CN104408770A - Method for modeling cumulus cloud scene based on Landsat8 satellite image - Google Patents
Method for modeling cumulus cloud scene based on Landsat8 satellite image Download PDFInfo
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Abstract
The invention discloses a method for modeling the cumulus cloud scene based on a Landsat8 satellite image, belongs to the field of computer graphics, and can automatically construct the geometrical shape and the extinction coefficient of a cumulus cloud from the Landsat8 satellite image. The method comprises the steps of firstly, differentiating image pixels into two types which are cumulus cloud and earth surface through the difference of cloud and earth surface in an visible image and a long-wave infrared image, secondly, computing the height of cloud top through the linear relation between the temperature and the height of the cumulus cloud, further estimating the height of cloud base, then computing the optical thickness of the cloud through a bi-directional reflectance function, further computing the extinction coefficient of the cloud, and finally optimizing the shape of the cloud, performing sampling at the interior of the cloud, and forming a particle model of the cloud and performing drawing. The method has the advantages that a remote sensing theory is introduced to guide the construction of the cumulus cloud scene, the facticity and the fidelity of the cumulus cloud scene can be effectively improved, the modeling process is completely automatic, and the efficiency is high.
Description
Technical field
The invention belongs to field of Computer Graphics, special cloud modeling field, be specifically related to a kind of method based on Landsat8 satellite image modeling cumulus scene.
Background technology
Cloud is as a kind of common spontaneous phenomenon, and its shape is ever-changing, and the process being formed, develop and dissipate is extremely complicated, and the modeling of cloud is challenging work always.Classical cloud modeling method is divided into two classes substantially, i.e. the method for Kernel-based methods and the method for physically based deformation emulation.The former mainly utilizes the shape of the means such as fractal theory and Noise texture modeling cloud, can build cloud true to nature, but modeling process relies on parameter adjustment frequently.Even avoid setting parameter to reduce, researchist starts the method for attempting physically based deformation emulation, and the fluid motion equation namely simplified by simulation emulates the generative process of cloud.The method of physically based deformation can modeling time domain continuous print cloud by setting initial boundary conditions.But, because initial boundary conditions becomes nonlinearity relation with final cloud shape, expect that building satisfied cloud shape still needs repeatedly to adjust parameter.Therefore, the side of these two classes classics is not suitable for building and comprises the hundreds of piece even cloud scene of thousands of, and is mainly used in building single cloudlet or small-scale scene.
Compared to the cloud modeling method of classics, the multiple dimensioned and authenticity that the method for data-driven contains due to data, becomes a study hotspot gradually.The data relevant with cloud modeling are divided three classes substantially, i.e. natural image, satellite cloud picture and numerical simulation data.Utilizing in natural image modeling cloud, the people such as Dobashi are first from the polytype cloud of single image modeling.Subsequently, the people such as Yuan propose a single scattering model simplified, and by the 3D shape of the Converse solved cumulus of this model.Different from natural image, satellite cloud picture and numerical simulation data are usually used to the cloud system building large scale.For satellite cloud picture, the hurricane of the modeling large scale that first people such as Dobashi utilize to simplify.In recent years, the people such as Yuan propose to utilize remote sensing theory from low-resolution satellite imagery, estimate cloud parameter, thus build the 3D shape of large scale cloud system.But their method is not suitable for the modeling of cumulus.Be because low-resolution satellite imagery is difficult to record the information of cumulus on the one hand, be because the method depends on setting parameter on the other hand, cause the error of geometric thickness excessive, even exceeded the typical sizes of cumulus.
In February, 2013, provide high-resolution satellite image the Landsat8 jointly launched by US Geological Survey and US National Aeronautics and Space Administration, relate to 11 wave bands from visible ray to LONG WAVE INFRARED, spatial resolution is from 100 meters to 15 meters.These high-resolution satellite images are that the structure of cumulus scene provides possibility.
