CN109389063A - Remote sensing image Strip noise removal method based on wave band correlation - Google Patents

Remote sensing image Strip noise removal method based on wave band correlation Download PDF

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Publication number
CN109389063A
CN109389063A CN201811131117.8A CN201811131117A CN109389063A CN 109389063 A CN109389063 A CN 109389063A CN 201811131117 A CN201811131117 A CN 201811131117A CN 109389063 A CN109389063 A CN 109389063A
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wave band
classification
banded improvement
remote sensing
image
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CN109389063B (en
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尚志鸣
王哲
张绪国
吴立民
钟灿
文高进
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Beijing Institute of Space Research Mechanical and Electricity
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Beijing Institute of Space Research Mechanical and Electricity
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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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/30—Noise filtering

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Abstract

A kind of remote sensing image Strip noise removal method based on wave band correlation comprises the following steps that Step 1: calculating its all band and wave band B to be denoised in imagedBetween related coefficient, using related coefficient greater than given threshold R several wave bands as wave band to be sorted, unsupervised classification is carried out to it, obtains classification chart Bm；Step 2: utilizing classification chart BmTreat denoising wave band BdIn Banded improvement row carry out the bearing calibration of spatial domain match by moment branch handle；Traverse BdAll noise bars of full figure, are removed the Banded improvement in whole picture image.Of the invention goes Banded improvement method, faster can accurately handle the differently other Banded improvement of species, improve the quality of optical remote sensing image, expands its application range, and computational efficiency is high, and robustness is strong.

