform, multidate, provide new stage of earth observation data to high resolving power.The earth's surface disaster is the common issue with that human society faces, and the timely monitoring of great earth's surface disaster and fast response are public safety and the scientific and technical severe challenge that faces, and fast reliable Disaster Event automatic sensing is most important.Contrast the remote sensing image of same period not time, can extract disaster information and calamity field feature based on changing to detect.But, affected by remotely-sensed data uncertainty and earth's surface disaster complicacy, at present its disaster identification of variation monitoring based on remotely-sensed data mainly relies on man-machine interaction and visual interpretation, human intervention is essential, can't realize the automatic identification of Disasters, more can't satisfy satellite in orbit in the automatic identification requirement of earth's surface disaster.
Fractal Objective Concept U.S. mathematician Mandelbrot (B.B.Mandelbort) at first proposes.At the fractal theory that the basis of fractal and fractal geometry take the irregular geometry form as research object proposes, be very active a kind of new thought, new method and new theory of current scientific research field.Self similarity principle and grey iterative generation principle are the cardinal principle of fractal theory, and it characterizes fractally has unchangeability under common geometric transformation, i.e. scaling.Self-similarity in the fractal body can be identical, also can be similar on the statistical significance.The self-similar fractal of standard is mathematical abstract, and self-similarity is the symmetry formation recurrence from different yardsticks, and the unlimited meticulous structure of grey iterative generation is such as section's contract (Koch) snowflake curve, Xie Erbinsiji (Sierpinski) carpet curve etc.In fact, form with self-similarity extensively is present in occurring in nature, the different types of physical form of occurring in nature and space distribution thereof generally have different fractal dimension (be called for short minute dimension), and between the gray scale of natural fractal and image certain corresponding relation are arranged.Textural characteristics and the degree of roughness of therefore dividing dimension can be used for the Description Image surface, the fractal dimension value of remote sensing images can be described earth surface space structure information effectively.According to fractal theory, minute dimension is the important parameter to non-smooth, irregular, extremely complicated fractal the carries out quota portray such as form is broken, has characterized complexity and the degree of roughness of fractal, and namely the texture in the image is more complicated, and minute dimension is larger; Texture is simpler, and minute dimension is less; The similar texture of complexity has roughly the same minute dimension.Therefore, also corresponding different minute dimension just of the texture of different complexities in the image.
After earthquake, flood, landslide, rubble flow, hurricane, tsunami, fistula of perineum wet goods earth's surface disaster occured, the earth's surface destroys because of disaster or foreign matter covers, the corresponding change of textural characteristics meeting of its remote sensing images, thus its fractal dimension value is changed.By a series of mathematical computations, the change of fractal dimension of corresponding region in the two scape remote sensing images before and after can detecting, thereby disaster unit and the calamity field scope of definite earth's surface disaster, and judge disaster intensity.Accordingly, but initiate emergency plan carries out analysis confirmation, then starts the scheme that current satellite focuses on observation and the collaborative observation of other satellites, realizes the tracking and monitoring of great earth's surface disaster and disaster chain.
The disaster remote sensing automatic identifying method based on multiple dimensioned graticule mesh and change of fractal dimension that the present invention proposes, its overall process is by computing machine independent processing, automatically computing, break away from manual intervention fully, be a kind of suitable earth's surface disaster monitoring mechanism, satellite earth receiving station and in orbit in satellite automatically identify the method for earth's surface disaster.
List of references:
[1]Clarke，K.C.″Computation?of?the?fractal?dimension?of?topographic?surfaces?using?the?triangularprism?surface?area?method.″Computers?&?Geosciences，1986.12(5)：713-722.
[2]Sun，W.，G.Xu，et?al.″Fractal?analysis?of?remotely?sensed?images：A?review?of?methods?andapplications.″International?Journal?of?Remote?Sensing，2006.27(22)：4963-4990.
[3]Mandelbrot，B.B.″Self-Affine?Fractals?and?Fractal?Dimension.″Physica?Scripta，1985.32(4)：257.
[4] Chen Wenkai, what Shaolin, Zhou Zhonghong, Zhang Suping. the research of BEFORE AND AFTER EARTHQUAKE remote sensing image fractal characteristic. geodetic surveying and geodynamics, 2010, (6): 24-30.
[5] Yang Yancong, Peng Ruidong, Zhou Hongwei. the fractal dimension computing method of three dimensions digital picture. China Mining University's journal, 2009, (2): 251-258.
[6] Chen Aiqun. domestic and international remote sensing state-of-the-art technology and development trend thereof. mapping scientific and technological information, 2008, (1): 12-15.