Summary of the invention
The technical problem to be solved in the present invention is: overcome the deficiencies in the prior art, provides a kind of cumulus scene modeling method based on Landsat8 satellite image, from visible images and LONG WAVE INFRARED image, can build geometric configuration and the extinction coefficient of cumulus.On this basis, optimize the shape of cloud, form the particle model of cloud and draw.Experiment shows, the method that the present invention proposes can build cumulus scene true to nature quickly and automatically from satellite image, improves modeling efficiency.
The technical scheme that the present invention solves above-mentioned technical matters employing is: a kind of method based on Landsat8 satellite image modeling cumulus scene, and performing step is as follows:
Step (1), cloud ground is separated, and utilizes cloud and the feature of earth's surface pixel in different-waveband image, in conjunction with threshold method, image pixel is divided into cumulus pixel and earth's surface pixel, and utilizes edge detection operator, extracts the cumulus region be communicated with;
Step (2), shape are estimated, utilize the surface temperature in the territory, local homogeneity calculated product cloud sector of temperature, utilize the temperature of LONG WAVE INFRARED image, surface temperature and Lapse rate of air temperature to estimate cloud-top height.On the basis supposing level at the bottom of cumulus, using the mean value of the cloud-top height of cumulus zone boundary pixel as the height of cloud base;
Step (3), extinction coefficient calculate, and utilize the albedo of visible images, and the bidirectional reflectance function in conjunction with visible light wave range calculates the optical thickness of cloud.The cloud-top height utilizing step (2) to obtain and the height of cloud base calculate the geometric thickness of cloud, and then on the basis of hypothesis cumulus Vertical Uniform, utilize optical thickness and geometric thickness to estimate the extinction coefficient of cumulus;
Step (4), particle sampler and drafting, the cloud-top height utilizing step (2) to obtain and the height of cloud base build the initial distance field of cloud, and form initial extinction coefficient body.Disturbance initial distance field, forms the shape details of cloud, and upgrades extinction coefficient body.Utilize extinction coefficient body to sample in the inside of cloud, form the particle model of cloud, and adopt the repeatedly particle model of forward scattering model to cumulus to draw.The modeling result that final realization is similar to input satellite image.
Further, the particular content of described step (1) medium cloud ground separation is as follows:
Step (A1), compared with earth's surface, the temperature of cloud is lower, and brightness is higher, and therefore the temperature value of LONG WAVE INFRARED image is less, and the albedo value of visible images is larger.Utilize cloud and the difference of earth's surface pixel in temperature and albedo, by setting threshold value, image pixel is divided into cloud pixel and earth's surface pixel;
Step (A2), for cloud pixel, utilize Image Edge-Detection operator, obtain the region of some connections, each connected region is considered as a cumulus.
Further, the step that in described step (2), shape is estimated is specific as follows:
Step (B1), first, utilize the surface temperature in the territory, local homogeneity calculated product cloud sector of temperature; Secondly, the temperature of LONG WAVE INFRARED image and the difference of surface temperature and Lapse rate of air temperature is utilized to estimate cloud-top height;
Step (B2), suppose level at the bottom of cumulus, calculate the mean value of the cloud-top height of current cumulus zone boundary pixel, and it can be used as the height of cloud base of cumulus.
Further, the concrete steps that in described step (3), extinction coefficient calculates are as follows:
Step (C1), the bidirectional reflectance function of the albedo of visible images and visible light wave range is utilized to solve the optical thickness of cloud;
The difference of step (C2), the cloud-top height utilizing step (2) to obtain and the height of cloud base calculates the geometric thickness of cloud; Suppose that extinction coefficient is evenly distributed in vertical direction, and using the extinction coefficient of the ratio of optical thickness and geometric thickness as cumulus.
