CN103679648B - A kind of match by moment satellite image Strip noise removal method based on space segmentation - Google Patents

Abstract

A kind of match by moment satellite image Strip noise removal method based on space segmentation, the method by the atural object of different spectral signatures according to average, intermediate value, gradient feature split, and adopt standard match by moment to process each cut zone.The method is for the big remote sensing panchromatic image of data volume, based on basic treatment theory, it is provided that a kind of effective image noise minimizing technology.The method can effectively reconstructed images, improve the quality of image, efficiency of algorithm is also significantly high.

Description

A kind of match by moment satellite image Strip noise removal method based on space segmentation

Technical field

The invention belongs to remote sensing image process field, relate to a kind of match by moment satellite image Strip noise removal method based on space segmentation.

Background technology

Banded improvement is the key factor affecting optical satellite image image quality, it is suppressed that or to remove Banded improvement be one of satellite ground pretreatment basic link of carrying out radiation treatment.The imaging system of optical satellite is due to the interference of internal and external factors, as: the performance of CCD device (charge-coupled image sensor) changes in time, atmospheric interference etc., after carrying out image homogenization relative detector calibration, image still can remain Banded improvement, the existence of this noise like, greatly reducing the definition of image, the follow-up interpretation process for image adds difficulty, it is therefore necessary to rejected.

By being analyzed finding that such Banded improvement has following characteristics to the Banded improvement of residual in image: 1. the locus that noise occurs is random.2. between noise to surrounding atural object, nonlinearity is relevant.

The method removing Banded improvement conventional at present is summed up and can be divided into two classes: a class is the denoising method for image space domain feature extraction；Another kind of is spatial domain and frequency domain are combined, and adopts the method that suitable filter operator removes Banded improvement.Wherein the typical algorithm of spatial domain denoising has the Strip noise removal method of Gadallahd et al. multi-load image based on match by moment proposed, the method requires that gradation of image is homogeneous, and Banded improvement image is adjacent between image and there is higher dependency, this kind of algorithm has good effect for the satellite image that the atural object processing local is single, but when processing the satellite image that view picture atural object enriches, poor effect；Then on this basis, Liu Zhengjun et al. proposes the improvement moment-matching method compensated based on average, the method utilizes the multispectral information of high spectrum image to correct the tonal distortion that standard moment-based operator produces, the method has well processed the tonal distortion visited between unit caused when standard moment-based operator corrects image, but the tonal distortion visited in unit that not correction standard moment-based operator does not cause, and the method is for single-range panchromatic image inapplicable；Liu Yan et al. proposes the random Strip noise removal algorithm of the improvement match by moment based on level set, although the tonal distortion that this algorithm inhibits to a certain extent between the spy unit that standard moment-based operator causes when correcting image and visits in unit, but the method precision when distinguishing the atural object of different spectral characteristic being based on image greyscale segmentation is not high, can divide due to atural object mistake when processing high resolution ratio satellite remote-sensing image and produce random tonal distortion；The Denoising Algorithm that spatial domain and frequency domain combine is presently mainly the time-frequency feature utilizing wavelet transformation, by image is carried out wavelet transformation, the wavelet coefficient rule of conversion of research noise, thus extracting the composition of Banded improvement and it being rejected, but wavelet transformation is computationally intensive, and spectral information can be caused greater loss, for the process of view picture satellite image, calculate speed and spectrum reservation degree is unsatisfactory.

Summary of the invention

Present invention solves the technical problem that and be: overcome the deficiencies in the prior art, for the random Banded improvement existed in panchromatic image, a kind of match by moment satellite image Strip noise removal method based on space segmentation, the problem that the standard moment-matching method of solving is not applied for the uneven satellite image of atural object are provided.

