CN101819039A - Method for analyzing and evaluating earth surface coarse graining degree by using digital image - Google Patents
Method for analyzing and evaluating earth surface coarse graining degree by using digital image Download PDFInfo
- Publication number
- CN101819039A CN101819039A CN 201010153596 CN201010153596A CN101819039A CN 101819039 A CN101819039 A CN 101819039A CN 201010153596 CN201010153596 CN 201010153596 CN 201010153596 A CN201010153596 A CN 201010153596A CN 101819039 A CN101819039 A CN 101819039A
- Authority
- CN
- China
- Prior art keywords
- image
- particle
- software
- numerical value
- face
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Landscapes
- Image Analysis (AREA)
Abstract
The invention provides a method for analyzing and evaluating an earth surface coarse graining degree by using a digital image. The method comprises the following steps of: (1) acquiring a wind-eroded earth surface digital image; (2) importing the digital image into ERDAS-IMAGE software for performing format transformation, cutting, brightness extension, the statistical analysis of image gray-scale information, the sorting processing of grain types by using a space model and the respective isolation and separation of grains in the digital image; (3) converting the digital image into an image of a vector type by using ARC-GIS software and characterizing the grains in the digital image of the vector type to form independent polygons; and (4) representing the polygons as large as the actual gains by taking the original image as a negative under the ARC-GIS software, performing regression analysis in SPSS software by taking a represented area as a dependent variable and an extracted area as an independent variable to obtain an area compensation equation of an extraction method, performing area compensation operation on the polygons as large as the characterized gains obtained by processing to obtain actual sizes of the gains so as to calculate the residual quantity of earth surface coarse grains and evaluate the wind-eroded earth surface coarse graining degree. The method has the advantages of convenient and simple operation and high measuring efficiency.
Description
Technical field
The present invention relates to a kind of closely taking pictures and obtain the numerical value image and extract wind erosion face of land coarse grain residual quantity information, analyzing and evaluating earth surface coarse degree methods, belong to soil drifting monitoring and evaluation technical field through ERDAS-IMAGE and ARC-GIS software processes by digital camera.
Background technology
Desertification is one of global environment problem, and distribution is all arranged in different continents, and the survival and development of human society are constituted a serious threat, and China is subjected to one of the most serious country of desertification harm in the world.Show that according to China's desertification for the third time and desertification situation communique (in June, 2005, the State Administration of Forestry) by 2004, the national desertification soil total area was 263.62 ten thousand km
2, account for 27.46% of territory total area, be distributed in 498 counties (flag, city) of 18 provinces (autonomous region, municipality directly under the Central Government) such as Beijing, the Inner Mongol, Xinjiang.In various forms of desertification; soil drifting is the primary form of expression of arid, semiarid and inferior moistening arid biogeographic zone desertification of land; its generation development will cause land quality to descend; plough and degenerate; the grassland deterioration; soil losses, face of land coarse and then formation are the desert view of characteristics with the drift sand, have a strong impact on local farming and animal husbandry production and socioeconomic sustainable and stable development.In addition, wind erosion also produces the aerosol particle that is suspended in a large number in the atmosphere, directly human and biological living space is polluted, and influences human health and life security.The wind erosion monitoring and evaluation is meant to be monitored and estimates the coverage and the action intensity of soil drifting, can illustrate the wind erosion origin cause of formation, simulation wind erosion process, estimation wind erosion development speed and degree by deeply analyzing the monitoring and evaluation result, for the present situation of grasping desertification and sandy land and dynamic change trend, formulation control wind erosion macro-level policy-making provide scientific basis.It is the primary basis and the prerequisite of carrying out desertification watch and evaluation that wind erosion face of land coarse process is grasped in research; scientific evaluation coarse degree can accurately be judged the situation of degree of wind erosion, potential wind erosion quantity, weather-proof ability, and then takes science, effective, time saving and energy saving prophylactico-therapeutic measures each stage at wind erosion.In real work, can accurately hold the ratio and the Restzustand of face of land coarse grain, be the most important condition of local degree of wind erosion of science judgment and potential erosion ability.Simultaneously, concern between the content of face of land larger particles residues such as research face of land aerodynamic roughness and surface skining, gravel, soil block, the grade, especially the relation between face of land aerodynamic roughness and the above-mentioned face of land residue coverage has important scientific meaning in the