CN105574856A - Ice-snow area extraction method based on dual-polarized SAR (Synthetic Aperture Radar) image - Google Patents
Ice-snow area extraction method based on dual-polarized SAR (Synthetic Aperture Radar) image Download PDFInfo
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Abstract
The invention discloses an ice-snow area extraction method based on a dual-polarized SAR (Synthetic Aperture Radar) image. The method comprises the following steps: acquiring an original dual-polarized SAR image through an SAR satellite; acquiring a DEM (Digital Elevation Model) elevation data image; further acquiring a reference dual-polarized SAR image through preprocessing; then, calculating backscattering coefficients of pixel points of in the reference dual-polarized SAR image; further extracting a wet snow information image accordingly; and combining a wet snow pixel proportion of a pixel window and average altitude information obtained through the DEM elevation data image to realize extraction of an ice-snow area finally. Through adoption of the ice-snow area extraction method, extraction of a wet snow area is realized through an amplitude feature and a polarization feature of a single-time-phase SAR image; extraction of a dry-snow area is realized by adding DEM altitude information on the basis; and the accuracy and timeliness of ice-snow area extraction are improved effectively.
Description
Technical field
The present invention relates to Snow Cover Area monitoring technical field, be specially a kind of ice and snow area extraction method based on dual polarization SAR image.
Background technology
The existence of cryosphere affects the various aspects of the mankind's activities such as tellurian water resource, communications and transportation, agricultural production, social economy, also plays an important role to the detection of meteorology and estimating.It is a crucial value of cryosphere information that snow capping amasss, and it has directly reacted water resource circulation and climate change.For the mankind, snow both can be considered to resource, simultaneously also can by as threat.On the one hand, abundant snowfall is that water generating and water resource supply provide reliable guarantee, and snowfall to a certain degree is simultaneously to alleviating damage caused by a drought and promoting that the growth of crops also serves positive effect.On the other hand, the disasteies such as sudden flood and snowslide are easily produced because temperature raises the snow melt caused.The Melting Glaciers: that global warming causes is the subject under discussion that the world today pays close attention to, the rising on sea level has great threat to coastal state and island country, melt along with snow mountain, the flood disaster risk of part river basin and physical features height above sea level low country increases, and crop growth also can be affected.In addition, the extreme cold current weather broken out all over the world in recent years, as the strong weather etc. of the U.S. at the beginning of the especially big snow disaster of south China beginning in 2008, the strongest cold current, 2014 over more than 70 years of Europe winter in 2012, cause huge loss all to the lives and properties of the mankind.Therefore, the drawing of snow cover area is in the research of Global climate change and water resources management, and the aspect that snow disaster scope and degree are quick and precisely assessed, and serves very important effect.
Early stage accumulated snow detects and depends on meteorological stations Information Monitoring, but this observation procedure is by the restriction in geographic position, cannot comprehensively monitor the glacier snow mountain of remote districts and High aititude.Remote sensing technology is the effective means of monitoring Snow Cover Area, has the advantage such as large area, real-time.Optical remote sensing technology development relatively early, has high resolving power, multispectral section, the advantage such as periodicity, for the Optical remote satellite of various countries' transmitting, has had many scholars to propose Snow Mapping method based on remote optical sensing both at home and abroad.But because snow-up terrain is usually along with harsh climate, have a large amount of cloud cover, remote optical sensing imaging will be subject to the impact of sexual intercourse weather unavoidably.Passive microwave remote sensing technology is also applied to snow lid information research, can be used for the detection of distribution of Snow Cover Over and water equivalent of snow, but because of resolution lower, accurate measurement result cannot be provided.
Synthetic-aperture radar (SyntheticApertureRadar, SAR) belongs to active microwave remote sensing, by launched microwave pulse signal, transfers to earth's surface and by after scatterer reflects, receives echoed signal.Synthetic-aperture radar has the advantage of round-the-clock, round-the-clock, variable-resolution, and because microwave has the characteristic penetrating sexual intercourse, therefore SAR imaging can not affect by snow-up terrain cloud layer, haze etc.In each time period imaging, the measurement of different scale scope can also be carried out by adjustment resolution by the restriction of sunshine unlike, SAR with optical satellite.
But because the change in time and space of ice and snow is very fast, how to make full use of Mono temporal SAR data and extract ice and snow information, strengthen the ageing difficult problem being prior art and needing to solve of ice and snow information extraction.
