CN106599796A - Cloud and cloud shadow distance assessment method facing remote sensing image cloud shadow detection - Google Patents
Cloud and cloud shadow distance assessment method facing remote sensing image cloud shadow detection Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/10—Terrestrial scenes
- G06V20/13—Satellite images
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- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/26—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion
- G06V10/267—Segmentation of patterns in the image field; Cutting or merging of image elements to establish the pattern region, e.g. clustering-based techniques; Detection of occlusion by performing operations on regions, e.g. growing, shrinking or watersheds
Abstract
The invention discloses a cloud and cloud shadow distance assessment method facing remote sensing image cloud shadow detection. The cloud and cloud shadow distance assessment method comprises steps that image gray values are extracted column by column; accumulated gray values are calculated row by row, and are divided by pixel numbers to acquire gray value accumulation rates; slopes of various segments of a curve are extracted; low speed accumulation and fast speed accumulation, which occur continuously, are used as a cloud shadow and cloud area, and a distance between a low speed accumulation starting point and a fast speed accumulation starting point is used as a vertical distance between the cloud and the cloud shadow; the gray value accumulation rates of two rows ri and ri+delta r, the distance between which is delta r, are calculated row by row, and the fast speed accumulation areas and the low speed accumulation areas thereof are identified; the seed growing of the pixel marked as the cloud and the seed growing of the pixel marked as the cloud shadow are carried out, and the cloud marking and the cloud shadow marking are carried out according to a communication area to acquire a cloud object and a cloud shadow object; on the basis of acquisition of the vertical distance delta r and a horizontal distance delta c, thresholds r' and c'of a threshold template are set, and a cloud shadow template is moved in a range of delta r+-r' and in a range of delta c+-c', and a distance satisfying a requirement of a minimum distance between the cloud and the cloud shadow is used as optimal distance assessment between the cloud and the cloud shadow.
Description
Technical field
The present invention relates to a kind of cloud and cloud shadow distance method of estimation towards the detection of remote sensing image cloud shadow.Implement
On, according to the internal relation that cloud and cloud shade are formed, with reference to the characteristics of cloud and higher and relatively low cloud shade gray value, converted
For gray value cumulative percentage, the distance between cloud and cloud shade according to a preliminary estimate；Then cloud and cloud shade are extracted using seed growth, then
The characteristics of being presented negatively correlated according to the gray value between cloud shade and cloud, by minimization correlation coefficient, obtains cloud and cloud shade
Apart from optimal estimating.This method is based entirely on image feature and without the need for parameters such as sun altitude, sensing station and attitudes,
Accurately estimate the distance between cloud and shade, can be used for the compound classification extraction of cloud and cloud shade and its be mutually authenticated, improve cloud
Shadow accuracy of detection.
Background technology
On the image that passive remote sensing is obtained, cloud and cloud shadow region (in the case where obscuring, hereinafter referred to as cloud shadow)
A small amount of earth's surface information is only included, the white space of information is formed on remote sensing image.On the one hand, the presence of Yun Ying causes can use
Cloudless data it is deficient, the scarcity of data becomes the important restrictive factor towards earth's surface application.On the other hand, due to Yun Heyun
Shade causes image statistics feature to be distorted, restriction colors of image normalization, image mosaic, time series analysis, change inspection
Many applications such as survey, by the detection of cloud shadow cloud shadow mask is obtained, and so as to cloud shadow zone domain be rejected, can significantly improve various process
With the precision of analysis.Thus, Yun Ying detections are the important research contents of remote sensing image pretreatment.