practice of defending and controlling sand.The face of land coarse degree evaluation of in the past eroding mainly is to determine the content of face of land coarse grain by direct sample screening measurement method, and then calculates wind erosion quantity, erosion intensity and potential erosion ability.Continuous progress along with means of testing; terrain is measured and progressively is applied to this field in conjunction with advanced method of testings such as wind tunnel experiments; for example the particle to size fractionated dyes; after certain wind speed deflation, photo-optics is carried out on the surface; by calculating the residual quantity that each grade particle is gone up on wind erosion surface in the image, the erode monitoring and evaluation of coarse degree of the variation of estimation wind erosion quantity.But all there is the original ground surface state that destroys in said method; repeatable poor; more loaded down with trivial details and the restriction instrument and equipment inconvenience that is subjected to electric power and environmental baseline of mensuration process is in defectives such as terrain uses; simultaneously; the wind erosion coarse actual sampling in surface soil top layer thickness is the major limitation sex factor of the face of land, left and right sides coarse grain residual quantity evaluation result; but be difficult to accurately record face of land wind erosion coarse layer thickness at the experiment terrain at present; therefore; caused present soil drifting monitoring and evaluation method precision low; error is big; the present situation that wastes time and energy; limited by instrument and equipment and means of testing; the practicality of above-mentioned evaluation method is lower; particularly in the arid that does not possess laboratory condition; the semiarid zone, the concrete mensuration work of monitoring and evaluation many times is difficult to carry out and finishes.
In order to overcome above-mentioned defective, easy to carry at the terrain instrument and equipment, the mensuration process is fairly simple, adopt repeatable good noncontact mode and do not destroy the original ground surface state, be not subjected to the experimental situation condition restriction simultaneously can demand exploitation urgently at the normal assay method of implementing of terrain.
Summary of the invention
Based on above consideration; the invention provides a kind of numerical value video recording analysis evaluating earth surface coarse degree methods of utilizing; take the noncontact mode just, gathered the numerical value image on the wind erosion coarse face of land also by follow-up ERDAS-IMAGE and the analysis of ARC-GIS software processes quickly; can extract the information such as content, grade of larger particles face of land residues such as face of land coarse grain residual quantity and surface skining, gravel, soil block comparatively accurately and quickly, be present soil drifting monitoring and evaluation method relatively easily.
The numerical value video recording analysis evaluating earth surface coarse degree methods of utilizing provided by the invention may further comprise the steps:
(1) according to the erode collection of face of land numerical value image of the selected drainage pattern that satisfies following four conditions of monitoring and evaluation target call: the resolution of image can reach the requirement of distinguishing target particles; Camera is fixed on the tripod, and camera lens is perpendicular to collection face; On collection face, place rule; Cover direct sunlight, purpose is to eliminate the interference of shade;
(2) the numerical value image that step (1) is gathered, import ERDAS-IMAGE software, carry out format conversion, cutting, brightness stretching, image greyscale Information Statistics analysis, adopt spatial model to carry out particle classification processing, isolated separately the making a distinction of the particle in the numerical value image;
The numerical value image of the lattice types after (3) utilization ARC-GIS software is handled step (2) is converted into the image of vector type, and particle is characterized as being isolated one by one polygon in the numerical value image of vector type;
(4) under ARC-GIS software, be the polygon that negative is described particle true form size with the original image; to describe area as dependent variable; extract area and in SPSS software, carry out regretional analysis as independent variable; obtain the area compensation equation of extracting method; use the polygon of the characterizing particles size that this equation obtains processing to carry out the area compensation operation; calculate the particle actual size, and then obtain face of land coarse grain residual quantity, the evaluation of the face of land coarse degree of eroding.
Preferably, the quantity and the geometric size information of all face of land things that comprise particle and skinning of the correct identification of the wind erosion face of land numerical value image energy of gathering in the step (1) and detection wind erosion face of land diameter 〉=0.1mm.
Preferably, processing procedure in step (2) and (3) and method are carried out according to ERDAS-IMAGE software and ARC-GIS software operation rules respectively.
Preferably, in the step (2), the numerical value image format transforms and to be meant and to adopt ERDAS-IMAGE software input/output function the image of the JPG form that collects to be converted into the IMG image mode of ERDAS-IMAGE software default.