Further, in described step (4), the concrete steps of particle sampler and drafting are as follows:
Step (D1), the cloud-top height utilizing step (2) to obtain and the height of cloud base build the initial distance field of cloud, and form initial extinction coefficient body;
Step (D2), by adding first ball on the surface of cloud, the distance field of disturbance cloud, forms the shape details of cloud, and utilizes arest neighbors interpolation algorithm to upgrade extinction coefficient body;
Step (D3), be that positive grid node place produces cloud particle at extinction coefficient, wherein, the radius of particle is proportional to mesh spacing, and the center of particle is obtained by the position of disturbance grid node, and the extinction coefficient of particle equals the extinction coefficient of this node.Finally, the repeatedly particle model of forward scattering model to cumulus is adopted to draw.
The present invention's advantage is compared with prior art:
The present invention with high-resolution satellite cloud atlas for input, computer picture is learned a skill and remote sensing theory organically blend, realize the cumulus scene modeling based on visible images and LONG WAVE INFRARED image.Compared with modeling method before, method of the present invention is a kind of method of What You See Is What You Get, and realize simple, completely automatically, modeling efficiency is high, is suitable for the structure of large-scale virtual scene.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of cumulus scene modeling of the present invention;
Fig. 2 is the result figure that is separated of cloud of the present invention ground, and wherein scheming (a) is visible images, and figure (b) is segmentation result, and white is cloud, and black is earth's surface;
Fig. 3 is the result figure of particle sampler of the present invention, and wherein scheming (a) is initial surface, and figure (b) is the surface after optimizing, the drafting design sketch that figure (c) is particle sampler;
Fig. 4 is drafting design sketch of the present invention, wherein schemes (a) vertical view, and figure (b) is plan view, and figure (c) is that cloud at dusk draws effect.
Embodiment
Below in conjunction with accompanying drawing and example, the present invention is described in further detail:
The visible images that the present invention adopts Landsat8 satellite to provide and LONG WAVE INFRARED image, the wave band of these two images is respectively 0.63um – 0.68um and 10.60um-11.20um, and horizontal resolution is 30m and 100m.LONG WAVE INFRARED image have recorded the temperature on cloud or earth's surface, and unit is kelvin degree K, is designated as T; Visible images have recorded albedo, and represent the ratio of the radiation of the radiation that satellite reception arrives and sun incidence, dimensionless, is designated as
(Fig. 2 (a)).On the basis of these two band satellite images, as shown in Figure 1, the invention process process comprises four key steps: cloud ground is separated, and utilizes visible images and LONG WAVE INFRARED image, and extracts the cumulus region in image in conjunction with threshold method; Shape is estimated, utilizes the Temperature estimate cloud-top height of LONG WAVE INFRARED image, and then estimates the height of cloud base; Extinction coefficient calculates, and utilizes the albedo of visible images to calculate the optical thickness of cloud, and then estimates the extinction coefficient of cloud; Particle sampler and drafting, sample in the inside of every YIDUOYUN, forms the particle model of cloud, and draw it.The present invention is implemented as follows:
Step one: cloud ground is separated, and utilizes visible images and LONG WAVE INFRARED image, and extracts the cumulus region in image in conjunction with threshold method:
In order to modeling cumulus, first need correctly separated to the cumulus region in satellite cloud picture and other regions.Due to compared with earth's surface, the height above sea level of cloud is higher, and temperature is lower, and albedo is higher, therefore combines and adopts LONG WAVE INFRARED image and visible images, tag cloud pixel.Particularly, if a pixel meets following formula, cloud pixel is judged to be:
Wherein ∈ is threshold value, and value is 220 in the present invention.
After all pixels in satellite cloud picture are all marked as cloud pixel or earth's surface pixel, obtain the bianry image of a width about cloud mask.Further, utilize Image Edge-Detection operator, form the region of some connections, and each UNICOM region is considered as a cumulus.As shown in Fig. 2 (b), by simple threshold method, comparatively accurate cumulus segmentation result can be obtained.
Step 2: shape is estimated, utilizes the Temperature estimate cloud-top height of LONG WAVE INFRARED image, and then estimate the height of cloud base:
Because the cloud body of cumulus is thicker, optical thickness is comparatively large, and surface radiation is difficult to arrive cloud top through cloud body, thus is arrived by satellite capture, therefore the temperature T of LONG WAVE INFRARED image can be regarded as cloud-top temperature, and then estimate cloud-top height CTH.