Summary of the invention
The object of the present invention is to provide a kind of ice and snow area extraction method based on dual polarization SAR image for the problems referred to above, with the ice and snow area information of enhanced SAR image extract ageing, technical scheme is as follows:
Based on an ice and snow area extraction method for dual polarization SAR image, comprise the following steps:
1) original dual polarization SAR image is obtained in same detection zone, i.e. original HH polarization diagrams picture and original HV polarization diagrams picture;
2) the DEM altitude figures image of original HH polarization diagrams picture or original HV polarization diagrams picture is obtained;
3) pre-service is carried out to original dual polarization SAR image and obtain benchmark SAR image, be i.e. benchmark HH polarization diagrams picture and benchmark HV polarization diagrams picture;
4) backscattering coefficient of all pixel points in benchmark HH polarization diagrams picture and benchmark HV polarization diagrams picture is calculated respectively;
5) according to the backscattering coefficient of each pixel point, binary conversion treatment is carried out to benchmark HH polarization diagrams picture, obtain image A;
6) be divided by by the backscattering coefficient of corresponding to benchmark HH polarization diagrams picture and benchmark HV polarization diagrams picture pixel point and obtain ratio images, correlative value image carries out binary conversion treatment and obtains image B;
7) logic and operation is carried out to image A and image B, thus obtain snow slush frame;
8) information in conjunction with described DEM altitude figures image extracts the ice and snow frame including snow slush pixel point and dry snow pixel point from snow slush frame;
9) count total number K of snow slush pixel point and dry snow pixel point according to ice and snow frame, according to the area m of each pixel Vertex cover, calculate the total area of all ice and snow pixel points, i.e. S=m × K.
Further, describedly pre-service concrete grammar is carried out to original dual polarization SAR image be: successively radiation calibration and geocoding are carried out to original dual polarization SAR image.
Further, the backscattering coefficient M of described pixel point
0computing method be: M
0=10log σ, unit is db; Wherein σ is the value of each picture dot point in benchmark SAR image.
Further, the method obtaining described image A is specially: arrange backscattering coefficient threshold value M, is compared by the backscattering coefficient of pixel points all in benchmark HH polarization diagrams picture, the pixel point being more than or equal to M is labeled as 1 with threshold value M, the pixel point being less than M is labeled as 0, thus obtains image A;
The method obtaining described image B is specially: arrange fractional threshold N, the ratio of the backscattering coefficient of corresponding to benchmark HH polarization diagrams picture and benchmark HV polarization diagrams picture pixel point is compared with threshold value N, the pixel point being more than or equal to threshold value N in ratio images is labeled as 1, the pixel point being less than threshold value N is labeled as 0, thus obtains image B;
1 is labeled as snow slush pixel point in described snow slush frame.
Further, the value of described backscattering coefficient threshold value M is-17; The value of described fractional threshold N is 0.2.
Further, the method for described extraction ice and snow frame is:
Using the pixel of n × n in snow slush frame o'clock as a pixel window, calculate the snow slush pixel point number k of each pixel window, and then calculate the snow slush pixel scale of each pixel window: R=k/ (n × n);
The altitude information of each pixel point carried according to described DEM altitude figures image calculates the mean sea level H of pixel point in each pixel window;
Snow slush pixel scale threshold value R is set
0with elevation threshold H
0, find out H>=H in snow slush frame
0and R>=R
0pixel window, all pixel points in this pixel window are labeled as 2, and as dry snow pixel point, all the other pixel windows remain unchanged, thus obtain ice and snow frame.
Further, described snow slush pixel scale threshold value R
0value is 10%, described elevation threshold H
0value is 3900 meters.
The invention has the beneficial effects as follows: the present invention achieves the extraction of snow slush area by the amplitude characteristic of the SAR image of Mono temporal and polarization characteristic, add the extraction that DEM altitude information achieves dry snow area on this basis, effectively enhance the accuracy of ice and snow area extraction and ageing.
Accompanying drawing explanation
Fig. 1 is the step block diagram of the ice and snow area extraction method based on dual polarization SAR image.
Fig. 2-1 is the original HH polarization diagrams picture in region to be detected.
Fig. 2-2 is the original HV polarization diagrams picture in region to be detected.
Fig. 2-3 is the DEM altitude figures image in region to be detected.
Fig. 3 carries out the benchmark HH polarization diagrams picture after radiation calibration and geocoding to original HH polarization diagrams picture.