Preferably, in the step (2), the image cutting is by the regular clipping function of ERDAS-IMAGE software the frame of former image edge 5cm to be removed, and purpose is in order to eliminate the influence of scale to the further analyzing and processing of image.
Preferably, in the step (2), the concrete way that brightness stretches is to set up brightness stretching spatial model to handle under ERDAS-IMAGE software, earlier former image is split as three single band images, take the stretching rule of X '=X*255/Max (X) to stretch respectively to each wave band image, the back single band image that will stretch again is combined into triband numerical value image.The purpose that brightness stretches is the unified unification of being convenient to the spatial classification model to same brightness of same type wind erosion face of land numerical value image that different time is taken.
Preferably; in the step (2); image greyscale Information Statistics analysis is meant at first carries out the particle classification according to the color and the brightness of particle; thereafter adopt ERDAS-IMAGE software cursor query function to inquire about dissimilar particle surface gray value informations, statistical study and then understand and grasp dissimilar particle grey value characteristics in the image.
Preferably, in the step (2), particle classification processing is meant sets up the classification processing that the classifying space model carries out particle under ERDAS-IMAGE software, make after the processing in the image that each particle is isolated to come, classifying rules is at also obtaining through constantly handling more progressively to improve that every type of particle is formulated on above-mentioned image greyscale Information Statistics analysis foundation, in arid, the semiarid region granulomere, the face of land of will eroding is divided into five classes: reflect strong particle, in the reflection and be yellow particle, in the reflection but be not yellow particle, reflection is weak but be not that blue particle and reflection are weak and be blue particle, and Dui Ying classifying rules is successively: not[(R-B)＞=and C
4] and (R+G+B)＞=C
1, (R+G+B)＞=C
3And (R-B)＞=C
4, not[(R-B)＞=C
4] and (R+G+B)＞=C
2And (R+G+B)＜C
1, not[(R-B)＞=C
4] and (R+G+B)＞=C
3And (R+G+B)＜C
2Or{ (R+G+B)＜C
3And R＞=B＞=G} and (R+G+B)＜C
3And B＞=G＞=R, this not, and and or operational symbol of being Boolean calculation wherein; C
1, C
2, C
3, C
4Be constant and the C between the 0-765
1＞C
2＞C
3＞C
4Above-mentioned classifying rules more clearly is illustrated in as in the following table 1:
The classifying rules of table 1 arid, semiarid region
The grain type processing rule
Reflect strong not[(R-B)＞=C
4] and (R+G+B)＞=C
1
In the reflection and be yellow
(R+G+B)＞＝C
3and(R-B)＞＝C
4
Look
In the reflection but be not
not[(R-B)＞＝C
4]and(R+G+B)＞＝C
2and(R+G+B)＜C
1
Yellow
A little less than the reflection not[(R-B)＞=C
4] and (R+G+B)＞=C
3And (R+G+B)＜C
2}
But not blue or{ (R+G+B)＜C
3And R＞=B＞=G}
Reflection is weak and be prison
(R+G+B)＜C
3and?B＞＝G＞＝R
Look
Preferably, in the step (3), the polygonal one by one area of characterizing particles size and use output function that attribute list is output as the EXCEL form after the utilization ARC-GIS software automatic computing.
Preferably, in the step (4), be that negative can not also there is no need to describe all particles because of numbers of particles is numerous when describing the polygon of particle true form with the original image, the particle that only needs the rendering image line of symmetry to pass through, this method of sampling broad covered area, representative strong and restriction can not be omitted the bad particle of extraction effect.
The described numerical value video recording analysis evaluating earth surface coarse degree methods of utilizing has following advantage with respect to prior art:
1, the present invention has adopted closely noncontact shooting technology collection face of land numerical value image; extract the coarse grain residual quantity information face of land coarse monitoring and evaluation of eroding through follow-up ERDAS-IMAGE software and ARC-GIS software image analyzing and processing; face of land wind erosion coarse layer thickness causes the representativeness of sample lower, the coarse problem of monitoring and evaluation result because of can not accurately measuring when terrain is taken a sample to have avoided classic method; therefore science is credible more, and obtained monitoring and evaluation achievement can provide scientific basis and necessary reference for the control of soil drifting conscientiously.