One kind of Yun Ying detections is directly perceived and the characteristics of straightforward procedure is the relatively low shadow lightness value using cloud brightness value is higher
Enter row threshold division, it is the key issue for needing to solve that threshold value is chosen.Dynamic threshold (Wu Chuanqing, Wang Qiao, Yang Zhifeng. based on mixed
The water body remote sensing images for closing pixel analysis go cloud method [J]. remote sensing journal .2006, (10) 2:176-183.) and multi thresholds method
(.KMeans such as Wang Wei, Song Weiguo, Liu Shixing clusters MODIS cloud detection algorithms [J] in combination with multiple spectrum thresholds. spectroscopy
With spectrum analyses, 2011,31 (4):1061-1064.) etc. advanced Research on threshold selection is used to improve the precision of threshold value selection.So
And, an insurmountable latent defect of such method：Be difficult to effectively to distinguish cloud and high reflectance atural object and cloud shade and
Antiradar reflectivity atural object, threshold optimization is that balance is reached between false drop rate and loss.
In order to solve the problems, such as cloud and high reflectance atural object and cloud shade and low reflection atural object indistinguishability, on the one hand,
The more low auxiliary information of cloud-top temperature is used to improve cloud shadow accuracy of detection.Typical algorithm is as the ACCA of Landsat ETM+
Reflectance signature of (the Automatic Cloud-Cover Assessment) algorithm by cloud shadow in each wave band builds decision-making
Tree, realize that cloud shadow is detected, so as to improve cloud shadow accuracy of detection (Irish RR, Barker JL, Goward SN, et
al.Characterization of the Landsat-7ETM+Automated Cloud-Cover Assessment
(ACCA)Algorithm[J].Photogrammetric Engineering&Remote Sensing,2006,72(10):
1179–1188.).The gray value comparative approach of multi_temporal images is to cause similar time to obtain on image by cloud and cloud shade
Gray scale value mutation, for aiding in cloud shadow to detect (Hagolle O, Huc M, Pascual D V, et al.AMulti-Temporal
Method for Cloud Detection,Applied to FORMOSAT-2,VENμS,LANDSAT and SENTINEL-
2Images[J].Remote Sensing of Environment,2010,114(8):1747–1755.).Such method lack
Point is to need extra auxiliary information, such as many phase images or special spectrum channel (such as thermal infrared), not enough in auxiliary information
In the case of, the practicality of method is reduced.
The internal relation that cloud and cloud shade are formed so that there is certain spatial relationship between cloud and cloud shade, pass through
Cloud and cloud shade are mutually authenticated, it is possible to increase the precision of the detection of cloud and cloud shade.The space of cloud and cloud shade on image
Relation is decided by sun altitude, sensing station and attitude, the height of cloud at image capturing moment.It is general in image metadata
There is provided parameters such as sun altitude, the position of sensor and attitudes, but cloud level degree determines that being one is difficult to obtain parameter, because
And the parameter of cloud and shadow positions relation cannot be directly obtained by physical model.To this, general solution is in image point
On the basis of cutting acquisition cloud and cloud shade, the object-oriented method of the corresponding relation between cloud and cloud is determined by sun altitude
(Zhu,Z.,&Woodcock,C.E.(2012).Object-based Cloud and Cloud Shadow Detection in
Landsat Imagery.Remote Sensing of Environment,118,83–94.；Watmough Gr,Atkinson
Pm,Hutton C W.A Combined Spectral and Object-Based Approach to Transparent
Cloud Removal in an Operational Setting for Landsat ETM+[J].International
Journal Of Applied Earth Observation And Geoinformation,2011,13(2):220–227.)。
While such method achieves preferable effect, there is also some shortcomings, the precision of such as cloud and cloud shadow extraction determine away from
From the precision estimated, to the application of algorithm uncertainty is brought.A kind of cloud and cloud shade based on image is proposed to this present invention
Method for estimating distance.
The content of the invention
For traditional remote sensing image cloud and the deficiency of cloud shadow distance method of estimation, the present invention proposes a kind of based on image
Cloud and cloud shadow distance method of estimation.