Fig. 4 is the method exemplary plot of benchmark HH polarization diagrams picture being carried out to binary conversion treatment.
Fig. 5 is the method exemplary plot that correlative value image carries out binary conversion treatment.
Fig. 6 obtains the method exemplary plot of snow slush frame.
The snow slush frame in Fig. 7 region to be detected.
The ice and snow frame in Fig. 8 region to be detected.
The snow and ice cover area-graph picture in Fig. 9 region to be detected.
Embodiment
Below in conjunction with the drawings and specific embodiments, the present invention will be further described: as shown in Figure 1, and a kind of ice and snow area extraction method based on dual polarization SAR image of the present invention, comprises the following steps:
1) original dual polarization SAR image is obtained:
In the present embodiment, C-band can be obtained by ASAR (advancedsyntheticapertureradar Advanced Synthetic Aperture Radar) satellite and obtain original dual polarization SAR image in same detection zone, in this original dual polarization SAR image, comprise HH, HV two polarization diagrams pictures, i.e. co polarization diagram picture and cross polarization image, as shown in Fig. 2-1 and Fig. 2-2, this is two polarization diagrams pictures of the Qilian mountains, upstream, Heihe ice Watershed, Fig. 2-1 is original HH polarization diagrams picture, and Fig. 2-2 is original HV polarization diagrams picture.
2) DEM altitude figures image is obtained:
NEST image processing software is utilized to open original HH polarization diagrams picture or original HV polarization diagrams picture, utilize the download image function of NEST image processing software to get DEM (DigitalElevationModel digital elevation model) the altitude figures image of original HH polarization diagrams picture or original HV polarization diagrams picture, Fig. 2-3 is this area DEM altitude figures image.The altitude information of each pixel point is carried in DEM altitude figures image.
3) benchmark dual polarization SAR image is obtained:
Pre-service is carried out to original dual polarization SAR image, obtains benchmark dual polarization SAR image.Namely respectively radiation calibration and geocoding are carried out to original HH polarization diagrams picture and original HV polarization diagrams picture, obtain benchmark HH polarization diagrams picture and benchmark HV polarization diagrams picture.
Wherein, radiation calibration is associated with Terrain Scattering characteristic by image picture elements point, and geocoding is for image adds geography information.In the present embodiment, for benchmark HH polarization diagrams picture, the result after radiation calibration and geocoding as shown in Figure 3.4) backscattering coefficient is calculated:
Calculate the backscattering coefficient (unit is db) of all pixel points in benchmark HH polarization diagrams picture and benchmark HV polarization diagrams picture respectively;
Computing formula is: M
0=10log σ
In formula, σ is the value of each picture dot point in benchmark SAR image.
5) bianry image of benchmark HH polarization diagrams picture is obtained:
Backscattering coefficient threshold value M is set, the backscattering coefficient of pixel points all in benchmark HH polarization diagrams picture is compared with threshold value M, the pixel point being more than or equal to M is labeled as 1, the pixel point being less than M is labeled as 0, thus obtains image A.
In the present embodiment, backscattering coefficient threshold value M value is-17, if the pixel point number in benchmark HH polarization diagrams picture is 30 × 30, is described with 3 × 3 pixel points any in HH polarization diagrams picture.As shown in Figure 4, the backscattering coefficient of 3 × 3 pixel points in benchmark HH polarization diagrams picture is compared with backscattering coefficient threshold value-17, pixel point backscattering coefficient being more than or equal to-17 is labeled as " 1 ", pixel point backscattering coefficient being less than-17 is labeled as " 0 ", obtain " 0,1 " binary segmentation image, right half part as shown in Figure 4.
6) bianry image of ratio images is obtained:
The backscattering coefficient of corresponding to benchmark HH polarization diagrams picture and benchmark HV polarization diagrams picture pixel point is divided by and obtains ratio images; Arrange fractional threshold N, compared by all ratios in ratio images with threshold value N, the pixel point being more than or equal to threshold value N is labeled as 1, and the pixel point being less than threshold value N is labeled as 0, thus obtains image B.
In the present embodiment, fractional threshold N value is 0.2, is described equally with any 3 × 3 pixel points.The backscattering coefficient of benchmark HH polarization diagrams picture and the corresponding pixel point of benchmark HV polarization diagrams picture is made ratio, its ratio result forms ratio images according to preimage unit point position, left-half as shown in Figure 5, again the backscattering coefficient ratio of pixel points all in ratio images is compared with fractional threshold 0.2, pixel point ratio being more than or equal to 0.2 is labeled as " 1 ", pixel point ratio being less than 0.2 is labeled as " 0 ", obtains " 0,1 " binary segmentation image, right half part as shown in Figure 5.