2, not disturbance of the present invention destroys face of land virgin state, the monitoring and evaluation favorable repeatability, and environmental protection more can be as classic method need not cause unnecessary destruction to environment because of what excavate section, take soil sample.
3, the present invention can and be covered the equipment such as curtain that direct sunlight uses by digital camera, tripod, scale and implemented, break through soil drifting monitoring and evaluation classic method and relied on large-scale instrument and equipments such as wind-tunnel, sieve shaker, be subjected to the restriction of experimental situation condition, need be transported to a large amount of samples from terrain the bottleneck of cumbersome procedure such as experimental determination; The instrument and equipment highly versatile that the present invention uses, it is easy to carry operation, and the terrain determination efficiency is higher, and apply especially suit arid, semiarid zone harsh in environmental baseline and that lack laboratory condition.
Description of drawings
Fig. 1 is a kind of former figure in surface, sand dune in the numerical value video recording analysis evaluating earth surface coarse degree methods Application Example 1 that utilizes of the present invention.
Fig. 2 is a kind of the utilization in the numerical value video recording analysis evaluating earth surface coarse degree methods Application Example 1 through ERDAS-IMAGE software of the present invention, carries out format conversion, brightness stretching, image greyscale Information Statistics and analyzes, adopts spatial model to carry out the particle classification to handle the back image.
Fig. 3 is converted into the image of vector type for a kind of numerical value image that utilizes in the numerical value video recording analysis evaluating earth surface coarse degree methods Application Example 1 lattice types after the utilization ARC-GIS software processes of the present invention.
Fig. 4 is the image that negative is described the particle true form that the image line of symmetry passes through for a kind of the utilization in the numerical value video recording analysis evaluating earth surface coarse degree methods Application Example 1 under ARC-GIS software with the original image of the present invention.
Fig. 5 is a kind of former figure in surface, meadow that influenced for economic activity by the people that utilizes of the present invention.
Fig. 6 is a kind of the utilization in the numerical value video recording analysis evaluating earth surface coarse degree methods Application Example 2 through ERDAS-IMAGE software of the present invention, carries out format conversion, brightness stretching, image greyscale Information Statistics and analyzes, adopts spatial model to carry out the particle classification to handle the back image.
Fig. 7 is converted into the image of vector type for a kind of numerical value image that utilizes in the numerical value video recording analysis evaluating earth surface coarse degree methods Application Example 2 lattice types after the utilization ARC-GIS software processes of the present invention.
Fig. 8 is the image that negative is described the particle true form that the image line of symmetry passes through for a kind of the utilization in the numerical value video recording analysis evaluating earth surface coarse degree methods Application Example 2 under ARC-GIS software with the original image of the present invention.
Need to prove that Fig. 1～8 all are to have amplified in the middle of the image of 150 times and intercepting locally on the basis of image actual size, purpose is for the clear trickle shape facility of particle that represents.
Embodiment
Concrete Application Example is as follows:
The image collection device all selects to use fuselage to be CanonEos5D-Mark II among the embodiment 1 and 2, camera lens is the digital camera of CanonEF24-105mm f/4L IS USM, camera pixel is 5616 * 3,744 21 026 304 pixels, lens focus 24-105mm, the particle grade size requirements that need distinguish interpretation according to the monitoring and evaluation purpose filters out best screening-mode for taking area 330 * 220mm, shooting height 74cm.The numerical value image data resolution that obtains under this condition is 289.62 pixels/mm
2, maximum geometry deformation rate is 6.98%, and presents the rule that increases gradually around the mind-set therefrom, has reached the accuracy requirement of coarse grain residual quantity information extraction in the face of land in the soil drifting monitoring and evaluation.