The present invention principle be：In visible ray and near infrared channels, impact of the cloud to remote sensing process is mainly reflected in (as schemed
1)：(I) on the one hand the earth longwave radiation reflection of earth's surface information is returned to ground by cloud to visible ray and the high reflection of near infrared band
Table, so as to get reduce comprising earth's surface information radiation amount up to sensor；On the other hand solar radiation is reflected in a large number and is sensed
Device is received, and equivalent to the larger white noise of intensity is added in image, cloud is formed on image.(II) while, cloud is to sun spoke
The a large amount of reflections penetrated, so as to get the amount of radiation up to earth's surface is reduced, so that the long-wave radiation of earth surface reflection is accordingly reduced, in shadow
As upper formation cloud shade.It can be seen that exist between cloud and cloud shade that negative correlation is shown as between symbiosiss and gray value.
The satellite platform for carrying optical pickocff typically adopts sun-synchronous orbit, (is to obtain Northern Hemisphere morning image
The convenience discussed, only considers here Northern Hemisphere morning image, and other situations can initially be estimated according to sun altitude
Meter).Because sunlight is irradiated from southeastern direction, the cloud shade on remote sensing image is caused to be generally present in the direction northwest of cloud.
Simultaneously as the distance of the sun and cloud is far longer than the distance of sensor and cloud, and sensor is usually vertical earth observation, is made
The distance for obtaining North and South direction is far longer than the distance of east-west direction.Thus, the distance between the cloud and cloud shade in the typical Northern Hemisphere
As shown in Figure 2.
According to principles above, in order to accurately estimate the distance between cloud and cloud shade, one kind of the present invention is towards remote sensing shadow
As the cloud and cloud shadow distance method of estimation of the detection of cloud shadow adopt the following technical scheme that one scape image of process：
Step 1：Image greyscale value is extracted by column, is regarded as the function of retinue change.Due to the gray value of cloud it is higher,
Cloud shade gray value is relatively low, thus cloud forms " plateau " on curve, and cloud shade forms " depression "；
Step 2：From the beginning of the first row, accumulation gray value is calculated line by line, and divided by number of pixels, the numerical value can be considered as ash
Angle value cumulative percentage, corresponding curve is referred to as gray value cumulative percentage curve；
Step 3：Above-mentioned curve is simplified using Douglas method, is indicated with the line segment of different length, carried
Take each section on curve of slope.Cloud dash area gray value is relatively low, and accumulation is slow, causes the slope of corresponding line segment less.And cloud
Brightness value it is higher, accelerated accumulation causes the slope of the corresponding line segment of curve larger；
Step 4：Using the continuous low speed accumulation for occurring with accelerated accumulation as a cloud shade and the region of cloud, and by low speed
The distance between accumulation starting point and accelerated accumulation starting point are used as cloud and the fore-and-aft distance Δ r of cloud shade.By accelerated accumulation area
The pixel in domain is labeled as cloud, and the pixel of low speed accumulation area is labeled as cloud shade, by the cloud of all extractions and cloud shade longitudinal direction away from
From average as whole scape image fore-and-aft distance estimation；
Step 5：Line by line to the two row r at a distance of Δ riAnd ri+ Δ r, according to step 1 and step 2 gray accumulation rate is calculated, and
Recognize accelerated accumulation therein and low speed accumulation area.Will be in riCapable low speed accumulation area and riThe accelerated accumulation area of+Δ r rows
Domain as one group of cloud shadow, and using the distance between low speed accumulation starting point and accelerated accumulation region as corresponding cloud and cloud shade
Lateral distance delta c in region.Simultaneously the pixel in accelerated accumulation region is labeled as into cloud, and the pixel of low speed accumulation area is labeled as
Cloud shade, using the average of the cloud of all extractions and cloud shade lateral separation as whole scape image lateral separation estimation.
Step 6：On the basis of the above, the pixel for being labeled as cloud and cloud shade is carried out into seed growth, and according to connected region
Domain is entered to rack and cloud Shadow marks, obtains cloud and cloud shadow object；
Step 7：On the basis of fore-and-aft distance Δ r and horizontal Δ c is obtained, given threshold template threshold value r' and c', in Δ r
± r' and Δ c ± c' scopes movement cloud shadow templates, distance when cloud and cloud shade correlation coefficient to reach minimum is used as the cloud
Optimal distance estimations and cloud shade between
On this basis, it is possible to use the symbiosiss of cloud shade and cloud enter to rack shadow detection, a kind of method be pixel X
(i, j) position is cloud shade, andFor cloud, so as to pass through the mutual inspection of cloud and cloud shade, cloud shadow is improved
Accuracy of detection.
It is an advantage of the invention that：(1) internal relation according to cloud shade and cloud in formation, with reference to the sun, sensor three
The spatial relationship of person, estimates the approximate location relation between cloud and cloud shade, while estimating cloud and cloud shade using position relationship
Symbiosiss enter to rack shadow detection, high antiradar reflectivity atural object can be overcome to cloud and the adverse effect of cloud shadow extraction.(2) root
According to cloud and the gray scale feature of cloud shade, enter to rack in gray value cumulative percentage domain shade and cloud distance estimations, the method directly exists
The space of raw video completes cloud and cloud shadow distance is estimated, it is to avoid the deficiency that traditional object-oriented method cloud shadow is extracted.
Description of the drawings
Fig. 1 is remote sensing process medium cloud and cloud shadow formation principle schematic diagram；
Fig. 2 is cloud and Yun Ying position relationship schematic diagram on the typical optical image of the Northern Hemisphere, and wherein Fig. 2 (a) is raw video,
Fig. 2 (b) is the cloud and cloud shadow vectors figure for extracting；
Fig. 3 is that the inventive method is embodied as flow chart；
Fig. 4 is fore-and-aft distance estimation principle figure of the present invention, and wherein Fig. 4 (a) is original image and its certain row, and Fig. 4 (b) is
The gray value degree of the row；Fig. 4 (c) is gray accumulation rate figure；Fig. 4 (d) extracts cloud shadow fore-and-aft distance schematic diagram；
Fig. 5 is lateral separation estimation principle figure of the present invention；
Fig. 6 is experimental result 1 of the present invention for the number of domestic high score 1；
Fig. 7 is experimental result 2 of the present invention for the number of domestic high score 1；
Fig. 8 is experimental result of the present invention for Landsat TM5 data.
Specific embodiment
Technical scheme is further illustrated below in conjunction with the accompanying drawings.
Hypothesis has the remote sensing image X that a scape R rows C is arranged, and needs to estimate the distance between cloud therein and cloud shade, to carry out
Yun Ying is detected.
A kind of cloud and cloud shadow distance method of estimation towards the detection of remote sensing image cloud shadow of the present invention, including following step
Suddenly：
Step 1：Gray value is extracted
The gray value of each scape image is extracted by column, and the gray value of c row (c=1,2 ..., C) can be expressed as：
Xc=＜ x1,c,x2,c,....,xR,c＞T
Fig. 4 (a) is gray value curve such as Fig. 4 (b) of three rows on the left of GF1 images, vertical line, the wherein cloud of inframe and cloud shade
Presentation is the relatively low depression of the higher plateau of gray value and gray value on gray value curve.But due to gray-value variation therein
Acutely, it is difficult to extracting directly cloud and cloud shadow region.
Step 2：Gray accumulation rate curve is extracted
The computational methods of gray value cumulative percentage of c row are：
Wherein, R is the line number of image, it can be seen that last is the arithmetic mean of instantaneous value of the row gray scale.The curve can be with
It is considered as the accompanying cumulative speed of gray value, finally reaches the average of the row.It can be seen that the gray value accumulation of shade in Fig. 4 (c)
It is relatively slow, and the accumulation of cloud is very fast, and possess clear and definite flex point, so that cloud and its correspondence cloud shadow region are more prone to carry
Take.