The technical scheme is that a kind of match by moment satellite image Strip noise removal method based on space segmentation, comprise the following steps:

1) to each pixel of pending satellite image, (x, y) sets up contiguous range k × k, and wherein k is positive odd number, and namely with pixel, (x, centered by y), selects the region L of k × k size；In acquisition region L, (x, y), in acquisition region L, (x y), obtains gradient to the intermediate value g of the gray value of all pixels to the meansigma methods f of the gray value of all pixels $G(x,y)=\sqrt{G_x^2+G_y^2},$ Wherein $G_x=\left[\begin{array}{ccc}-1& 0& 1\\ -2& 0& 2\\ -1& 0& 1\end{array}\right]*L,G_y=\left[\begin{array}{ccc}1& 2& 1\\ 0& 0& 0\\ -1& -2& -1\end{array}\right]*L,$ Wherein * is convolution symbol；

The three-dimensional coordinate being this pixel with the average f of each pixel, intermediate value g, gradient G, is mapped in the three-dimensional cartesian coordinate system with f, g, G respectively three axles, builds the three-dimensional co-occurrence matrix model of pending satellite image；And segmentation times N is set, wherein N is the positive integer more than 1；

2) in step 1) the three-dimensional co-occurrence matrix model set up arranges initial segmentation point s (f₀,g₀,G₀)；To cross some s (f₀,g₀,G₀) and be parallel to the plane of fOg, cross some s (f₀,g₀,G₀) and be parallel to the plane of gOG and cross some s (f₀,g₀,G₀) and be parallel to the plane of fOG three-dimensional co-occurrence matrix is divided into eight sub spaces；The definition four sub spaces in fOg plane are lower floor subspace, and all the other four sub spaces are subspace, upper strata；In the sub spaces of upper strata four, the subspace that definition is intersected with G axle is region A₀, the subspace symmetrical with this subspace angulation is reference zone B₀；In lower floor four sub spaces, region, the subspace C that definition is intersected with G axle₀, the subspace symmetrical with this subspace angulation is zoning D₀；

3) move s point along G axle, moving process obtains reference zone A in real time₀、B₀The sum of interior entropy, the maximum of points taking sum is the optimal partition point s on note G axle_G(f₀,g₀,G₀'), and regain reference zone A_0GAnd B_0G；S is moved along f axle_GPoint, obtains reference zone A in real time in moving process_0GAnd B_0GThe sum of interior entropy, the maximum of points taking sum is designated as the optimal partition point s on f axle_fG(f₀',g₀,G₀'), and regain reference zone A_0fGAnd B_0fG；S is moved along g axle_fGPoint, obtains reference zone A in real time in moving process_0fGAnd B_0fGThe sum of entropy, the maximum of points taking sum is designated as the optimal partition point s on g axle_fgG(f₀',g₀',G₀'), and regain reference zone A_0fgGAnd B_0fgG；By optimal partition point s_fgGIt is designated as s', by reference zone A_0fgGAnd B_0fgG, it is designated as reference zone A₁And B₁, it is thus achieved that eight sub spaces of three-dimensional co-occurrence matrix first order segmentation, and obtain the zoning C of first order segmentation₁And D₁；

4) in step 3) in the zoning C that obtains₁、D₁In each region in repeat step 2), step 3), obtain the zoning of four second level segmentation；

5) step 4 is repeated) until the iterations N arranged is finished, obtain final 2^NZoning；

6) calculation procedure 5) in the average of the grey scale pixel value corresponding to each zoning that obtains and variance, adopt standard moment-matching method, recalculate the gray value of pixel corresponding to this region, the final Banded improvement removing pending satellite image.

Present invention advantage compared with prior art is in that:

For the remote sensing panchromatic image that data volume is big, based on basic treatment theory, it is provided that a kind of effective image noise minimizing technology.The method can by the atural object of different spectral signatures according to average, intermediate value, gradient feature split, and adopt standard match by moment to process each cut zone, enough reconstructed images effectively, improve the quality of image, efficiency of algorithm is also significantly high.

Accompanying drawing explanation

Fig. 1 is the schematic flow sheet of the embodiment of the present invention；

Fig. 2 is three-dimensional co-occurrence matrix quadrant schematic diagram.

Detailed description of the invention

Basic theories according to image processing it is known that, the histogrammic different peak values of image represent the different types of ground objects comprised in image, although standard moment-matching method can cause tonal distortion when processing the image that atural object enriches, namely when image rectangular histogram has multiple peak value or histogram peak curve span is bigger, employing standard moment-matching method can cause tonal distortion phenomenon, but the curve distribution for the less single peak value of tonal range span, standard moment-matching method is when carrying out noise removal process to image, generation tonal distortion phenomenon in image can't be caused.Therefore the single imaging data visited within unit is made a distinction according to the difference (namely different tonal gradations) of image histogram distribution, then variance and the average of different gray areas imaging data are asked for respectively, the finally imaging data to different gray areas, carry out standard match by moment process respectively, thus can solve the tonal distortion between the spy unit that standard match by moment causes owing to average changes and owing to variance changes the tonal distortion visited in unit caused.