Cumulus is generally positioned at the troposphere of below height above sea level 10km, and temperature here declines with the speed of about 6.5K/km along with height above sea level.Utilize this meteorology rule, in conjunction with surface temperature T
g, can cloud-top height be obtained by formula below:
In above formula, temperature T is known, and Lapse rate of air temperature γ=6.5K/km, surface temperature T
gtherefore the key parameters solving cloud-top height CTH is become.Consider that surface temperature remains unchanged (local homogeneity) in less geographic area, and always higher than the temperature of cloud pixel, therefore use the maximum temperature in this neighborhood of pixels to carry out approximate surface temperature T
g:
T
g(p)＝max(T(p
i),|p-p
i|＜D
0) (3)
In above formula, D
0for the diameter in current cumulus region.
From observational data, cumulus is tower-like and bottom flat.Therefore, cloud base can be approximated to be surface level, and only need can characterize surface, cloud base with single height value.Under the condition that supposition cloud base is smooth, the present invention utilizes the cloud-top height mean value of cumulus zone boundary pixel as height of cloud base CBH:
In above formula,
represent the set of cumulus zone boundary pixel,
represent the number of boundary pixel.
Step 3: utilize the albedo of visible images to calculate the optical thickness of cloud, and then estimate the extinction coefficient of cloud:
After obtaining height of cloud base CBH and cloud-top height CTH, calculate the geometric thickness Δ Z=CTH-CBH of cloud.Further, following formula can be utilized to be associated with opticalthicknessτ by extinction coefficient σ:
σ＝τ/ΔZ (5)
In order to try to achieve opticalthicknessτ, the bidirectional reflectance function of the visible light wave range that the present invention adopts the people such as Kokhanovsky to propose:
In above formula
the albedo of visible images record,
the spherical albedo of semi-infinite atmosphere, K
0(μ) be the Escape function of non-absorbing medium, g is asymmetric factor, and α is parameter.If θ
vand φ
vzenith angle and the position angle of satellite respectively, θ
sand φ
szenith angle and the position angle of the sun, then u=|cos (θ respectively
v) |, u
0=| cos (θ
s) |, relative bearing
because cumulus belongs to water cloud (cloud particle is made up of spherical water droplets), asymmetric factor g=0.85, α=1.07, and
wherein, A=3.944, B=-2.5, C=10.664.
Because Landsat8 is with the close angular observation earth's surface perpendicular to ground, the zenith angle of satellite can be approximated to be 0 degree, and the position angle of satellite then can be obtained according to the track geometry of satellite.In addition, the zenith angle of the sun and position angle can directly obtain from satellite data.Can find out in formula (6), to only have unknown number, i.e. an opticalthicknessτ.Therefore, the albedo of visible images is substituted into formula (6) left side, instead can solve opticalthicknessτ.
Step 4: particle sampler and drafting, forms the particle model of cloud, and draws it:
Cloud-top height CTH and height of cloud base CBH is utilized to build the initial surface of cloud.This surface comprises three parts: surface, cloud top, the side of surface, cloud base and cloud.Wherein, surface, cloud top and surface, cloud base are determined by CTH and CBH, and the side of cloud obtains by connecting cloud top border vertices that is surperficial and surface, cloud base.As shown in Fig. 3 (a), the bottom surface of cloud is more flat, and this is caused by the hypothesis of cloud base level; There is more flat region in side, is not mainly overlapped by cloud top and cloud base and cause; Cloud top relative smooth, mainly because the spatial resolution of LONG WAVE INFRARED cloud atlas is relatively low.