7) snow slush frame is extracted:
Logic and operation is carried out to image A and image B, obtains snow slush frame, the right half part namely shown in Fig. 6, in snow slush frame, be labeled as the pixel point of " 1 ", be snow slush pixel point.
In the present embodiment, as shown in Figure 7, the white portion in figure is snow slush to view picture snow slush frame, and black part is divided into dry snow or without snow region.
8) ice and snow frame is extracted:
Using the pixel of n × n in snow slush frame o'clock as a pixel window, calculate the snow slush pixel point number k of each pixel window, and then calculate the snow slush pixel scale of each pixel window: R=k/ (n × n).In the present embodiment, in snow slush frame, with the upper left corner (0,0) coordinate points is starting point, utilizes the pixel window of 3 × 3 to slide, because the pixel point number arranged in snow slush frame is 30 × 30, therefore pixel window needs slip 100 times, during each slip, count the number k of snow slush pixel in each pixel window, then to calculate in whole snow slush frame snow slush pixel scale R=k/ (3 × 3) under each 3 × 3 pixel windows.
The altitude information of each pixel point carried according to described DEM altitude figures image calculates the mean sea level H of pixel point in each pixel window.In the present embodiment, with the DEM altitude figures image upper left corner (0,0) coordinate points for starting point, when utilizing the pixel window of 3 × 3 to slide, the mean sea level H of pixel point in each pixel window can be calculated.
Snow slush pixel scale threshold value R is set
0with elevation threshold H
0, find out H>=H in snow slush frame
0and R>=R
0pixel window, all pixel points in this pixel window are labeled as 2, and as dry snow pixel point, all the other pixel windows remain unchanged, thus obtain ice and snow frame.
In the present embodiment, snow slush pixel scale threshold value R
0value is 10%, elevation threshold H
0value is 3900 meters, and as shown in Figure 8, wherein grey is snow slush pixel point to view picture ice and snow frame, and black is dry snow pixel point, and white is without snow region.
9) ice and snow area information is extracted
Count total number K of snow slush pixel point in ice and snow frame and dry snow pixel point, then according to the area m of each pixel Vertex cover, calculate the total area of all ice and snow pixel points, i.e. S=m × K, thus obtain ice and snow area information.As shown in Figure 9, white is snow and ice cover region, and black is without snow region.
Claims (7)
1., based on an ice and snow area extraction method for dual polarization SAR image, it is characterized in that, comprise the following steps:
1) original dual polarization SAR image is obtained in same detection zone, i.e. original HH polarization diagrams picture and original HV polarization diagrams picture;
2) the DEM altitude figures image of original HH polarization diagrams picture or original HV polarization diagrams picture is obtained;
3) pre-service is carried out to original dual polarization SAR image and obtain benchmark SAR image, be i.e. benchmark HH polarization diagrams picture and benchmark HV polarization diagrams picture;
4) backscattering coefficient of all pixel points in benchmark HH polarization diagrams picture and benchmark HV polarization diagrams picture is calculated respectively;
5) according to the backscattering coefficient of each pixel point, binary conversion treatment is carried out to benchmark HH polarization diagrams picture, obtain image A;
6) be divided by by the backscattering coefficient of corresponding to benchmark HH polarization diagrams picture and benchmark HV polarization diagrams picture pixel point and obtain ratio images, correlative value image carries out binary conversion treatment and obtains image B;
7) logic and operation is carried out to image A and image B, thus obtain snow slush frame;
8) information in conjunction with described DEM altitude figures image extracts the ice and snow frame including snow slush pixel point and dry snow pixel point from snow slush frame;
9) count total number K of snow slush pixel point and dry snow pixel point according to ice and snow frame, according to the area m of each pixel Vertex cover, calculate the total area of all ice and snow pixel points, i.e. S=m × K.
2. the ice and snow area extraction method based on dual polarization SAR image according to claim 1, it is characterized in that, describedly pre-service concrete grammar is carried out to original dual polarization SAR image be: successively radiation calibration and geocoding are carried out to original dual polarization SAR image.
