CN103942769B - A kind of satellite remote-sensing image fusion method - Google Patents

A kind of satellite remote-sensing image fusion method Download PDF

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CN103942769B
CN103942769B CN201310667191.2A CN201310667191A CN103942769B CN 103942769 B CN103942769 B CN 103942769B CN 201310667191 A CN201310667191 A CN 201310667191A CN 103942769 B CN103942769 B CN 103942769B
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CN103942769A (en
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余顺超
卢敬德
刘超群
余文波
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Pearl River Hydraulic Research Institute of PRWRC
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Abstract

The invention discloses a kind of satellite remote-sensing image fusion method, method is: a width is had to the multi-spectrum remote sensing image of near infrared band, red spectral band, green light band, blue wave band, calculate Natural color band combination intensity I, near infrared band intensity and the ratio R of Natural color band combination intensity I, normalized differential vegetation index NDVI respectively, then set up a basic enhancing operator k according to ratio R1, set up feature selection operator k according to NDVI2With feature smoothing operator k3, k1、k2And k3Product constitute spectrum comprehensive compensation coefficient S, then band value each in image is all multiplied by (1+S), then the result obtained is the result after each Band fusion.The present invention data source with near infrared band as enhancement process, devising the enhancing operator of explicit physical meaning, remote sensing image carries out selectable Feature Fusion enhancing, method is simple, computing is quick, it is adaptable to all multiband satellite images with near-infrared, red, green, blue spectroscopic data.

Description

A kind of satellite remote-sensing image fusion method
Technical field
The present invention relates to Remote sensing image processing research field, merge particularly to a kind of satellite remote-sensing image Method.
Background technology
The Remote sensing image enhancing of broad sense include the radiation correcting of image, band combination, band math, filtering, The image processing techniquess such as denoising, color transformed, rectangular histogram regulation.The image enhaucament of narrow sense refers in particular to through spoke Penetrate correction, the image of band combination carries out the process of rectangular histogram regulation.Traditional Imaging enhanced method has segmentation Linear stretch, regulation brightness/contrast regulation, color balance, color range and curve adjustment etc..At Imaging enhanced Processing procedure should be followed certain principle, mainly include following some: (1) diversity principle: image increase Strength reason is with the difference between prominent different atural objects as basic principle.(2) clarity principle: dividing according to image Resolution, the remote sensing figure of the corresponding proportion chi of making is the most apprehensive.(3) suitable neutral principle: improving highlighted atural object On the basis of dull atural object contrast, the original color of reservation as much as possible, texture, contrast, especially The details of highlighted atural object and the level difference of dull atural object, seek entirety between color, brightness, contrast Balance.
Remote Sensing Image Fusion is the latest development of tradition image enhancement technique, is the one of Remote Sensing Image Processing Technology Individual know-how field, has been developed serial of methods at present.May be roughly divided into from the point of view of merging principle Three below base class:
1. fusion method based on signal analysis principle: by high-pass filtering, principal component analysis (PCA), Fu Vertical leaf transformation (FFT), small echo (Wavelet) conversion, Gram-Schimdt conversion, Curvelet conversion etc. Signal processing technology is applied to data fusion, forms corresponding integration technology and method.
2. based on the fusion method that color space is theoretical: by main colour model RGB, CMYK, Lab, IHS(HSB), HSV etc. is applied to image co-registration, is IHS(HSB in the method that remote sensing circle is conventional at present) Conversion is merged.
3. based on Mathematical Statistics Analysis, the fusion method of four arithmetic operation: fusion side based on Mathematical Statistics Analysis Method mainly has regression analysis to merge.Method based on four arithmetic operation, mainly includes ratio method, differential technique, adds Power superposition, multiple amplify and four fundamental rules hybrid operation way.In this kind of method, classical method is that Brovey melts Conjunction method and CN fusion method.
For the data object of integration technology applied research, generally comprise following several types: high-resolution Full-colour image and Multispectral Image Fusion, optical imagery and SAR image fusion, EO-1 hyperion or multispectral image Merge, multi-temporal image merges.
For multispectral data, tradition Enhancement Method lays particular emphasis on the external effect of Imaging enhanced, uses more Man-machine interaction mode realizes, and its achievement is generally unsuitable for quantitative spectrochemical analysis application.Merge Enhancement Method general Having clear and definite algorithm, be suitable to computer and automatically process, enhancing achievement is also beneficial to follow-up quantitative spectrometric and divides Analysis application.