Embodiment 1: be positioned at 38 ° 59 ' 08 of N in the geographic position "; 109 ° 08 ' 49 of E ", height above sea level 1310m, administrative division is in domestic test site, Mu Us Shadi research centre, Tu Ke town, Wushen Banner, Ordos City, the Inner Mongol, the scene is stepped on to examine and has been chosen a typical crescentic barchan as research object, gathered the respectively image of 12 patches of windward slope, leeward slope along the main air direction, 10m pushes up arrangement from the end, slope to the slope at interval successively.Fig. 1 is windward slope the 3rd patch sand dune former figure in the face of land, and filming image is about 15 o'clock on the 14th October in 2009, and is fine, and thin cloud is arranged, and fully automatic mode is taken; Use ERDAS-IMAGE software to carry out image format conversion, brightness stretching, image greyscale Information Statistics analysis, adopt spatial model to carry out grain type classification processing, make isolated separately the making a distinction of particle in the numerical value image, as Fig. 2; The numerical value image of the lattice types after ARC-GIS software will be classified processing is converted into the image of vector type, and particle is characterized as being isolated one by one polygon in the numerical value image of vector type, as Fig. 3; Under ARC-GIS software, be the polygon that negative is described particle true form size with the original image, as Fig. 4, to describe area also is that the particle real area is as dependent variable, extract area and in SPSS software, carry out regretional analysis as independent variable, in the type, the area of setting up husky surface type as basic data with the image of first, second two patch of windward slope compensates equation, and the image data of patch 3 is through carrying out the back checking of processing accuracy behind the compensation operation, and the basic effect of extraction sees Table 2.
Table 2 has reflected the extraction effect of this method in the present embodiment: the quantity of particle is not all lost, and has extracted 100%; The phenomenon that particle is interconnected is fewer, be communicated with quantity and area all below 2%, mostly the part that is interconnected is in original image that two same color particulate fractions are overlapping and causes, present disaggregated model is bad to the differentiation effect of this class situation, but the amount of this phenomenon is seldom, and is little to the influence of overall process precision; What extracting method lost mainly is the area (about 50%) of particle, and the loss of area is the sacrifice of making in order to guarantee the complete of amounts of particles, has also confirmed the importance of setting up area compensation equation.
In SPSS software, carry out linear regression analysis; set up area compensation equation; linear analysis the results are shown in Table 3; the linear regression analysis result shows; particle area and the real area of utilizing numerical value video recording analysis evaluating earth surface coarse degree methods to extract under 0.05 level of signifiance are linear, can set up equation of linear regression.
The particle area compensation equation of setting up in the present embodiment is:
S′＝1.083×S+0.524(1)
S ' in the formula be behind the compensation operation grains of sand area also promptly this method extract the final area of particle that obtains, S is the first area that obtains that extracts.
Patch 3 back verification msgs are 100.13% through the withdrawal ratio that area compensates after equation (1) calculates readjustment, whole extraction area with describe the area difference little and with patch 3 describe carry out paired sample T check after area and compensation back area match one by one, analysis result sees Table 4, under 0.05 level of signifiance, two groups of data pairings are reliable, do not have significant difference.Therefore, it is higher to have illustrated more all that from the withdrawal ratio of integral body or pairing average this method is extracted precision, and error is little, and it is feasible being applied in the wind erosion face of land coarse information extraction of sand dune.
Embodiment 2: selecting by strong people is that the grass-land deterioration district that the economic activity interference aggravates is the Experimental Area, the geographic position is positioned at 41 ° 21 ' 06 of N "; 109 ° 08 ' 15 of E ", height above sea level 1313m, administrative division reaches Mao Qi and calls the domestic Xi La in river town together and herd in the benevolence grassland tour holiday village in Baotou, the Inner Mongol.The yurt post that the selected deterioration of grasslands that typically causes because of artificial travel in holiday economic development activities, a face of land expose in the Experimental Area is as research object, along the main air direction and by the center, post is that article one is gathered line, the interval is that next bar is gathered line for 45 ° successively, interval 10m lays 15 from the radiation to the periphery of center, post and gathers patches on the every collection line, has gathered 8 numerical value images of gathering 120 patches of lines altogether.Fig. 5 is that filming image is about 11 o'clock on the 27th September in 2009, and is fine, and thin cloud is arranged along the main air direction first patch wind erosion former figure in the face of land, and fully automatic mode is taken; Use ERDAS-IMAGE software to carry out image format conversion, brightness stretching, image greyscale Information Statistics analysis, adopt spatial model to carry out grain type classification processing, make isolated separately the making a distinction of particle in the numerical value image, as Fig. 6; The numerical value image of the lattice types after ARC-GIS software will be classified processing is converted into the image of vector type, and particle is characterized as being isolated one by one polygon in the numerical value image of vector type, as Fig. 7; Under ARC-GIS software, be the polygon that negative is described particle true form size with the original image, as Fig. 8, to describe area also is that the particle real area is as dependent variable, extract area and in SPSS software, carry out regretional analysis as independent variable, in the type, with along the image of main air direction first patch area compensation equation as basic data grassland establishment type, the image data of patch 2 is through carrying out the back checking of processing accuracy behind the compensation operation, and the basic effect of extraction sees Table 2.