Summary of the invention
The objective of the invention is to design a kind of disaster remote sensing automatic identifying method based on multiple dimensioned graticule mesh and change of fractal dimension, thus satisfy earth's surface disaster monitoring mechanism, satellite earth receiving station and in orbit in satellite automatically identify the needs of earth's surface disaster.The method step core is as follows: automatically make up remote sensing image pair; Automatically intercept the area-of-interest image blocks; Automatically divide the multiple dimensioned graticule mesh of image blocks; Automatically calculate the sub-image piece and divide dimension and variation thereof; Automatically identify the earth's surface disaster.The feature of the method is that the disaster identifying is finished automatically by computing machine fully, need not manual intervention, can be earth's surface disaster monitoring mechanism, satellite earth receiving station and in orbit in satellite automatically identify the line trace monitoring of going forward side by side of earth's surface disaster technical support be provided.
For reaching the purpose of foregoing invention, the invention provides a kind of disaster remote sensing automatic identifying method based on multiple dimensioned graticule mesh and change of fractal dimension.Described method comprises following concrete steps:
S1: take this observation image of covering region of interest as reference, extract the upper for the moment image of covering the same area of observation mutually from Image Database, consist of the image pair of geographical coupling, intercepting respectively pixel count from image centering is 2
n* 2
nThe image blocks of (general n 〉=10);
S2: image blocks is divided into 2
2i(i=0,1,2 ..., m, m≤n-7) individual pixel count is 2
N-i* 2
N-iThe sub-image piece, and calculate respectively minute dimension of each sub-image piece, and then calculate and the change of fractal dimension of each sub-image piece of recording image centering, finish the i time grid partition and change of fractal dimension calculating;
S3: repeat the S2 process until divide for the m time;
S4: with the change of fractal dimension of image antithetical phrase image blocks under the different graticule mesh yardsticks, be tied to form figure by the correspondence position pass of its sub-image piece in graticule mesh, obtain the space distribution of sub-image piece change of fractal dimension under the different graticule mesh yardsticks;
S5: the grid unit under the thinnest yardstick is as benchmark, retrieve one by one the change of fractal dimension of its higher level's grid unit, if the change of fractal dimension trend of the grid units at different levels of its correspondence consistent (having monotonicity) judges that then disaster has occured imagery zone corresponding to this grid unit;
S6: take the generation that determines the grid unit of disaster as set, according to the change of fractal dimension threshold value, identification hazard scope, and judge disaster intensity according to the change of fractal dimension size.
Wherein, described method also comprised the treatment step in early stage of satellite remote sensing date and image before step S1:
SA: the imaging processing of satellite remote sensing date;
SB: utilize the Satellite TT data to determine (comprising satellite transient posture and orbit altitude, sensor attitude and parameter) ground coverage of every width of cloth image;
SC: the front and back two scape satellite images adjacent to the time that comprises area-of-interest carry out radiation, geometry correction and geographical registration.
Wherein, described method also comprises the subsequent applications step after step S6:
SD: when automatically identifying by above-mentioned steps may have the condition of a disaster in the area-of-interest time, then but initiate emergency plan carries out analysis confirmation, then start current satellite and focus on observation and the collaborative observation program of other satellites, realize the tracking and monitoring of great earth's surface disaster and disaster chain.
Wherein, among the step S3 of described method, divide number of times and determined by minute scaling interval of dimension.Scale is a kind of to the fractal description of natural things, and the fractal of occurring in nature set up in certain scaling interval often, and does not have self-similarity beyond this is interval.In the method that the present invention relates to, scaling interval determines that by the pixel count of multiple dimensioned graticule mesh width generally its pixel count should be more than or equal to 2
7
Wherein, among the step S6 of described method, the change of fractal dimension threshold value is pre-determined by a large amount of observation and analysis and the experimental data of zones of different, different calamity kinds.
Description of drawings
Fig. 1 is a kind of process flow diagram of the disaster remote sensing automatic identifying method based on multiple dimensioned graticule mesh and change of fractal dimension;
Fig. 2 is a kind of case synoptic diagram of the disaster remote sensing automatic identifying method based on multiple dimensioned graticule mesh and change of fractal dimension.
Embodiment
Following examples are used for explanation the present invention, but are not used for limiting the scope of the invention.