Step 3：Gray accumulation rate curve matching
Using Douglas method, above-mentioned curve is simplified, and the folding that curve is expressed as being made up of each line segment
Line.Assume to be fitted to n section broken lines altogether, wherein i-th line section can be expressed as：
L (i)=(i, ki,bi,rbi,rei)
Wherein kiAnd biRespectively the i-th slope of a curve and intercept, rbiAnd reiThe initial line number of respectively this section curve and
Terminate line number.
Step 4：Fore-and-aft distance is estimated to be extracted with cloud shade and cloud
It is false using the continuous low speed accumulation line segment for occurring and accelerated accumulation line segment as a cloud shade and the candidate region of cloud
If i+1 bar Line segment detection is cloud, and i bars Line segment detection is cloud shade, by the fore-and-aft distance Δ r between cloud and cloud shadekCan
To be expressed as：
Δrk=rbi+1-rbi
rbi+1And rbiThe initial line number of difference i+1 and i bar line segments, the i.e. distance of AB sections in Fig. 4 (d).
On this basis, the pixel in accelerated accumulation region is labeled as into cloud, the pixel in low speed accumulation area is labeled as
Cloud shade, i.e. the pixel fragment of AB sections compares for cloud shade in Fig. 4 (d), and BC segment marks are designated as cloud.
Assume to extract K groups correspondingly cloud and cloud shade on image altogether, and Yun Heyun on the average of the fore-and-aft distance scape image
The estimation of shade fore-and-aft distance Δ r；
Step 5：Lateral separation is estimated
The gray accumulation rate of r rows and r+ Δ r rows is counted line by line, as shown in Figure 5 by the low of the continuous appearance on Δ r
Speed accumulation and accelerated accumulation estimate the fore-and-aft distance estimation side of cloud and cloud shade respectively as cloud and cloud shade according to step 4
Method estimates lateral separation, then tag cloud and cloud shade, then using the average of cloud and the lateral separation of cloud shade as whole scape image
The Δ c according to a preliminary estimate of cloud and cloud shadow distance.
By said process, it is possible to obtain the r of Δ according to a preliminary estimate and Δ c of cloud and cloud shadow distance on remote sensing image.
Step 6：Cloud and cloud shade seed growth
Because the data at cloud and the edge of cloud shade may fail effectively extraction, thus, by the good Yun Heyun of above-mentioned labelling
Shade statistics UNICOM region, and seed growth is carried out, obtain cloud and cloud shade.
Step 7：Yun Ying distance optimizations
On the basis of fore-and-aft distance Δ r and horizontal Δ c is obtained, cloud shadow templates threshold value r' and c' are set, in Δ r ± r'
With moving die plate in the range of Δ c ± c', and correlation coefficient C is calculated, negative correlation coefficient is reached the skew of minima as optimal
Estimate
According to above-mentioned steps, it is possible to obtain the distance between cloud and cloud shade optimal estimating.On this basis can be to cloud
It is mutually authenticated with cloud shade, to improve cloud shadow accuracy of detection.On this basis, using method to two groups of GF1 images and one
Group Landsat methods are processed, and the result for obtaining is as shown in Fig. 6 to Fig. 8.It can be seen that context of methods can be effectively to cloud
Effective detection is carried out with shade, and cloud and cloud shade are extracted simultaneously, can effectively distinguish height reflectance atural object.
Content described in this specification embodiment is only enumerating to the way of realization of inventive concept, the protection of the present invention
Scope is not construed as being only limitted to the concrete form that embodiment is stated, protection scope of the present invention is also and in art technology
Personnel according to present inventive concept it is conceivable that equivalent technologies mean.