In order to obtain cloud shape true to nature, the present invention utilizes the fractal method of the people such as Nishita to generate the details of cloud, optimizes the shape of cloud.First, the bounding box of discrete initial surface, generates a regular grid.Each net point has two attributes, and one is extinction coefficient, and another is the distance of this net point to initial surface.Given net point (x
i, y
j, z
k), if CBH<z
k<CTH, then the extinction coefficient of this net point is set to pixel (x
i, y
j) extinction coefficient; Otherwise, be set to 0.Secondly, place first ball of different radii on the surface of cloud at random, thus produce new distance field.Circulation 3-4 time, forms the final distance field that cloud is corresponding.Consider that cloud base is more flat, the radius of first ball successively decreases with highly linear, namely places little first ball on cloud top, places larger first ball in cloud base.Original shape due to cloud there occurs change (Fig. 3 (b)), and extinction coefficient body needs to do corresponding adjustment.Here, arest neighbors interpolation algorithm is adopted to upgrade extinction coefficient body.Then, at the net point place generation cloud particle that extinction coefficient is positive.Wherein, the radius of particle is proportional to mesh spacing, and the center of particle is obtained by the position of disturbance grid node, and the extinction coefficient of particle equals the extinction coefficient of this node.Finally, utilize the method for the people such as Harris to draw the cloud in scene, effect is as Fig. 3 (c).Fig. 4 gives the effect of the cumulus scene drawing under different points of view and Different Light.
The content be not described in detail in instructions of the present invention belongs to the known prior art of professional and technical personnel in the field.
The above is only the preferred embodiment of the present invention; it should be pointed out that for those skilled in the art, under the premise without departing from the principles of the invention; can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.
Claims (5)
1., based on a method for LandSat8 satellite image modeling cumulus scene, it is characterized in that the method step is as follows:
Step (1), cloud ground is separated, and utilizes visible images and LONG WAVE INFRARED image, and extracts the cumulus region in image in conjunction with threshold method;
Step (2), shape are estimated, utilize the Temperature estimate cloud-top height of LONG WAVE INFRARED image, and then estimate the height of cloud base;
Step (3), extinction coefficient calculate, and utilize the albedo of visible images to calculate the optical thickness of cloud, and then estimate the extinction coefficient of cloud;
Step (4), particle sampler and drafting, sample in the inside of every YIDUOYUN, forms the particle model of cloud, and draw it.
2. a kind of method based on LandSat8 satellite image modeling cumulus scene according to claim 1, is characterized in that: the particular content that described step (1) medium cloud ground is separated is as follows:
Albedo in step (A1), conbined usage visible images and the temperature data in LONG WAVE INFRARED image, by simple threshold method, be divided into two classes, i.e. cloud pixel and earth's surface pixel by image pixel;
Step (A2), for cloud pixel, utilize Image Edge-Detection operator, obtain the region of some connections, each connected region is considered as a cumulus.
3. according to claim 1 based on the method for LandSat8 satellite image modeling cumulus scene, it is characterized in that: the step that described step (2) shape is estimated is specific as follows:
Step (B1), for cloud pixel, the linear relationship of the temperature of LONG WAVE INFRARED image and sea level elevation is utilized to estimate cloud-top height;
Step (B2), hypothesis cumulus at the bottom of level basis on, using the mean value of the cloud-top height of cumulus zone boundary pixel as the height of cloud base.
4. according to claim 1 based on the method for LandSat8 satellite image modeling cumulus scene, it is characterized in that: the step that in described step (3), extinction coefficient calculates is as follows:
Step (C1), utilize the albedo of visible images, the bidirectional reflectance function in conjunction with visible light wave range calculates the optical thickness of cloud;
Step (C2), the cloud-top height utilizing step (2) to obtain and the height of cloud base calculate the geometric thickness of cloud, and then on the basis of hypothesis cumulus Vertical Uniform, utilize optical thickness and geometric thickness to estimate the extinction coefficient of cumulus.