Natural color (true color) image data has between wave band that dependency is strong, blue wave band is disturbed shadow by steam Ring the inherent shortcomings such as spectral response is partially strong, vegetation spectral response is relatively weak, cause Natural color (true color) Combination image shows that stereovision is strong, brightness and definition the highest, some of major surface features Natural color from So, the external shortcoming such as true color is untrue, a very long time, it almost became uncared-for application model.
Along with the development of satellite remote sensing technology, high-resolution satellite image data become increasingly abundant.Image resolution Raising compensate for the disadvantages mentioned above of Natural color (true color) image, high-resolution simultaneously to a certain extent Image wave band is less, and the band combination mode of colored synthesis image is limited, Natural color (true color) image by Gradually become the prevailing model of high-resolution color resultant image application.
While it is true, the inherent shortcoming of Natural color (true color) image, external shortcoming remain unchanged, seriously Have impact on its application effect.Directly use the general enhancement techniques such as conventional histogram regulation can be to a certain degree On overcome its external shortcoming, but difficulty is big, and efficiency is low, and has very high point with experience and the cognition of operator System；To be used for overcoming its inherent shortcoming the most hardly possible.Therefore, matching in color, level how is obtained Clearly, Natural color (true color) the Imaging enhanced result that details is abundant becomes the pass that successive image processes Key step.
Summary of the invention
Present invention is primarily targeted at the shortcoming overcoming prior art with not enough, it is provided that a kind of satellite remote sensing shadow As fusion method, the method changes the dependency between each wave band of image of satellite remote sensing field, is effectively improved The abundant information degree of this combination, and improve the data structure of image, reduce the difficulty of its post processing, Enhanced image can meet visual analysis and quantitative spectrochemical analysis application simultaneously, has high promotional value.
The purpose of the present invention is realized by following technical scheme: a kind of satellite remote-sensing image fusion method, should Method is: have near infrared band (NIR wave band), red spectral band (R wave band), green light band for a width The multi-spectrum remote sensing image of (G-band), blue wave band (B wave band), calculates Natural color band combination respectively Intensity I, near infrared band intensity and the ratio R of Natural color band combination intensity I, normalized differential vegetation index NDVI, Then a basic enhancing operator k is set up according to ratio R1, set up a feature selection operator according to NDVI k2With a feature smoothing operator k3, k1、k2And k3Product constitute spectrum comprehensive compensation coefficient S, then will In image, each band value is all multiplied by (1+S), then the result obtained is the result after each Band fusion；
Basic enhancing operator k1Computational methods be: k1=R-c1；Wherein c1An eigenvalue for R；
Feature selection operator k2Computational methods be: k2=δ；Wherein, it is more than vegetation with non-when the value of NDVI The classification thresholds c of vegetation2Time, then δ value is 1, and otherwise δ value is 0；
Feature smoothing operator k3Value equal to NDVI.
The present invention is directed to the dependency between Natural color (true color) the combination each wave band of image, information redundancy is many Etc. fault of construction, introduce the outside enhancing source near infrared band data source as enhancement process, (true to Natural color Colored) combination visual fusion enhancing；It is unfavorable for that subsequent quantitation analysis is applied for traditional enhancement techniques, and straight Scoop out and easily cause again waters etc. by existing fusion method feature atural object Natural color is unnatural, true color is untrue Deng shortcoming, use for reference the expression-form of the image interfusion method such as IHS, devise a basic enhancing operator k1。 For Natural color (true color) image, the feature atural object such as image stereovision is strong, the highest, the vegetation of definition The external defects such as Natural color is unnatural, true color is untrue, draw with reference to the piecewise linearity in traditional enhancement techniques The core concept stretched, devises an enhancing operator k for image feature selection2.Meanwhile, in order to eliminate Picture noise that feature selection causes increases, and devises one for the smooth enhancing operator k of image color3。 By calculating spectrum comprehensive compensation coefficient, it is thus achieved that spectrum merges achievement.
Concrete, the computational methods of described Natural color band combination intensity I are as follows:
I = b R + b G + b B 3 ;
Wherein, bR、bG、bBIt is respectively the red, green, blue band strength inputting image.
Concrete, the calculating side of the ratio R of described near infrared band intensity and Natural color band combination intensity I Method is as follows:
R = b NIR I ;
Wherein, bNIRFor inputting the near infrared band intensity of image.