3. the ice and snow area extraction method based on dual polarization SAR image according to claim 1, is characterized in that, the backscattering coefficient M of described pixel point
0computing method be: M
0=10log σ, unit is db; Wherein σ is the value of each picture dot point in benchmark SAR image.
4. the ice and snow area extraction method based on dual polarization SAR image according to claim 1, it is characterized in that, the method obtaining described image A is specially: arrange backscattering coefficient threshold value M, the backscattering coefficient of pixel points all in benchmark HH polarization diagrams picture is compared with threshold value M, the pixel point being more than or equal to M is labeled as 1, the pixel point being less than M is labeled as 0, thus obtains image A;
The method obtaining described image B is specially: arrange fractional threshold N, the ratio of the backscattering coefficient of corresponding to benchmark HH polarization diagrams picture and benchmark HV polarization diagrams picture pixel point is compared with threshold value N, the pixel point being more than or equal to threshold value N in ratio images is labeled as 1, the pixel point being less than threshold value N is labeled as 0, thus obtains image B;
1 is labeled as snow slush pixel point in described snow slush frame.
5. the ice and snow area extraction method based on dual polarization SAR image according to claim 4, is characterized in that, the value of described backscattering coefficient threshold value M is-17; The value of described fractional threshold N is 0.2.
6. the ice and snow area extraction method based on dual polarization SAR image according to claim 1, is characterized in that, the method for described extraction ice and snow frame is:
Using the pixel of n × n in snow slush frame o'clock as a pixel window, calculate the snow slush pixel point number k of each pixel window, and then calculate the snow slush pixel scale of each pixel window: R=k/ (n × n);
The altitude information of each pixel point carried according to described DEM altitude figures image calculates the mean sea level H of pixel point in each pixel window;
Snow slush pixel scale threshold value R is set
0with elevation threshold H
0, find out H>=H in snow slush frame
0and R>=R
0pixel window, all pixel points in this pixel window are labeled as 2, and as dry snow pixel point, all the other pixel windows remain unchanged, thus obtain ice and snow frame.
7. the ice and snow area extraction method based on dual polarization SAR image according to claim 6, is characterized in that, described snow slush pixel scale threshold value R
0value is 10%, described elevation threshold H
0value is 3900 meters.
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| CN107170006A (en) * | 2017-06-01 | 2017-09-15 | 中国科学院遥感与数字地球研究所 | The separation method and device of sea ice and Seawater Information in diameter radar image |
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| CN110456352A (en) * | 2019-08-30 | 2019-11-15 | 南京大学 | A kind of glacier recognition methods based on coherence factor threshold value |
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| CN113298802A (en) * | 2021-06-15 | 2021-08-24 | 中国人民解放军国防科技大学 | Method for detecting SAR image quality problem caused by external radiation source |
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| CN115061112A (en) * | 2022-08-09 | 2022-09-16 | 中国科学院地理科学与资源研究所 | Planar snow water equivalent obtaining method and device and electronic equipment |
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| CN107170006A (en) * | 2017-06-01 | 2017-09-15 | 中国科学院遥感与数字地球研究所 | The separation method and device of sea ice and Seawater Information in diameter radar image |
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| CN110456352B (en) * | 2019-08-30 | 2022-03-04 | 南京大学 | Glacier identification method based on coherence coefficient threshold |
| CN113050090A (en) * | 2021-03-28 | 2021-06-29 | 自然资源部国土卫星遥感应用中心 | Dual-polarization HH and HV radar image feature fusion enhancement method |
| CN113050090B (en) * | 2021-03-28 | 2023-08-15 | 自然资源部国土卫星遥感应用中心 | Dual-polarized HH, HV radar image feature fusion enhancement method |
| CN113298802A (en) * | 2021-06-15 | 2021-08-24 | 中国人民解放军国防科技大学 | Method for detecting SAR image quality problem caused by external radiation source |
| CN113920448A (en) * | 2021-12-15 | 2022-01-11 | 航天宏图信息技术股份有限公司 | Flood inundation information extraction method and device, electronic equipment and storage medium |
| CN113920448B (en) * | 2021-12-15 | 2022-03-08 | 航天宏图信息技术股份有限公司 | Flood inundation information extraction method and device, electronic equipment and storage medium |
| CN115061112A (en) * | 2022-08-09 | 2022-09-16 | 中国科学院地理科学与资源研究所 | Planar snow water equivalent obtaining method and device and electronic equipment |
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