Table 2 has reflected the extraction effect of this method in the present embodiment: the quantity of particle is not all lost, and has extracted 100%; The phenomenon that particle is interconnected is fewer, be communicated with quantity and area all below 2%, mostly the part that is interconnected is in original image that two same color particulate fractions are overlapping and causes, present disaggregated model is bad to the differentiation effect of this class situation, but the amount of this phenomenon is seldom, and is little to the influence of overall process precision; What extracting method lost mainly is the area (about 50%) of particle, and the loss of area is the sacrifice of making in order to guarantee the complete of amounts of particles, has also confirmed the importance of setting up area compensation equation.
Carry out linear regression analysis in SPSS software, set up area compensation equation, linear analysis the results are shown in Table 3.
The linear regression analysis result shows that particle area and the real area of utilizing numerical value video recording analysis evaluating earth surface coarse degree methods to extract are linear, can set up equation of linear regression under 0.05 level of signifiance.
The particle area compensation equation of setting up in the present embodiment is:
S′＝1.605×S+0.302(2)
S ' in the formula be behind the compensation operation grains of sand area also promptly this method extract the final area of particle that obtains, S is the first area that obtains that extracts.
Patch 2 back verification msgs are 104.58% through the withdrawal ratio that area compensates after equation (2) calculates readjustment, whole extraction area with describe the area difference little and with patch 2 describe carry out paired sample T check after area and compensation back area match one by one, analysis result sees Table 4, under 0.05 level of signifiance, two groups of data pairings are reliable, do not have significant difference.Therefore, it is higher to have illustrated more all that from the withdrawal ratio of integral body or pairing average this method is extracted precision, and error is little, and it is feasible being applied in the information extraction of meadow wind erosion coarse.
The basic effect that table 2 extracts
Table 3 linear regression analysis result
Table 4 paired sample T assay
Claims (10)
1. one kind is utilized numerical value video recording analysis evaluating earth surface coarse degree methods, it is characterized in that, may further comprise the steps:
(1) according to the erode collection of face of land numerical value image of the selected drainage pattern that satisfies following four conditions of monitoring and evaluation target call: the resolution of image can reach the requirement of distinguishing target particles; Camera is fixed on the tripod, and camera lens is perpendicular to collection face; On collection face, place rule; Cover direct sunlight;
(2) the numerical value image that step (1) is gathered, import ERDAS-IMAGE software, carry out format conversion, cutting, brightness stretching, image greyscale Information Statistics analysis, adopt spatial model to carry out particle classification processing, isolated separately the making a distinction of the particle in the numerical value image;
The numerical value image of the lattice types after (3) utilization ARC-GIS software is handled step (2) is converted into the image of vector type, and particle is characterized as being isolated one by one polygon in the numerical value image of vector type;
(4) under ARC-GIS software, be the polygon that negative is described particle true form size with the original image; to describe area as dependent variable; extract area and in SPSS software, carry out regretional analysis as independent variable; obtain the area compensation equation of extracting method; use the polygon of the characterizing particles size that this equation obtains processing to carry out the area compensation operation; calculate the particle actual size, and then obtain face of land coarse grain residual quantity, the evaluation of the face of land coarse degree of eroding.
2. method according to claim 1 is characterized in that, the quantity and the geometric size information of all face of land things that comprise particle and skinning of the correct identification of the wind erosion face of land numerical value image energy of gathering in the step (1) and detection wind erosion face of land diameter 〉=0.1mm.
3. method according to claim 1 is characterized in that, processing procedure in step (2) and (3) and method are carried out according to ERDAS-IMAGE software and ARC-GIS software operation rules respectively.
4. method according to claim 1, it is characterized in that, in the step (2), the numerical value image format transforms and to be meant and to adopt ERDAS-IMAGE software input/output function the image of the JPG form that collects to be converted into the IMG image mode of ERDAS-IMAGE software default.