Concrete, the computational methods of described normalized differential vegetation index NDVI are as follows:
NDVI = b NIR - b R b NIR + b R ;
Wherein, bNIR、bRIt is respectively the near infrared band intensity of input image and red band strength.
Preferably, described foundation strengthens operator k substantially1Time selected c1=Rmin, based on this feature value, k1 Value use following methods obtain:
k1=R-Rmin；
Wherein, R is the ratio of near infrared band intensity and Natural color band combination intensity I, RminRefer to image The minima of R in all pixels.
Preferably, described feature selection operator k is set up2Time selected vegetation and non-vegetation classification thresholdsNDVImaxRefer to the maximum of NDVI, c in all pixels of image2According to The histogram feature of NDVI determines with image vegetation characteristics check analysis.
Further, described vegetation and the classification thresholds c of non-vegetation2Value is 0.
Present invention introduces feature smoothing operator k3, mainly ensure the color that enhanced image is adjacent between looking for Color transition is naturally smooth.Test shows, directly uses the kronecker δ function that the noise of image can be caused to increase, Original image from vegetation to bare rock, soil, artificial structure is again to the color nature Interim of water body Destroyed, especially in the intermediate zone from land to water body, can shape by the intermediate zone to sparse vegetation from dense planting Become obvious noise (speckle), break the balance transition between original true color image atural object.And on using State smoothing operator and can effectively overcome disadvantages mentioned above.
The present invention compared with prior art, has the advantage that and beneficial effect:
1, for having the visible ray satellite remote-sensing image of near-infrared, red, green, blue wave band, owing to it lacks " temperature " information, than TM, ETM mono-" temperature, brightness, green degree, humidity " four of data of class Dimension structure is few one-dimensional, the only three dimensional structure of " brightness, green degree, humidity ", its Natural color (true color) Between 3 visible light wave ranges (red wave band, green wave band, blue wave band) of combination, dependency is the highest, its information Redundancy is the highest, and near infrared band is the lowest with their dependency, and its information has the biggest independence. This data are in its three band combinations chromatic image, and the information that Natural color (true color) combines is the abundantest. The fusion object of the present invention is multispectral data, and fusion PCR is characterized level and merges, and fusion method is based on color The fusion method of color space theory is comprehensive with four arithmetic operation fusion method based on statistical analysis, uses for reference simultaneously The thought that piecewise linearity in conventional enhancement process strengthens.The inventive method with near infrared band for enhancing at The data source of reason, substantially strengthen operator, feature selection operator and the feature that devise explicit physical meaning smooth Operator, carries out selectable Feature Fusion enhancing to Natural color (true color) image, and method is simple, computing Fast, it is adaptable to all multiband satellite images with near-infrared, red, green, blue spectroscopic data, also fit For by various geometry strengthen fusion method (as principal component analysis (PCA), Fourier transform (FFT), Small echo (Wavelet) conversion, Gram-Schimdt conversion, Curvelet conversion) obtain near-infrared, red, Multiband satellite image green, blue.
2, Natural color (true color) image is highly susceptible to the interference of steam in air due to its blue wave band so that it is The object spectrum obtained is unstable, according to correlational study, the brightness value actual atural object measured light of blue wave band Spectrum is bigger than normal so that blue layer coverd with by image so that overall image is the most blue.Image vegetation is the darkest simultaneously Light, and some atural object such as direct reflection such as building and bare rock is strong, brightness is the highest, dim atural object with Highlighted both atural object forms powerful contrast, and colors of image is poor with atural object actual color concordance, some features Atural object Natural color is unnatural, and true color is untrue.Enhancing achievement obtained by the inventive method is that spectrum merges Achievement, reinforced effects is notable, effectively improves Natural color (true color) visual data arrangement, improves certainly The so definition of color (true color) image, stereovision, color balance, abundant information degree.