It is a kind of disaster remote sensing automatic identifying method process flow diagram based on multiple dimensioned graticule mesh and change of fractal dimension of the present invention shown in the Figure of description, described method specifically may further comprise the steps: choose this and image that upper a period of time, the phase satellite image consisted of pair, intercepting respectively pixel count is 2
n* 2
nThe image blocks of (usually n 〉=10); By multiple dimensioned graticule mesh it being divided into a series of pixel counts is 2
N-i* 2
N-i(i=0,1,2 ..., the sub-image piece of i≤n-7); Calculate respectively minute dimension of each sub-image piece under the different graticule mesh yardsticks, and then calculate the change of fractal dimension of sub-image piece under the same graticule mesh yardstick; With the change of fractal dimension of image antithetical phrase image blocks under the different graticule mesh yardsticks, be tied to form figure by the correspondence position pass of its sub-image piece in graticule mesh, obtain the space distribution of sub-image piece change of fractal dimension under the different graticule mesh yardsticks; According to the change of fractal dimension threshold value, the identification hazard scope, and according to change of fractal dimension size judgement disaster intensity.Accordingly, but initiate emergency plan carries out analysis confirmation, then starts current satellite and focuses on observation and the collaborative observation program of other satellites, realizes the tracking and monitoring of great earth's surface disaster and disaster chain.Whole processing procedure mainly is comprised of three phases: the multiple dimensioned grid partition of image blocks, sub-image piece change of fractal dimension calculate, the earth's surface disaster is identified automatically.
1. the multiple dimensioned grid partition of image blocks
It is a kind of multiple dimensioned regular grid division methods that the image that the present invention relates to is divided.
This grid partition method is at first take this observation image of covering region of interest as reference, extracts the upper for the moment image of covering the same area of observation mutually from Image Database, consists of the image pair of geographical coupling; Intercepting respectively pixel count based on geographic coordinate from image centering is 2
n* 2
nThe image blocks of (general n 〉=10); Image blocks is divided into 2
2i(i=0,1,2 ..., m, m≤n-7) individual pixel count is 2
N-i* 2
N-iThe sub-image piece, be designated as the i time division, calculate and minute dimension of each sub-image piece of recording image centering and changing, and then rule is carried out i+1 evenly division according to this.
Can get thus, the i time division obtains 2
2iIndividual sub-image blocks.Using sub-image piece that ranks number will divide rear gained each time (is 2 from the pixel count of remote sensing image centering intercepting by it at the raw video piece
n* 2
nImage blocks) on the position be numbered that (divide such as the i time: upper left corner sub-image block number is designated as (1,1), and lower left corner sub-image block number is designated as (2
i, 1), upper right corner sub-image block number is designated as (1,2
i), lower right corner sub-image block number is designated as (2
i, 2
i)).
The ordinal number that record is divided each time and the numbering of each sub-image piece are for its fractal dimension calculation and change retrieval and facilitate.
In the image blocks grid partition method that the present invention relates to, to judge after dividing for the i time and divide gained sub-image block size for i+1 time whether in the scaling interval of minute tieing up (in the method that the present invention relates to, scaling interval determines that by the pixel count of multiple dimensioned graticule mesh width generally its pixel count should be more than or equal to 2
7), only have and when satisfying condition, just carry out subsequent divided, stop otherwise divide.
2. sub-image piece change of fractal dimension calculates
The sub-image piece fractal dimension calculation that relates among the present invention can be selected existing fractal assessment algorithm, such as surperficial Prism Method, minute box method, variogram method, minute collimation method, Fourier power spectrum method etc.Answer minute dimension of sub-image piece to ask poor (minute dimension of upper for the moment phase image sub-image piece deducts minute dimension of the corresponding sub-image piece of current image) the image Middle Phase, obtain sub-image piece change of fractal dimension.
After the change of fractal dimension of whole sub-image pieces of finishing each yardstick grid partition gained calculates, be stored in the array, program is called this array realization disaster and is automatically identified in follow-up operation.
3. the earth's surface disaster is identified automatically
Calculate two stages by the multiple dimensioned grid partition of image blocks and sub-image piece change of fractal dimension, obtain the spatial distribution map of the change of fractal dimension under the different graticule mesh yardsticks, can present performance difference and the variation tendency of earth's surface variation under the different demarcation yardstick in the remote sensing image.Grid unit under the thinnest yardstick is as benchmark, retrieve one by one the change of fractal dimension of its higher level's grid unit, if the change of fractal dimension trend of the grid units at different levels of its correspondence consistent (having monotonicity) judges that then disaster has occured imagery zone corresponding to this grid unit; And then take determine disaster has occured grid unit as set, according to the change of fractal dimension threshold value, identification hazard scope, and judge disaster intensity according to change of fractal dimension size.
Above embodiment only is used for explanation the present invention; and be not limitation of the present invention; the those of ordinary skill in relevant technologies field; in the situation that does not break away from the spirit and scope of the present invention; can also make a variety of changes; therefore all technical schemes that are equal to also belong to category of the present invention, and scope of patent protection of the present invention should be limited by its claim.
Claims (4)
1. the disaster remote sensing automatic identifying method based on multiple dimensioned graticule mesh and change of fractal dimension is characterized in that, described method is calculated the multiple dimensioned grid partition of image and combined with change of fractal dimension, automatically finishes disaster identification overall process.
2. a kind of disaster remote sensing automatic identifying method based on multiple dimensioned graticule mesh and change of fractal dimension as claimed in claim 1 is characterized in that, the method service object be earth's surface disaster monitoring mechanism, satellite earth receiving station and in orbit in satellite.
3. a kind of disaster remote sensing automatic identifying method based on multiple dimensioned graticule mesh and change of fractal dimension as claimed in claim 1 is characterized in that described method comprises the steps:
S1: take this observation image of covering region of interest as reference, extract the upper for the moment image of covering the same area of observation mutually from Image Database, consist of the image pair of geographical coupling, intercepting respectively pixel count from image centering is 2
n* 2
nThe image blocks of (general n 〉=10);
S2: image blocks is divided into 2
2i(i=0,1,2 ..., m, m≤n-7) individual pixel count is 2
N-i* 2
N-iThe sub-image piece, and calculate respectively minute dimension of each sub-image piece, and then calculate and the change of fractal dimension of each sub-image piece of recording image centering, finish the i time grid partition and change of fractal dimension calculating;
S3: repeat the S2 process until divide for the m time;
S4: with the change of fractal dimension of image antithetical phrase image blocks under the different graticule mesh yardsticks, be tied to form figure by the correspondence position pass of its sub-image piece in graticule mesh, obtain the space distribution of sub-image piece change of fractal dimension under the different graticule mesh yardsticks;
S5: the grid unit under the thinnest yardstick is as benchmark, retrieve one by one the change of fractal dimension of its higher level's grid unit, if the change of fractal dimension trend of the grid units at different levels of its correspondence consistent (having monotonicity) judges that then disaster has occured imagery zone corresponding to this grid unit;
S6: take the generation that determines the grid unit of disaster as set, according to the change of fractal dimension threshold value, identification hazard scope, and judge disaster intensity according to the change of fractal dimension size.
4. a kind of disaster remote sensing automatic identifying method based on multiple dimensioned graticule mesh and change of fractal dimension as claimed in claim 3 is characterized in that, institute is all automatically operations in steps, need not manual intervention.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107655457A (en) * | 2016-12-23 | 2018-02-02 | 航天星图科技（北京）有限公司 | A kind of Geological Hazards of debris recognition methods based on remote sensing satellite image |
| CN107730496A (en) * | 2017-10-26 | 2018-02-23 | 武汉大学 | A kind of multi-temporal remote sensing image building change detecting method based on image blocks |
| CN108346155A (en) * | 2018-02-12 | 2018-07-31 | 西北大学 | The analysis of Influential Factors system that comes down and analysis method |
| CN112461206A (en) * | 2019-09-09 | 2021-03-09 | 深圳市熠摄科技有限公司 | Landform latent transformation observation equipment |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5307292A (en) * | 1992-06-24 | 1994-04-26 | Christopher A. Brown | Method of quantifying the topographic structure of a surface |
-
2011
- 2011-09-02 CN CN2011102575298A patent/CN102967850A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5307292A (en) * | 1992-06-24 | 1994-04-26 | Christopher A. Brown | Method of quantifying the topographic structure of a surface |
Non-Patent Citations (2)
| Title |
|---|
| 杜华强等: "《北京地区温度场分布与地面空间结构关系的分形描述》", 《遥感学报》 * |
| 陈文凯等: "《地震前后遥感影像分形特征研究》", 《大地测量与地球动力学》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107655457A (en) * | 2016-12-23 | 2018-02-02 | 航天星图科技（北京）有限公司 | A kind of Geological Hazards of debris recognition methods based on remote sensing satellite image |
| CN107655457B (en) * | 2016-12-23 | 2018-09-25 | 航天星图科技（北京）有限公司 | A kind of Geological Hazards of debris recognition methods based on remote sensing satellite image |
| CN107730496A (en) * | 2017-10-26 | 2018-02-23 | 武汉大学 | A kind of multi-temporal remote sensing image building change detecting method based on image blocks |
| CN108346155A (en) * | 2018-02-12 | 2018-07-31 | 西北大学 | The analysis of Influential Factors system that comes down and analysis method |
| CN112461206A (en) * | 2019-09-09 | 2021-03-09 | 深圳市熠摄科技有限公司 | Landform latent transformation observation equipment |
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Application publication date: 20130313 |