5. method according to claim 1 is characterized in that, in the step (2), the image cutting is by the regular clipping function of ERDAS-IMAGE software the frame of former image edge 5cm to be removed.
6. method according to claim 1, it is characterized in that, in the step (2), the concrete way that brightness stretches is to set up brightness stretching spatial model to handle under ERDAS-IMAGE software, earlier former image is split as three single band images, take the stretching rule of X '=X*255/Max (X) to stretch respectively to each wave band image, the back single band image that will stretch again is combined into triband numerical value image.
7. method according to claim 1; it is characterized in that; in the step (2); image greyscale Information Statistics analysis is meant at first carries out the particle classification according to the color and the brightness of particle; thereafter adopt ERDAS-IMAGE software cursor query function to inquire about dissimilar particle surface gray value informations, statistical study and then understand and grasp dissimilar particle grey value characteristics in the image.
8. method according to claim 1, it is characterized in that, in the step (2), particle classification processing is meant sets up the classification processing that the classifying space model carries out particle under ERDAS-IMAGE software, make after the processing in the image that each particle is isolated to come, classifying rules is at also obtaining through constantly handling more progressively to improve that every type of particle is formulated on above-mentioned image greyscale Information Statistics analysis foundation, in arid, the semiarid region granulomere, the face of land of will eroding is divided into five classes: reflect strong particle, in the reflection and be yellow particle, in the reflection but be not yellow particle, reflection is weak but be not that blue particle and reflection are weak and be blue particle, and Dui Ying classifying rules is successively: not[(R-B)＞=and C
4] and (R+G+B)＞=C
1, (R+G+B)＞=C
3And (R-B)＞=C
4, not[(R-B)＞=C
4] and (R+G+B)＞=C
2And (R+G+B)＜C
1, not[(R-B)＞=C
4] and (R+G+B)＞=C
3And (R+G+B)＜C
2Or{ (R+G+B)＜C
3And R＞=B＞=G} and (R+G+B)＜C
3AndB＞=G＞=R, this not, and and or operational symbol of being Boolean calculation wherein; C
1, C
2, C
3, C
4Be constant and the C between the 0-765
1＞C
2＞C
3＞C
4
9. method according to claim 1 is characterized in that, in the step (3), and the polygonal one by one area of characterizing particles size and use output function that attribute list is output as the EXCEL form after the utilization ARC-GIS software automatic computing.
10. method according to claim 1 is characterized in that, in the step (4), is that negative is described the polygon of particle true form and only described the particle that the image line of symmetry passes through with former numerical value image.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010101535960A CN101819039B (en) | 2010-04-19 | 2010-04-19 | Method for analyzing and evaluating earth surface coarse graining degree by using digital image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN2010101535960A CN101819039B (en) | 2010-04-19 | 2010-04-19 | Method for analyzing and evaluating earth surface coarse graining degree by using digital image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN101819039A true CN101819039A (en) | 2010-09-01 |
| CN101819039B CN101819039B (en) | 2012-02-29 |
Family
ID=42654225
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN2010101535960A Active CN101819039B (en) | 2010-04-19 | 2010-04-19 | Method for analyzing and evaluating earth surface coarse graining degree by using digital image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN101819039B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103454063A (en) * | 2013-09-18 | 2013-12-18 | 河北省科学院地理科学研究所 | Farmland soil year wind erosion amount estimation method |
| CN113177911A (en) * | 2021-04-13 | 2021-07-27 | 沈阳大学 | Method for nondestructive evaluation of ozone sensitivity of plants by leaves |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1290851A (en) * | 2000-11-03 | 2001-04-11 | 华南理工大学 | Method for measuring and evaluating depth of road surface construction by digital image technology |
| CN1696654A (en) * | 2005-06-06 | 2005-11-16 | 清华大学 | Method for measuring grain size distribution of granules |