3, the wave band feature of true color image and ground object structure feature, determines directly by traditional enhancement techniques It is easy to attend to one thing and lose sight of another as rectangular histogram regulation etc. carries out process.When global feature difference strengthens, some local Feature difference may disappear because of enhancing.Enhance dim atural object, then have lost the details of highlighted atural object； Remaining the details of dim atural object, the chaos that can cause again dim atural object is unclear.Strengthen in global feature difference While the application software of local detail feature, traditional theory and some maturations can be kept to provide segmented line Property stretching thinking and means, but intractability is big.Thus general true color image processes, main or with It is main for retaining dim atural object, does not actually make too much Imaging enhanced, and reinforced effects is limited.Side of the present invention Method passes through spectrally compensating computing, and Natural color (true color) combination image carried out Feature Fusion enhancing, and with Merge the image before strengthening to compare, improve its data structure, reduce the difficulty of its post processing, significantly carry Its treatment effeciency high；Enhanced data are carried out the process such as traditional rectangular histogram regulation and is readily available mesh Depending on effect preferable Natural color (true color) image
4, the inventive method gained fusion results both can have been processed by normal image further and obtain high-quality nature Color (true color) image, at the bottom of Natural color (true color) image that the offer of remote sensing image visual interpretation is excellent Figure, it is also possible to for Natural color (true color) image quantitative analysis provide spectrum fused data, be conducive to into One step quantitatively obtains multiple thematic information by spectrum analysis.For application angle, the present invention has expanded and has defended The multiple target application spaces such as star remote sensing Natural color (true color) image visualization analysis and quantitative analysis, for expert With the different remote sensing image user such as popular, visual analysis user and quantitative resolution user, for computer network The remote sensing image application platform offer that platform, cell phone platform, GPS platform, printer output platform etc. are different A kind of the newly selected, contribute to promoting remote sensing image commercialization, popular application and the popularization and application of remote sensing technology. Particularly can effectively promote the extensive application of domestic satellite data.
Accompanying drawing explanation
Fig. 1 is the flow chart of the inventive method.
Fig. 2 is the remote sensing image exemplary plot of input in the present embodiment 1.
Fig. 3 is the image of Natural color band combination intensity I corresponding for Fig. 2.
Fig. 4 is the image of ratio R corresponding for Fig. 2.
Fig. 5 is normalized differential vegetation index NDVI image corresponding for Fig. 2.
Fig. 6 is the image of spectrum comprehensive compensation coefficient S corresponding for Fig. 2.
Fig. 7 is the Natural color image after Fig. 2 merges.
Detailed description of the invention
Below in conjunction with embodiment and accompanying drawing, the present invention is described in further detail, but the embodiment party of the present invention Formula is not limited to this.
Embodiment
The present embodiment with a width as shown in Figure 2, there is near infrared band (NIR), red spectral band (R), Fusion method in the present invention is described by green light band (G), the remote sensing image of blue wave band (B). Particular flow sheet is as shown in Figure 1.
1, input remote sensing image
Open a width and there is near infrared band (NIR), red spectral band (R), green light band (G), blue light ripple The multi-spectrum remote sensing image of section (B), as in figure 2 it is shown, be 1% drawing effect figure.
2, each basic parameter that fusion strengthens is calculated
Merge each basic parameter strengthened to specifically refer to: Natural color band combination intensity I, near infrared band intensity Ratio R, normalized differential vegetation index NDVI with Natural color band combination intensity I.Each parameter specifically calculated Journey is as follows.
For Natural color band combination intensity I,
I = b R + b G + b B 3 ;
Wherein, bR、bG、bBIt is respectively the red, green, blue band strength inputting image.Result of calculation is shown in Fig. 3.
Ratio R near infrared band intensity Yu Natural color band combination intensity I:
R = b NIR I ;
Wherein, bNIRFor inputting the near infrared band intensity of image.Result of calculation is shown in Fig. 4.
For normalized differential vegetation index NDVI:
NDVI = b NIR - b R b NIR + b R ;
Wherein, bNIR、bRIt is respectively the near infrared band intensity of input image and red band strength.Result of calculation See Fig. 5.
3, spectrum comprehensive compensation coefficient is calculated
Spectrum comprehensive compensation coefficient S is basic enhancing operator k1, feature selection operator k2With feature smoothing operator k3Product, calculate gained S image see Fig. 6.
Operator k is strengthened for basic1, it is as follows that it calculates process:
k1=R-c1；
Wherein, R is the ratio of near infrared band intensity and Natural color band combination intensity I, c1For R one Eigenvalue, c 1 ∈ [ R min , R max ] ⊆ [ 0 , R max ] , Typically take c1=Rmin。
For the feature selection operator k for feature selection2, it is as follows that it calculates process:
k2=δ；
Wherein, δ value 0 or 1, when the value of NDVI is more than vegetation and the classification thresholds c of non-vegetation2Time,Then δ value is 1, and otherwise δ value is 0.c2Rectangular histogram according to NDVI is special Levying and determine with image vegetation characteristics check analysis, general value is 0.
K is smoothed for the feature smooth for feature3, it is as follows that it calculates process:
k3=NDVI。
4, image co-registration
Set up band math expression formula (1+S) * b calculating Natural color (true color) and combine the enhancing image of each wave band, Wherein b is each wave band that Natural color (true color) combines, and operation result is each wave band of enhanced correspondence. Fig. 7 is shown in by Natural color image after fusion.
5, storage is merged and is strengthened achievement
By each for the most enhanced correspondence band image sealed storage respectively.
Image each wave band brightness, the statistical table of textural characteristics before and after 1 example fusion of table
2 examples of table merge before and after correlation coefficient and the statistical table of comentropy between image wave band
Table 1 is image each wave band brightness, the statistical table of textural characteristics before and after fusion, and the data from this table can Draw: image greyscale intermediate value, average totally improve, illustrate that image overall brightness strengthens；Standard deviation reduces, Illustrate that this method effectively improves vegetation and other atural object luminance contrast while improving vegetation overall brightness； Gradient absolute value totally strengthens, and illustrates that the texture of image, level, details become apparent from.This with merge before and after Improvement of visual effect be consistent.
Table 2 be merge before and after correlation coefficient and the statistical table of comentropy, the data from this table between image wave band Can draw: correlation coefficient overall reduction, the entropy of each wave band generally increases with the combination entropy of resultant image, explanation Merge and improve the data relation of true color (Natural color) image, and wave band and the information of resultant image Measure abundanter, be conducive to merging the further quantitative Treatment of achievement and analysis.
Above-described embodiment is the present invention preferably embodiment, but embodiments of the present invention are not by above-mentioned reality Execute the restriction of example, the change made under other any spirit without departing from the present invention and principle, modification, Substitute, combine, simplify, all should be the substitute mode of equivalence, within being included in protection scope of the present invention.

Claims (6)

1. a satellite remote-sensing image fusion method, it is characterised in that for a width have near infrared band, Red spectral band, green light band, the multi-spectrum remote sensing image of blue wave band, calculate Natural color band combination respectively Intensity I, near infrared band intensity and the ratio R of Natural color band combination intensity I, normalized differential vegetation index NDVI, then sets up a basic enhancing operator k according to ratio R1, set up a feature choosing according to NDVI Select operator k2With a feature smoothing operator k3, k1、k2And k3Product constitute spectrum comprehensive compensation coefficient S, Then band value each in image is all multiplied by (1+S), then the result obtained is the result after each Band fusion；
Basic enhancing operator k1Computational methods be: k1=R-c1；Wherein c1An eigenvalue for R；c1=Rmin, Based on this feature value c1, k1Value use following methods obtain:
k1=R-Rmin；
Wherein, R is the ratio of near infrared band intensity and Natural color band combination intensity I, RminRefer to image The minima of R in all pixels；
Feature selection operator k2Computational methods be: k2=δ；Wherein, it is more than vegetation with non-when the value of NDVI The classification thresholds c of vegetation2Time, then δ value is 1, and otherwise δ value is 0；
Feature smoothing operator k3Value equal to NDVI.
Satellite remote-sensing image fusion method the most according to claim 1, it is characterised in that described nature The computational methods of color band combination intensity I are as follows:
I = b R + b G + b B 3 ;
Wherein, bR、bG、bBIt is respectively the red, green, blue band strength inputting image.
Satellite remote-sensing image fusion method the most according to claim 2, it is characterised in that described the reddest Outer band strength is as follows with the computational methods of the ratio R of Natural color band combination intensity I:
R = b N I R I ;
Wherein, bNIRFor inputting the near infrared band intensity of image.
Satellite remote-sensing image fusion method the most according to claim 2, it is characterised in that described normalizing The computational methods changing vegetation index NDVI are as follows:
N D V I = b N I R - b R b N I R + b R ;
Wherein, bNIR、bRIt is respectively the near infrared band intensity of input image and red band strength.
Satellite remote-sensing image fusion method the most according to claim 1, it is characterised in that described foundation Feature selection operator k2Time selected vegetation and non-vegetation classification thresholds NDVImaxRefer to the maximum of NDVI, c in all pixels of image2Histogram feature according to NDVI with Image vegetation characteristics check analysis determines.
Satellite remote-sensing image fusion method the most according to claim 5, it is characterised in that described vegetation Classification thresholds c with non-vegetation2Value is 0.
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