Claims (2)
1. a kind of cloud and cloud shadow distance method of estimation towards the detection of remote sensing image cloud shadow, comprises the steps：
Step 1：Gray value is extracted；
The gray value of each scape image is extracted by column, and the gray value of c row (c=1,2 ..., C) can be expressed as：
Xc=＜ x1,c,x2,c,....,xR,c＞T；
Step 2：Gray accumulation rate curve is extracted；
The computational methods of gray value cumulative percentage of c row are：
Wherein, R is the line number of image, it can be seen that last is the arithmetic mean of instantaneous value of the row gray scale, and the curve is considered ash
The accompanying cumulative speed of angle value, finally reaches the average of the row；
Step 3：Gray accumulation rate curve matching；
Using Douglas method, above-mentioned curve is simplified, and the broken line that curve is expressed as being made up of each line segment.It is false
If being fitted to n section broken lines altogether, wherein i-th line segment table is shown as：
L (i)=(i, ki,bi,rbi,rei)
Wherein kiAnd biRespectively the i-th slope of a curve and intercept, rbiAnd reiThe initial line number of respectively this section curve and end
Line number；
Step 4：Fore-and-aft distance is estimated to be extracted with cloud shade and cloud；
Using the continuous low speed accumulation line segment for occurring and accelerated accumulation line segment as a cloud shade and the candidate region of cloud, it is assumed that the
I+1 bars Line segment detection is cloud, and i bars Line segment detection is cloud shade, by the fore-and-aft distance Δ r between cloud and cloud shadekCan be with table
It is shown as：
Δrk=rbi+1-rbi
rbi+1And rbiThe initial line number of difference i+1 and i bar line segments；
On this basis, the pixel in accelerated accumulation region is labeled as into cloud, it is cloudy that the pixel in low speed accumulation area is labeled as cloud
Shadow is assumed to extract K groups correspondence cloud and cloud shade on image altogether, and cloud and cloud shade are indulged on the average of the fore-and-aft distance scape image
To the estimation of distance；
Step 5：Lateral separation is estimated；
The gray accumulation rate of r rows and r+ Δ r rows is counted line by line, and the continuous low speed for occurring on Δ r is accumulated and quick
Accumulation estimates that cloud and the fore-and-aft distance method of estimation of cloud shade are estimated respectively as corresponding cloud shade and cloud according to step 4
Lateral separation, tag cloud and cloud shade, and using the average of cloud and the lateral separation of cloud shade as whole scape image cloud and cloud shade
The Δ c according to a preliminary estimate of distance.
By said process, it is possible to obtain the r of Δ according to a preliminary estimate and Δ c of cloud and cloud shadow distance on remote sensing image；
Step 6：Cloud and cloud shade seed growth；
Because the data at cloud and the edge of cloud shade may fail effectively extraction, thus, by the good cloud of above-mentioned labelling and cloud shade
Statistics UNICOM region, and seed growth is carried out, obtain cloud and cloud shade；
Step 7：Yun Ying distance optimizations；
On the basis of fore-and-aft distance Δ r and horizontal Δ c is obtained, cloud shadow templates threshold value r' and c' are set, in Δ r ± r' and Δ
Moving die plate in the range of c ± c', and correlation coefficient C is calculated, negative correlation coefficient is reached the skew of minima as best estimate
2. cloud according to claim 1 and cloud shadow distance method of estimation, it is characterised in that：Formed using cloud shade and cloud
On internal relation, with reference to the sun, the spatial relationship of sensor three, estimate the approximate location relation between cloud and cloud shade.
On this basis, with reference to cloud and the gray scale feature of cloud shade, by column and line by line using gray value cumulative percentage to suppress noise, and increase
Strong lime degree, estimates so as to directly enter to rack in the space of raw video with cloud shadow distance.
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| CN112102180A (en) * | 2020-08-21 | 2020-12-18 | 电子科技大学 | Cloud identification method based on Landsat optical remote sensing image |
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| CN109101894A (en) * | 2018-07-19 | 2018-12-28 | 山东科技大学 | A kind of remote sensing image clouds shadow detection method that ground surface type data are supported |
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