| RU2008114286A (en) * | 2008-04-15 | 2009-10-20 | Институт Проблем Комплексного Освоения Недр Российской Академии Наук (Ипкон Ран) (Ru) | METHOD FOR DETERMINING THE GRAN COMPOSITION OF CRACED BREED IN CAREERS |
-
2010
- 2010-04-19 CN CN2010101535960A patent/CN101819039B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1290851A (en) * | 2000-11-03 | 2001-04-11 | 华南理工大学 | Method for measuring and evaluating depth of road surface construction by digital image technology |
| CN1696654A (en) * | 2005-06-06 | 2005-11-16 | 清华大学 | Method for measuring grain size distribution of granules |
| RU2008114286A (en) * | 2008-04-15 | 2009-10-20 | Институт Проблем Комплексного Освоения Недр Российской Академии Наук (Ипкон Ран) (Ru) | METHOD FOR DETERMINING THE GRAN COMPOSITION OF CRACED BREED IN CAREERS |
Non-Patent Citations (2)
| Title |
|---|
| 《材料导报》 20081031 蓝章礼等 基于数字显微图像处理的水泥粒度分析 88-90 第22 卷, 第10 期 2 * |
| 《矿山机械》 20091231 张学军等 基于计算机视觉的铁矿石粉微粒尺寸的检测 14-16 第37卷, 第20期 2 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103454063A (en) * | 2013-09-18 | 2013-12-18 | 河北省科学院地理科学研究所 | Farmland soil year wind erosion amount estimation method |
| CN103454063B (en) * | 2013-09-18 | 2015-08-19 | 河北省科学院地理科学研究所 | Farmland soil year wind erosion amount estimation method |
| CN113177911A (en) * | 2021-04-13 | 2021-07-27 | 沈阳大学 | Method for nondestructive evaluation of ozone sensitivity of plants by leaves |
Also Published As
| Publication number | Publication date |
|---|---|
| CN101819039B (en) | 2012-02-29 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Chen et al. | Extraction of glacial lake outlines in Tibet Plateau using Landsat 8 imagery and Google Earth Engine | |
| CN109919875B (en) | High-time-frequency remote sensing image feature-assisted residential area extraction and classification method | |
| CN106127725B (en) | A kind of millimetre-wave radar cloud atlas dividing method based on multiresolution CNN | |
| CN104881865A (en) | Forest disease and pest monitoring and early warning method and system based on unmanned plane image analysis | |
| CN109344810A (en) | A kind of arable land use change monitoring method and system based on high score satellite remote sensing date | |
| Bechtel et al. | CENSUS of cities: LCZ classification of cities (level 0)–workflow and initial results from various cities | |
| Pesaresi et al. | The global human settlement layer from landsat imagery | |
| Cheng et al. | Multi-model solar irradiance prediction based on automatic cloud classification | |
| CN112861732B (en) | Method, system and device for monitoring land in ecological environment fragile area | |
| CN109784769B (en) | Agricultural non-point source pollution risk identification method | |
| CN104517126A (en) | Air quality assessment method based on image analysis | |
| Chunyang et al. | Sea fog detection using U-Net deep learning model based on MODIS data | |
| Xu et al. | Validation of MODIS-based monitoring for a green tide in the Yellow Sea with the aid of unmanned aerial vehicle | |
| Roncoroni et al. | Centimeter-scale mapping of phototrophic biofilms in glacial forefields using visible band ratios and UAV imagery | |
| CN101819039B (en) | Method for analyzing and evaluating earth surface coarse graining degree by using digital image | |
| Jaya et al. | Using Support Vector Machine To Identify Land Cover Change During Covid-19 Pandemic In Komodo National Park, Indonesia | |
| CN103336972A (en) | Foundation cloud picture classification method based on completion local three value model | |
| CN113158770A (en) | Improved mining area change detection method of full convolution twin neural network | |
| Mishra et al. | Application of classification techniques for identification of water region in multiple sources using Landsat-8 OLI imagery | |
| Xu et al. | Issues and challenges of remote sensing-based local climate zone mapping for high-density cities | |
| CN103605988A (en) | Foundation cloud atlas classification method based on spatial pyramid random mapping | |
| CN116503750A (en) | Large-range remote sensing image rural block type residential area extraction method and system integrating target detection and visual attention mechanisms | |
| Manikandan | Spatial and temporal dynamics of urban sprawl Using multi-temporal images and relative Shannon entropy model in Adama, Ethiopia | |
| CN105574551B (en) | A kind of ground cloud atlas automatic classification method based on group schema | |
| Gudelj et al. | Accuracy analysis of the inland waters detection |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |