CN105405112B - Multispectral satellite image range deviation index defogging method - Google Patents
Multispectral satellite image range deviation index defogging method Download PDFInfo
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- 239000003595 mist Substances 0.000 claims abstract description 92
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- 230000008859 change Effects 0.000 claims description 6
- 238000012937 correction Methods 0.000 description 14
- 230000000694 effects Effects 0.000 description 8
- 238000002474 experimental method Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 230000005855 radiation Effects 0.000 description 2
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T2207/10036—Multispectral image; Hyperspectral image
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20024—Filtering details
- G06T2207/20032—Median filtering
Abstract
The present invention relates to multispectral satellite image range deviation index defogging methods.According to the domain containing fog-zone of original multispectral satellite image and the difference setpoint distance bias exponent of the gray value of landmark region；Then, the range deviation index is applied into subrange in original spectrum satellite image, and made improvements.According to above-mentioned improved range deviation index by the way that coarse mist thickness image is calculated；Then process of refinement is carried out to described image, the mist thickness image after removal becomes more meticulous from original spectrum satellite image, so as to fulfill the defogging to original multispectral satellite image.The method versatility of the present invention is stronger, and the consistency that can effectively weaken in the domain of raw video fog-zone influences, and so as to restore the real spectrum information of earth's surface below mist, obtains high quality clearly earth's surface satellite image.
Description
Technical field
The present invention relates to multispectral satellite image range deviation index defogging methods, belong to optical satellite image image procossing
Technical field.
Background technology
In near earth space environment, the influence of cloud and mist directly contributes the quality decline of Optical remote satellite image and obscures,
Serious obstruction is brought to the pretreatment such as the atmospheric correction of image, matching, positioning and subsequent practical application.It is directed in the prior art
The processing of satellite image medium cloud, mist mainly passes through following three aspects：(1) obscuring, changing the imaging features such as slow based on mist,
The low-frequency component in frequency domain is eliminated using frequency domain transformation algorithm；(2) using the correlation between image band, in the spatial domain
Distinguish cloud, mist and real surface；(3) based on dark pixel method, the antiradar reflectivity atural object in raw video is determined, so as to isolate mist
Influence.
Main defogging method to be applied is as channel prior method based on dark in currently available technology, and this method is based on calculating
Defogging model in machine vision, by obtaining transmittance figure picture and estimating that atmosphere light achievees the purpose that defogging, main advantage is
The image effect being evenly distributed to mist is notable.But when haze influences more serious, method is just very limited, it is main
The image for showing as mist protrudes, and larger contrast occurs.
Invention content
It is an object of the invention to overcome the deficiencies in the prior art, it is proposed that multispectral satellite image range deviation index is gone
Mist method solves in the prior art when haze, which influences more serious or cloud and mist, to be unevenly distributed, removes the multispectral shadow of satellite
The technical issues of thick fog effect as in is poor.
The present invention is achieved by following scheme：
Multispectral satellite image range deviation index defogging method, step are as follows：
Step 1, according to the difference of the domain containing fog-zone of original multispectral satellite image and the gray value of landmark region set away from
From bias exponent；Then, the range deviation index is applied into subrange in original spectrum satellite image, and it is carried out
It improves；Improved range deviation exponential expression is：
Wherein, it is left in the x and y direction respectively centered on current pixel point (x, y) in original spectrum satellite image
Extended under upper right, extended distance is set as line, and the minimum gray value in the distance is taken to be denoted as min respectively as background value
(LineXi(x, y)) and min (LineYi(x, y)), i is expressed as wave band；DN'i(x, y) is when mist influence area is as original more
During spectrum image prospect pixel, the pixel gray value at current pixel point (x, y)；
Step 2, according to above-mentioned improved range deviation index by the way that coarse mist thickness image HTM is calculatedi(x,
y)；Then to described image HTMi(x, y) carries out process of refinement, the mist after removal becomes more meticulous from original spectrum satellite image
Thickness image, so as to fulfill the defogging to original multispectral satellite image.
Further, the expression formula of setpoint distance bias exponent is as follows in step 1：
Wherein,During to assume normal earth's surface as image backdrop pels, the true gray value at point (x, y)；
DNi' (x, y) for mist influence area as image prospect pixel when, the pixel gray value at current point (x, y)；I represents wave band；It theoretically represents the pixel value after defogging, is unknown parameter.
Further, in step 2 using improved range deviation index by the way that coarse mist thickness image is calculated
HTMiThe expression formula of (x, y) is as follows：
HTMi(x, y)=DNi'(x,y)-DNi'(x,y)·Δfi(x,y)
Wherein, DNi' (x, y) represent pixel value at raw video the i-th wave band point (x, y)；DNi 0After (x, y) is correction
Pixel value；Δfi(x, y) is range deviation index.
Further, to coarse mist thickness image HTM in step 2iThe process of refinement of (x, y) is specially：Coarse
Mist thickness image HTMiSize is the not overlaid windows of ω × ω defined in (x, y), then, obtains dark pixel image DMi(x',
Y') it is：
Wherein, Θ is represented with point (x' ω, y' ω) as starting point；
Then, medium filtering is carried out to the dark pixel image of acquisition.
Further, the mist thickness image after becoming more meticulous is also needed to be modified, and then obtained original more after defogging
Spectrum satellite image, the then expression formula for correcting the original multispectral satellite image after defogging are as follows：
Wherein, minDNi' to there is the pixel not influenced by mist in original multispectral image.
Present invention advantageous effect compared to the prior art is：
The present invention proposes multispectral satellite image range deviation index defogging method, the present invention will according to domain containing fog-zone and
The gray value of landmark region defines range deviation index, and the range deviation index is improved and is applied multispectral
In the subrange of image, so as to obtain coarse mist thickness image.Then it becomes more meticulous to the coarse mist thickness image
The mist thickness image optimized is handled, is accurately removed so as to fulfill to multispectral image image.The present invention utilizes definition
Range deviation index can effectively remove demisting thickness chart picture from multispectral image, especially when haze influence it is more serious or
When cloud and mist is unevenly distributed, in the case that thickness and distribution change greatly, it can effectively weaken raw video fog-zone domain
In consistency influence, so as to restore the real spectrum information of earth's surface below mist, obtain high quality clearly earth's surface satellite image.
And the method versatility of the present invention is stronger, and application range is wider, to the thick fog and the moon in different type satellite multispectral image
Shadow has the removal effect of highly significant.
Description of the drawings
Fig. 1 is the method flow diagram of the embodiment of the present invention；
Fig. 2 is the spatial distribution map of difference position in original image in the embodiment of the present invention；
Fig. 3 is the range deviation index of adjacent domain difference position in original image in the embodiment of the present invention；
Fig. 4 is whole and part range deviation index results image in the embodiment of the present invention；
(a) raw video；(b) pass through the result images of formula (7)；(c) pass through the result images of formula (8)；
Fig. 5 is mist thickness image coarse in the embodiment of the present invention in the mist thickness chart to be become more meticulous by optimization processing
Picture；
(a) coarse HTM；(b) HTM after optimizing；(c) HTM after filtered；
Fig. 6 is the blue wave band mist of in the embodiment of the present invention 400 × 400 Landsat8OLI multispectral image regional areas
Thickness image and correction result；
(a) raw video in Landsat8OLI multispectral images；
(b) the blue wave band mist thickness image after becoming more meticulous in Landsat8OLI multispectral images；
(c) result images in Landsat8OLI multispectral images；
Fig. 7 is the blue wave band mist thickness of in the embodiment of the present invention 400 × 400 GF-wfv2 multispectral image regional areas
Image and correction result；
(a) raw video in GF-wfv2 multispectral images；
(b) the blue wave band mist thickness image after becoming more meticulous in GF-wfv2 multispectral images；
(c) result images in GF-wfv2 multispectral images；
Fig. 8 is the blue wave band mist thickness image of in the embodiment of the present invention 400 × 400 ZY-3 multispectral image regional areas
With correction result；
(a) raw video in ZY-3 multispectral images；
(b) the blue wave band mist thickness image after becoming more meticulous in ZY-3 multispectral images；
(c) result images in ZY-3 multispectral images；
Fig. 9 is the Landsat8OLI images AHOT of the 1000 × 1000 of the embodiment of the present invention and the correction result of the present invention
And blue wave band histogram；
(a) Landsat8OLI raw videos；
(b) the AHOT results of Landsat8OLI images；
(c) result images of Landsat8OLI images in the present invention；
(d) histogram before and after the blue wave band correction of Landsat8OLI images；
Figure 10 is the GF-wfv2 images AHOT of the 1000 × 1000 of the embodiment of the present invention and the correction result and indigo plant of the present invention
Wave band histogram；
(a) GF-wfv2 raw videos；
(b) the AHOT results of GF-wfv2 images；
(c) result images of GF-wfv2 images in the present invention；
(d) histogram before and after the blue wave band correction of GF-wfv2 images；
Figure 11 is the ZY-3 images AHOT of the embodiment of the present invention and the correction result of the present invention and blue wave band histogram；
(a) ZY-3 raw videos；
(b) the AHOT results of ZY-3 images；
(c) result images of ZY-3 images in the present invention；
(d) histogram before and after the blue wave band correction of ZY-3 images.
Specific embodiment
The present invention will be further described in detail with reference to the accompanying drawings and examples.
Multispectral satellite image range deviation index defogging method, is as follows：
1. for satellite image due to it includes complicated atural object, and having a very wide distribution, the pixel gray value of cloud and mist becomes
It on different regions is inconsistent to change, therefore is difficult directly to distinguish cloud and mist and below normally by given threshold
Table.Therefore, for original multispectral satellite image in the present embodiment, respectively from general image and the original shadow of local regional analysis
As range deviation index Δ f (x, y) variation relation of current pixel point (x, y), and calculate the range deviation index map of the i-th wave band
As Δ fi(x, y) weakens the consistency influence factor of cloud, mist by calculating range deviation index, so as to obtain mist thickness image
To distinguish cloud, mist and real surface below；Concrete mode is as follows：
As shown in Fig. 2, three points a, b and c are distributed in original multispectral image, the gray value difference table of these three points
It is shown as DN'a、DNb' and DN'c：If a points are in the domain of fog-zone, b and c belong to normal region, since earth's surface is there are high luminance targets,
Therefore a points in the domain of fog-zone are likely less than the b and c of normal region, so it is difficult to directly being distinguished by gray value to 3 points.
So a, b and c represent its gray value by following relational expression：
Wherein, Ha, Hb and Hc are respectively the influence of the mist of corresponding points a, b and c,WithIt is respectively corresponding
The reflected energy of the normal earth's surface of point a, b and c.
Definition, range deviation index Δ fi(x, y) is：
Wherein,During to assume normal earth's surface as image backdrop pels, the true gray value at point (x, y)；
DNi' (x, y) for mist influence area as image prospect pixel when, the pixel gray value at current point (x, y)；I represents wave band.It theoretically represents the pixel value after defogging, is unknown parameter.Convolution (1) and formula (2) calculate point a, b, c tri-
The range deviation index of point is respectively Δ f (a), Δ f (b), Δ f (c), and expression formula is as follows：
When the influence of mist suffered by 3 points of point a, b and c is identical, i.e. Ha=Hb=Hc, range deviation index at this time
It is as follows to be used for reflecting the ratio relation of three real surface reflected energies：
As shown in figure 3, in the influence area of mist, it, can be with approximating assumption in a certain range since the variation of mist is slow
The influence of (each row or every a line) mist is consistent, as shown in Figure 3, it is assumed that p1、p2、p33 points are influenced consistent, three by mist
Corresponding relationship is p1h1:p2h2:p3h3, wherein p2And p32 points are closer to be difficult to differentiate between, and become after range deviation index
For op1:op2:op3, triangular distance is opened.Therefore by range deviation index can detach these consistent influences because
Element, so as to restore the relativity of real surface below mist.
2. applications distances bias exponent is in the entire scope and subrange of multispectral image
(1) the applications distances bias exponent in the range of general image：
If the high width of original multispectral image is respectively W and H, it is respectively j and i to define row number and line number, then in row side
Upward minimum gray value and maximum value is respectively XjMINAnd XjMAX；Minimum gray value and maximum value in the row direction be respectively
YiMINAnd YiMAX, the gray value for calculating unit pixel point in image is DNj,i, deviate the distance when forefront and current line minimum value
It is represented by：
xdmj=DNj,i-XjMIN
ydmi=DNj,i-YiMIN (5)
According to range deviation exponential formula defined above, range deviation of the unit pixel point (j, i) in entire image
Index can be expressed as：
In order to make unit pixel point that there is identical surface feature background in entire image, all row and column minimum values are taken respectively
Mean value as fixed value replace, i.e., above formula can be converted into：
Above formula (7) is the range deviation index applied to general image.
As shown in figure 4, raw video (Fig. 4 (a)) is by obtaining after the range deviation exponent arithmetic for being directed to general image
As a result as shown in (Fig. 4 (b)), fog-zone domain restrains oneself, and the influence of mist is weakened, and part earth's surface and shadow region highlight
Come.But, however it remains there are many lines in segmental defect, i.e. image, and this is mainly due to the single-row gray value of uniline is minimum
Be worth it is inconsistent caused by.And the place of mist thickness will appear clustering phenomena, therefore single pixel point is relative to entire image,
Range deviation index can change presentation region concentration phenomenon with earth's surface, and (such as adjacent multirow or multiple row will appear more consistent block
Shape phenomenon).Based on features described above, need to calculate range deviation index in multispectral image subrange.
(2) the applications distances bias exponent in the range of topography：
Range deviation index is applied in the subrange of multispectral image by being improved realization to formula (7).I.e.
Image reduces columns and rows, and centered on first point (x, y) in image, left and right carries out up and down in the x and y direction respectively
Extension, extended distance is set as line, takes minimum gray value in the distance as background value, be denoted as respectively min (LineX) and
Min (LineY), then formula (7) be converted into：
Formula obtains Δ f in (8)i(x, y) is that range deviation index is applied in the subrange of multispectral image.
If Fig. 4 (c) is shown in subrange using the result images obtained after range deviation index, can therefrom see
Go out the more uniform mist part of distribution to be almost eliminated entirely, shade and real surface refer to compared in entire scope applications distances deviation
What the image that number obtains restored becomes apparent, and remain the integral color of image, and shortcoming is thicker in mist distribution
, there is mist clustering phenomena in region and the marginal portion adjacent with normal region.But in general, it is applied according to algorithm principle
Range deviation index in subrange reflects the correlation of normal earth's surface indirectly, so being chosen at image in the present embodiment
The mode of application range deviation index is used to next obtain coarse mist thickness image in subrange.
3. obtain mist thickness image
Due to sensor distance earth's surface farther out, it is assumed that the distance of scenario objects relative sensors is identical, is intended to
Conventional radiation mode is shown below：
Lλ=L0+HR (9)
Wherein, LλRepresent the radiance that sensor receives, L0Represent the sum of earth surface reflection energy and atmospheric path radiation,
HR then represents the contribution margin of mist.
Again since DN values and the radiance of original remote sensing image have a linear relationship, institute's above formula can abbreviation be formula
(10)：
Wherein, DNi' (x, y) represent pixel value at raw video the i-th wave band point (x, y), DNi 0After (x, y) is correction
Pixel value, HTMi(x, y) is corresponding mist thickness image.
It can reflect the mutual pass of real surface below mist in a certain range according to range deviation index defined above
System, therefore, coarse mist thickness image HTM can be obtained by the range deviation exponential formula (8) in subrangei(x,y)
For：
HTMi(x, y)=DNi'(x,y)-DNi'(x,y)·Δfi(x,y) (11)
Preliminary coarse mist thickness image is obtained by above-mentioned formula, as shown in Fig. 5 (a), in the image in addition to comprising
Part earth's surface reflected energy is further included caused by algorithm takes the single-row or uniline window of certain distance on x, y direction
The Filamentous dark window of cross, it is therefore desirable to which further process of refinement is carried out to coarse mist thickness image.
4. process of refinement is carried out to coarse mist thickness image
Based on coarse mist thickness image obtained above, in order to cut down earth's surface included in coarse mist thickness image
Reflective information needs to carry out process of refinement to image, and the process of refinement basic thought taken in this implementation is：By not weighing
Folded window obtains dark pixel image, and dark pixel image is filtered.Specific method is as follows：
Assuming that coarse mist thickness chart image width height is respectively W and H, define in image not overlaid windows (i.e. by coarse mist
Thickness image is divided into integer block window) size for ω × ω originally implement from it is middle selection ω=3, then the dark pixel image obtained
DMiThe size of (x', y') is that W/ ω and H/ ω are：
Wherein, Θ is represented with point (x' ω, y' ω) as starting point, and nonoverlapping window size is ω × ω in image
Window.
In order to eliminate the edge effect in fog-zone domain and normal region, medium filtering is used to dark pixel image, then after filtering
It retrieves shown in mist thickness image such as Fig. 5 (b), for the Filamentous dark window of cross occurred in mist thickness image subregion,
A filtering operation need to be carried out again, and here equally using medium filtering, window size ω is set as 9 in the present embodiment.Pass through filtering
After obtain result such as Fig. 5 (c).
The earth's surface included in coarse mist thickness image can be cut down to a certain extent by above-mentioned filtered image
Reflective information.
Then, it is assumed that raw video has the pixel not influenced by mist, is defined as minDNi', therefore the mist of refinement is thick
Degree image subtracts the gray value minimum value of corresponding wave band raw video, is not processed when difference is negative.Final optimization pass
Mist thickness image is defined as HTMi' (x, y), and then obtain the original multispectral satellite image after defogging and be：
Experimental result and analysis：
Experiment 1：
In order to verify the validity of the above method, according to different images spatial resolution, chosen respectively such as figure Landsat8
OLI 30m resolution ratio Henan Province Dengfeng City region multispectral image, June 7 2013 time；GF-wfv2 high score No.1s are wide to be regarded
Field 15m resolution ratio Wuxi City, Jiangsu Province region multispectral image, in April, 2013 time and ZY-35.8m resolution ratio Beijing
Region multispectral image, in November, 2012 time, more than three width images tested.For the thick fog of local a small range distribution
Or uniform-mist, the extended distance line of range deviation index are set as 15, image size is 400 × 400.Fig. 6 (a) (b)
(c), Fig. 7 (a) (b) (c) and Fig. 8 (a) (b) (c) represent Landsat8 OLI, GF-wfv2 and ZY-3 multispectral images respectively
Raw video, become more meticulous after blue wave band mist thickness image and result images.
For testing picture ZY-3, obtained from Fig. 8 (a) and 8 (c) when mist distribution than it is more uniform when, regional area intrinsic fog
Contribution margin may be considered similar, therefore range deviation index can detach these consistency influence factors, make the whole of image
Body colour tune is consistent, and reaches good calibration result；When mist is more serious, picture Landsat8 OLI and GF- are such as tested
Wfv2, at this point, range deviation index while consistency influence factor is eliminated also can the region thicker to mist cut down, also
Former beneath portions earth's surface.In addition, for, due to the shade that contrast generates, range deviation index is also with fine in raw video
Reduction effect, such as Fig. 6 (c) upper left corners and Fig. 7 (c) lower left corner regions.
Experiment 2：
The method of the present invention not only effectively can influence more serious situation for haze, and with other prior arts
Method compared to also have significant effect.For large range of region in experiment 2, the earth's surface kind included by it
Class is various, has many high brightness earth's surfaces, such as loessland at this time.When extended distance line is smaller, high brightness earth's surface also can
There is correction over-education phenomenon, therefore need to be enlarged its extended distance line accordingly when handling such situation, set
It is 30, image is dimensioned to 1000 × 1000.Here it is compared with prior art AHOT methods, the original substantially of AHOT methods
It manages to carry out mist Thickness sensitivity by tri- wave bands of RGB, the removal of mist, lower Fig. 9 to figure is carried out using Cloud point bearing calibrations
11 provide the two methods result of three width images and blue wave band histogram variation respectively.
1 video quality evaluation parameter list of table
It can be obtained from Fig. 9 (b), Figure 10 (b) and Figure 11 (b), the requirement selection of AHOT algorithms is not influenced normal by mist
Region and thicker cloud sector domain there are artificial disturbing factor, and require the correlation with height between blue red wave band, school
Positive result can remove the influence of most of mist, but the tone of image can be made to change, and occur in addition with some spots；
The method of the present invention such as Fig. 9 (c), Figure 10 (c) and Figure 11 (c) are while integral color is retained, for eliminating in raw video
Consistency influence factor is more prominent.From histogram as can be seen that its blue wave band histogram of the result images of two methods
Initial point is more close with raw video, i.e., the dark pixel retained in image to a certain extent is constant so that is eliminating the same of mist
When, retain excessive earth surface reflection energy as much as possible, prevent excessive details from lacking.
Table 1 provides the quantitative parameter index before and after two methods, wherein, the original in table represent original image,
AHOT represents the result images obtained using AHOT methods, Ours represents the result images obtained using the method for the present invention.Work as mist
Influence when being weakened the mean value of general image can be reduced；Gray scale after the more big then defogging of standard deviation between each pixel etc.
Grade is more disperseed, and the difference between each pixel reduces；Average gradient is bigger, and the clarity of image is higher, and two kinds of correction results are more former
Beginning image increases, and the method for the present invention is less than normal compared with AHOT, and main cause may be that context of methods is eliminating the one of mist
Cause property also weakens the reflected energy of part earth's surface while influence, but this has no effect on the clarity of earth's surface image.Pass through
The results show of multi-source remote sensing satellite image, context of methods preferably, have centainly on the tone for preserving original image
Universality.
The present invention carries out intermediate value when carrying out process of refinement to rough mist thickness image by choosing the dark first image of acquisition
The mode of filtering, the method are only preferred embodiment, and the fine of other filtering modes alternatively equally may be used
Change is handled, such as gaussian filtering.
Under the thinking provided in the present invention, to above-mentioned implementation by the way of being readily apparent that those skilled in the art
Technological means in example is converted, is replaced, is changed, and is played the role of and the basic phase of relevant art means in the present invention
Goal of the invention that is same, realizing is also essentially identical, and the technical solution formed in this way is finely adjusted above-described embodiment to be formed, this
Kind technical solution is still fallen in protection scope of the present invention.
Claims (3)
1. multispectral satellite image range deviation index defogging method, which is characterized in that step is as follows：
Step 1, it is inclined according to the difference setpoint distance in the domain containing fog-zone of original multispectral satellite image and the gray value of earth surface area
Poor index；The expression formula of setpoint distance bias exponent is as follows：
Wherein,During to assume normal earth's surface as image backdrop pels, the true gray value at point (x, y)；DNi'
(x, y) for mist influence area as image prospect pixel when, the pixel gray value at current point (x, y)；I represents wave band；It theoretically represents the pixel value after defogging, is unknown parameter；Then, the range deviation index is applied in original
Begin the subrange of multispectral satellite image, and makes improvements；Improved range deviation exponential expression is：
Wherein, in original multi-spectral Satellite Images, centered on current pixel point (x, y), respectively in the x and y direction or so
Extended up and down, extended distance is set as Line, and the minimum gray value in the distance is taken to be denoted as min respectively as background value
(LineXi(x, y)) and min (LineYi(x, y)), i is expressed as wave band；DN'i(x, y) is when mist influence area is as original more
During spectrum image prospect pixel, the pixel gray value at current pixel point (x, y)；
Step 2, according to above-mentioned improved range deviation index by the way that coarse mist thickness image HTM is calculatedi(x,y)；
HTMiThe expression formula of (x, y) is：
HTMi(x, y)=DNi'(x,y)-DNi'(x,y)·Δfi(x,y)
Wherein, DNi' (x, y) represent pixel value at raw video the i-th wave band point (x, y)；Δfi(x, y) refers to for range deviation
Number；
Then to described image HTMi(x, y) carries out process of refinement, the mist after removal becomes more meticulous from original spectrum satellite image
Thickness image, so as to fulfill the defogging to original multispectral satellite image.
2. multispectral satellite image range deviation index defogging method according to claim 1, which is characterized in that step 2
In to coarse mist thickness image HTMiThe process of refinement of (x, y) is specially：In coarse mist thickness image HTMiIn (x, y)
The not overlaid windows that size is ω × ω is defined, then, obtains dark pixel image DMi(x', y') is：
Wherein, Θ is represented with point (x' ω, y' ω) as starting point；
Then, medium filtering is carried out to the dark pixel image of acquisition.
3. multispectral satellite image range deviation index defogging method according to claim 1, which is characterized in that fine
Mist thickness image after change also needs to be modified, and then obtains the original multispectral satellite image after defogging, then corrects defogging
The expression formula of original multispectral satellite image afterwards is as follows：
Wherein, minDNi' (x, y) be the presence of the pixel that is not influenced by mist in original multispectral image.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN103824260A (en) * | 2013-12-20 | 2014-05-28 | 中国科学院遥感与数字地球研究所 | High-resolution remote sensing image mist rapid removal technology |
| CN104966273A (en) * | 2015-05-29 | 2015-10-07 | 昆山数字城市信息技术有限公司 | DCM-HTM haze-removing method suitably used for optical remote sensing images |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN104966273A (en) * | 2015-05-29 | 2015-10-07 | 昆山数字城市信息技术有限公司 | DCM-HTM haze-removing method suitably used for optical remote sensing images |
Non-Patent Citations (3)
| Title |
|---|
| 《A Novel Fast Dehazing Algorithm in Driver Assistance System》;Che-Chung Lin等;《2011 6th IEEE Conference on Industrial Electronics and Applications》;20111231;第1560-1563页 * |
| 《不良条件下视觉感知增强技术研究与应用》;段世杰等;《中国优秀硕士学位论文全文数据库 信息科技辑》;20151015(第10期);第I138-394页 * |
| 《应用暗原色先验规律的遥感影像去雾技术》;王时震等;《测绘科学技术学报》;20110331;第28卷(第3期);第182-186页 * |
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