Description

Remote sensing image Strip noise removal method based on wave band correlation
Technical field
It a kind of is gone the invention belongs to remote sensing image technical field of information processing more particularly to based on wave band correlation Except remote sensing image Banded improvement method.
Background technique
With the development of remote sensing image acquisition technique, optical remote sensing image is all played in scientific research, production application Huge effect.But in remote sensing sensor-based system when obtaining corresponding surface data, the electromagnetic wave energy that is received by it by To the influence comprising a variety of situations such as landform, position of sun, atmospheric conditions, sensor itself performances, so as to cause remote sensing image The actual conditions of earth's surface can not be reacted completely, wherein more serious one kind is exactly Banded improvement (striping noise). Its form of expression be the period or aperiodically occur Banded improvement row and uplink and downlink normal pixel point gray value it is obviously partially dark or It is partially bright.
Banded improvement greatly affected the quality of image, thus to the space Objects extraction of image, Spectral Characteristics Analysis, Space characteristics enhancing and other effects causes serious influence.Especially type of ground objects is single, the uniform surface area of spectral response Domain, such as snowfield, desert, water body area, the influence of Banded improvement become apparent.
It is both at home and abroad two kinds for the processing method on Banded improvement basis: spatial domain processing, frequency domain processing.At spatial domain Reason includes the methods of match by moment and Histogram Matching, and principle simple application range is wide.But this method does not account for atural object Image greyscale variation caused by classification difference, is part caused by directly being handled with neighbouring normal row Banded improvement row Atural object gray correction is excessive, and cultural noise elimination in part is not thorough, not satisfactory to the total quality recovery effects of image, therefore In actual production, it needs to conduct further research it raising.
Summary of the invention
The technical problem to be solved by the present invention is overcome the deficiencies in the prior art, proposes that a kind of wave band correlation that is based on is gone Except the method for remote sensing image Banded improvement.The correlation seen by calculating wave band, using high correlation image to image to be processed Carry out Banded improvement removal, it is contemplated that differently species not under same spectrum range spectral response difference and it is same Different images region is changed by sun angle, atmosphere, component variations bring wave band correlation in scape image.It being capable of effectively needle Banded improvement is removed to different atural objects, there is very high computational efficiency, be readily put into production application.
The technical scheme adopted by the invention is that: a kind of remote sensing image Strip noise removal side based on wave band correlation Method comprises the following steps that
Step 1: calculating its all band and wave band B to be denoised in imagedBetween related coefficient, related coefficient is greater than Several wave bands of given threshold R carry out unsupervised classification as wave band to be sorted, to it, obtain classification chart Bm；
Step 2: utilizing classification chart BmTreat denoising wave band BdIn Banded improvement row carry out spatial domain match by moment correction side Method branch is handled；Traverse BdAll noise bars of full figure, are removed the Banded improvement in whole picture image.
Specific step is as follows for the step 1:
Step 1.1 selects wave band B to be denoiseddThe normal region not influenced by Banded improvement in image is with other not by item Wave band corresponding region with influence of noise calculates related coefficient, determines the threshold value R of related coefficient, and all related coefficients are greater than R Band fusion as wave band to be sorted；
Correlation coefficient r between two wave bands d and rdr:
Wherein, covdrFor the covariance of two wave bands, sdFor the standard deviation of wave band d, srFor the standard deviation of wave band r；
Original image is added as auxiliary spectral coverage, using equal in step 1.2, the gray level co-occurrence matrixes for calculating spectral coverage to be sorted Value drift algorithm classifies to related coefficient greater than the spectral coverage of threshold value R, class categories quantity according to the spectrum of figure to be sorted with The probability distribution of textural characteristics obtains classification chart Bm。
In the step 2, the method and step for carrying out denoising to any Banded improvement row is as follows:
Step 2.1 determines classification chart BmClassification number A, according to classification chart BmTreat denoising wave band BdWhere middle Banded improvement It goes and closes on normal row and traverse pixel-by-pixel, with classification chart BmThe classification of record corresponds, and determines the classification class of each pixel Not；
Step 2.2, will wave band B be denoiseddMiddle Banded improvement row presses different atural object classifications, extracts pixel ash to be processed Angle value separately constitutes pixel array D to be processed1、D2……DA；
Step 2.3, will wave band B be denoiseddThe normal row that middle Banded improvement row closes on, by different atural object classification extraction pictures Vegetarian refreshments gray value separately constitutes normal pixel point array D1′、D2′……D′A；
Any classification pixel array D to be processed that step 2.4, extraction are influenced by Banded improvementiAnd corresponding classification is just Normal pixel array Di', match by moment denoising or Histogram Matching denoising are carried out respectively；
I=1,2,3 ..., A, A are classification chart BmIn atural object classification number.
The threshold value R > 0.85.
The advantages of the present invention over the prior art are that:
(1) method of the invention, can by carrying out unsupervised classification to the higher image of image related coefficient to be processed To distinguish different atural object classifications, then targetedly Banded improvement is not carried out to the differently species of different Banded improvement rows and is gone out Processing traditionally will use the processing of spatial neighbor row to be subdivided into the generic atural object processing of adjacent row by space domain processing method, be promoted Processing accuracy.
(2) method of the invention is classified using the high spectral coverage of correlation, ensures category division and band classes to be denoised Hua Fen not be consistent, without pursuing highest nicety of grading, is handled by classification, in some interior strips, space phase can be broken Adjacent constraint is directly reconfigured to obtain processing data set with classification, tradition is avoided to cause spatial neighbor method windowing processing Computationally intensive problem, promoted calculating speed.
(3) method of the invention can quickly and efficiently remove the Banded improvement in image, so that the quality of image is improved, Expand application range.Of the invention goes Banded improvement method, faster can accurately handle the differently other Banded improvement of species, The quality for improving optical remote sensing image, expands its application range.And it is of the invention go Banded improvement method computational efficiency high, steadily and surely Property is strong.
Detailed description of the invention
Fig. 1 is the removal remote sensing image Banded improvement method flow diagram of the invention based on wave band correlation.
Fig. 2 (a) is the image before denoising；
Fig. 2 (b) is using the image after method denoising of the invention.
Specific embodiment
Invention is further explained with reference to the accompanying drawing.
Having various reactions for spectral radiance is not caused based on differently species, while considering different zones in same scape image By sun angle, atmosphere, the variation of CCD difference bring wave band correlation, the place to go remote sensing shadow based on wave band correlation is proposed Slice band Noise Method.As shown in Figure 1, the key step that the present invention is implemented can be divided into two stages:
Step 1: correlation waveband selection and classification.
1.1) wave band B to be denoised is selecteddIt is not made an uproar by band with other the normal region not influenced by Banded improvement in image The wave band corresponding region that sound shadow is rung calculates related coefficient.In practical applications, the correlation coefficient r between two wave bands d and rdr, this Related coefficient is the covariance cov of two wave bandsdrWith standard deviation product sdsrRatio:
sdFor the standard deviation of wave band d, srFor the standard deviation of wave band r；
In conjunction with the actual conditions of image and the size of related coefficient, the threshold value R of related coefficient is determined, by all related coefficients Band fusion greater than R is as classification wave band.Because higher related coefficient indicates the wave band image and image to be denoised more It is close, more there is reference significance.Then image classification figure B is obtained to the classification that exercises supervision of classification wave bandm。
By classification chart, atural object different classes of on image can be distinguished, the correlation between identical atural object can be established Relationship is convenient for the Strip noise removal for atural object classification.
1.2) it is directed to spectral coverage to be sorted, calculates its gray level co-occurrence matrixes, original image is added as auxiliary spectral coverage, using equal Value drift algorithm classifies to high related coefficient spectral coverage, and class categories quantity is specified automatically by algorithm, obtains classification chart Bm。
Step 2: Strip noise removal is not carried out for differently species.
2.1) classification chart B is determinedmIn type of ground objects classification number A.According to classification chart BmTo with wave band B to be denoiseddMiddle item It is expert at noise and traverses the normal row that closes on used, determine the atural object classification of each pixel.
2.2) pixel for belonging to same atural object classification in Banded improvement row is sorted out, and is stored respectively in array D1、D2……DAIn (A be image classification figure BmIn atural object classification number), if a certain categorical measure is smaller, be incorporated into light In spectrum and the closest classification of texture.
2.3) pixel for belonging to same atural object classification in the normal row adjacent with Banded improvement row is sorted out, and It is stored respectively in array D1′、D2′……D′AIn (A be image classification figure BmIn atural object classification number), if a certain categorical measure compared with It is small, then it is incorporated into spectrum and the closest classification of texture.
2.4) on the pixel array such as D of same atural object classification influenced by Banded improvement1, choose corresponding normal picture Vegetarian refreshments array such as D1', spatial domain correction process is used to this two groups of data, such as match by moment or Histogram Matching.Carrying out space After the correction process of domain, it can be realized for the differently other Strip noise removal of species.D1Corresponding D1', D2Corresponding D2' ... ..., DA Corresponding D 'A, i=1,2,3 ..., A；
2.5) circulation step 2.4), it finishes, is shown in data until the Banded improvement of all atural object classifications is entirely processed That is DAWith D 'ASpatial domain correction process finish.
2.6) all Banded improvement rows are recycled and execute step 2.2)~2.4) complete full figure processing.
The denoising of Modis image, correlation coefficient threshold R=0.98, as shown in Fig. 2 (a), Fig. 2 (b), the noise quilt of noise row Effectively inhibit.
It should be noted that and understand, in the feelings for not departing from the spirit and scope of the present invention required by appended claims Under condition, various modifications and improvements can be made to the present invention of foregoing detailed description.
Unspecified part of the present invention belongs to technology well known to those skilled in the art.