5. according to claim 1 based on the method for LandSat8 satellite image modeling cumulus scene, it is characterized in that: in described step (4), the step of particle sampler and drafting is as follows:
Step (D1), the cloud-top height utilizing step (2) to obtain and the height of cloud base build the initial distance field of cloud, and form initial extinction coefficient body;
Step (D2), disturbance initial distance field, form the shape details of cloud, and upgrade extinction coefficient body;
Step (D3), utilize extinction coefficient body to sample in the inside of cloud, form the particle model of cloud, and adopt the repeatedly particle model of forward scattering model to cumulus to draw.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106291588A (en) * | 2016-08-04 | 2017-01-04 | 安徽省安光环境光学工程技术研究中心有限公司 | A kind of cloud layer signal automatic-identifying method based on finite state machine |
| CN107689079A (en) * | 2017-08-28 | 2018-02-13 | 北京航空航天大学 | The cloudland method for reconstructing that a kind of satellite cloud picture is combined with natural image |
| CN109461197A (en) * | 2017-08-23 | 2019-03-12 | 当家移动绿色互联网技术集团有限公司 | A kind of cloud real-time rendering optimization algorithm based on spherical surface UV and re-projection |
| CN112348872A (en) * | 2020-09-28 | 2021-02-09 | 西安电子科技大学 | Physical process-based infrared cloud accumulation modeling method |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8818029B1 (en) * | 2010-05-27 | 2014-08-26 | The Board Of Trustees Of The University Of Alabama, For And On Behalf Of The University Of Alabama In Huntsville | Weather forecasting systems and methods |
| CN104143205A (en) * | 2013-05-11 | 2014-11-12 | 哈尔滨点石仿真科技有限公司 | Method for achieving real-time rendering of large-scale realistic volumetric cloud |
-
2014
- 2014-12-03 CN CN201410725528.5A patent/CN104408770A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8818029B1 (en) * | 2010-05-27 | 2014-08-26 | The Board Of Trustees Of The University Of Alabama, For And On Behalf Of The University Of Alabama In Huntsville | Weather forecasting systems and methods |
| CN104143205A (en) * | 2013-05-11 | 2014-11-12 | 哈尔滨点石仿真科技有限公司 | Method for achieving real-time rendering of large-scale realistic volumetric cloud |
Non-Patent Citations (4)
| Title |
|---|
| A.A.KOKHANOVSKY 等: "Simple approximate solutions of the radiative transfer equation for a cloudy atmosphere", 《REMOTE SENSING OF CLOUDS AND THE ATMOSPHERE IX》 * |
| CHUNQIANG YUAN 等: ""Modelling Cumulus Cloud Shape from a Single Image"", 《COMPUTER GRAPHICS FORUM》 * |
| CHUNQIANG YUAN 等: "An efficient framework for modeling clouds from Landsat8 images", 《SIXTH INTERNATIONAL CONFERENCE ON GRAPHIC AND IMAGE PROCESSING（ICGIP 2014）》 * |
| 杨晖 等: "图像分割方法综述", 《电脑开发与应用》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106291588A (en) * | 2016-08-04 | 2017-01-04 | 安徽省安光环境光学工程技术研究中心有限公司 | A kind of cloud layer signal automatic-identifying method based on finite state machine |
| CN109461197A (en) * | 2017-08-23 | 2019-03-12 | 当家移动绿色互联网技术集团有限公司 | A kind of cloud real-time rendering optimization algorithm based on spherical surface UV and re-projection |
| CN109461197B (en) * | 2017-08-23 | 2023-06-30 | 北京五一视界数字孪生科技股份有限公司 | Cloud real-time drawing optimization method based on spherical UV and re-projection |
| CN107689079A (en) * | 2017-08-28 | 2018-02-13 | 北京航空航天大学 | The cloudland method for reconstructing that a kind of satellite cloud picture is combined with natural image |
| CN107689079B (en) * | 2017-08-28 | 2019-12-27 | 北京航空航天大学 | Cloud scene reconstruction method combining satellite cloud picture and natural image |
| CN112348872A (en) * | 2020-09-28 | 2021-02-09 | 西安电子科技大学 | Physical process-based infrared cloud accumulation modeling method |
| CN112348872B (en) * | 2020-09-28 | 2023-10-03 | 西安电子科技大学 | Modeling method of infrared accumulated cloud based on physical process |
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