Claims (4)

1. a kind of remote sensing image Strip noise removal method based on wave band correlation, which is characterized in that comprise the following steps that
Step 1: calculating its all band and wave band B to be denoised in imagedBetween related coefficient, by related coefficient be greater than setting threshold Several wave bands of value R carry out unsupervised classification as wave band to be sorted, to it, obtain classification chart Bm；
Step 2: utilizing classification chart BmTreat denoising wave band BdIn Banded improvement row carry out spatial domain match by moment bearing calibration point Row processing；Traverse BdAll noise bars of full figure, are removed the Banded improvement in whole picture image.
2. a kind of remote sensing image Strip noise removal method based on wave band correlation according to claim 1, feature It is:
Specific step is as follows for the step 1:
Step 1.1 selects wave band B to be denoiseddThe normal region not influenced by Banded improvement in image is with other not by Banded improvement The wave band corresponding region of influence calculates related coefficient, determines the threshold value R of related coefficient, and all related coefficients are greater than to the wave band of R Fusion is used as wave band to be sorted；
Correlation coefficient r between two wave bands d and rdr:
Wherein, covdrFor the covariance of two wave bands, sdFor the standard deviation of wave band d, srFor the standard deviation of wave band r；
Step 1.2, the gray level co-occurrence matrixes for calculating spectral coverage to be sorted are added original image as auxiliary spectral coverage, are floated using mean value It moves algorithm and classifies to related coefficient greater than the spectral coverage of threshold value R, class categories quantity is according to the spectrum and texture of figure to be sorted The probability distribution of feature obtains classification chart Bm。
3. a kind of remote sensing image Strip noise removal method based on wave band correlation according to claim 1 or 2, special Sign is:
In the step 2, the method and step for carrying out denoising to any Banded improvement row is as follows:
Step 2.1 determines classification chart BmClassification number A, according to classification chart BmTreat denoising wave band BdMiddle Banded improvement be expert at And close on normal row and traverse pixel-by-pixel, with classification chart BmThe classification of record corresponds, and determines the class categories of each pixel；
Step 2.2, will wave band B be denoiseddMiddle Banded improvement row presses different atural object classifications, extracts pixel gray value to be processed, Separately constitute pixel array D to be processed1、D2……DA；
Step 2.3, will wave band B be denoiseddThe normal row that middle Banded improvement row closes on, by different atural object classification extraction pixels Gray value separately constitutes normal pixel point array D '1、D′2……D′A；
Any classification pixel array D to be processed that step 2.4, extraction are influenced by Banded improvementiAnd the normal pixel of corresponding classification Point array D 'i, match by moment denoising or Histogram Matching denoising are carried out respectively；
I=1,2,3 ..., A, A are classification chart BmIn atural object classification number.
4. a kind of remote sensing image Strip noise removal method based on wave band correlation according to claim 1 or 2, special Sign is: the threshold value R > 0.85.
CN201811131117.8A 2018-09-27 2018-09-27 Remote sensing image strip noise removing method based on wave band correlation Active CN109389063B (en)

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CN110533608A (en) * 2019-08-08 2019-12-03 西安电子科技大学 Image band noise suppressing method and its device, electronic equipment, storage medium
CN110988899A (en) * 2019-12-09 2020-04-10 Oppo广东移动通信有限公司 Method for removing interference signal, depth detection assembly and electronic device
CN111583132A (en) * 2020-04-20 2020-08-25 国家卫星气象中心(国家空间天气监测预警中心) Method, device, equipment and medium for removing abnormal strip noise of remote sensing image

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CN104820972A (en) * 2015-05-07 2015-08-05 北京空间机电研究所 Infrared image ME noise removal method based on on-orbit classification statistics
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CN101916430A (en) * 2010-07-13 2010-12-15 武汉大学 Waveband-correlation-based intra-class local fitting and resorting method of remote sensing image
CN103020913A (en) * 2012-12-18 2013-04-03 武汉大学 Remote-sensing image stripe noise removing method based on segmented correction
CN104021521A (en) * 2014-04-08 2014-09-03 刘健 Stripe removal method of remote sensing images based on Fourier high-pass filtering and focal analysis
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