Technical solution of the present invention is described in detail below in conjunction with Fig. 1 and specific embodiment.

1) I (x, y) for satellite image pixel (x, y) gray value at place, if this neighborhood of pixel points size

K × k (chooses k=3) namely with pixel (x in the present embodiment, y) centered by, select the region L of 3 × 3 sizes, and calculate its average f (x, y) for the meansigma methods of the gray value of pixels all in L, calculate intermediate value g (x, y) intermediate value of the gray value of all pixels, gradient in L $G(x,y)=\sqrt{G_x^2+G_y^2},$ Wherein $G_x=\left[\begin{array}{ccc}-1& 0& 1\\ -2& 0& 2\\ -1& 0& 1\end{array}\right]*L,$ $G_y=\left[\begin{array}{ccc}1& 2& 1\\ 0& 0& 0\\ -1& -2& -1\end{array}\right]*L,$ * it is convolution symbol；The three-dimensional coordinate being this picture element with the average f of each picture element, intermediate value g and gradient G, is mapped in the three-dimensional cartesian coordinate system with f, g and G respectively three axles, builds the three-dimensional co-occurrence matrix model of pending satellite image；And segmentation times N=3 is set；

2) 1) given initial segmentation point s (f in the three-dimensional co-occurrence matrix model set up₀,g₀,G₀) be, to cross some s (f₀,g₀,G₀) and be parallel to the plane of fOg, cross some s (f₀,g₀,G₀) and be parallel to the plane of gOG and cross some s (f₀,g₀,G₀) and be parallel to the plane of fOG three-dimensional co-occurrence matrix is divided into eight sub spaces, see Fig. 2；The definition four sub spaces in fOg plane are lower floor subspace, and all the other four sub spaces are subspace, upper strata；In the sub spaces of upper strata four, define the subspace intersected with G axle, and the subspace symmetrical with this subspace angulation is region A₀With reference zone B₀, i.e. A₀For region 4, B₀For region 5；In lower floor four sub spaces, define the subspace intersected with G axle, and the subspace symmetrical with this subspace angulation is region C₀With zoning D₀, i.e. C₀For region 0, D₀For region 1；

3) move s point along G axle, moving process obtains reference zone A in real time₀、B₀The sum of interior entropy, the maximum of points taking sum is the optimal partition point s on note G axle_G(f₀,g₀,G₀'), and regain reference zone A_0GAnd B_0G；S is moved along f axle_GPoint, obtains reference zone A in real time in moving process_0GAnd B_0GThe sum of interior entropy, the maximum of points taking sum is designated as the optimal partition point s on f axle_fG(f₀',g₀,G₀'), and regain reference zone A_0fGAnd B_0fG；S is moved along g axle_fGPoint, obtains reference zone A in real time in moving process_0fGAnd B_0fGThe sum of entropy, the maximum of points taking sum is designated as the optimal partition point s on g axle_fgG(f₀',g₀',G₀'), and regain reference zone A_0fgGAnd B_0fgG；By optimal partition point s_fgGIt is designated as s', by reference zone A_0fgGAnd B_0fgG, it is designated as reference zone A₁And B₁, it is thus achieved that eight sub spaces of three-dimensional co-occurrence matrix first order segmentation, and obtain the zoning C of first order segmentation₁And D₁；

4) in step 3) in the zoning C that obtains₁、D₁In each region in repeat step 2), step 3), obtain the zoning of 4 second level segmentation；

5) 4 are repeated), obtain zoning, final 8；

6) to 5) the middle each zoning obtained, calculate average and the variance of grey scale pixel value corresponding to this region, employing standard moment-matching method, recalculates the gray value of pixel corresponding to this region, the final Banded improvement removing pending satellite image.

The content not being described in detail in the present invention belongs to the known technology of professional and technical personnel in the field.

Claims (1)

1. the match by moment satellite image Strip noise removal method based on space segmentation, it is characterised in that step is as follows:

1) to each pixel of pending satellite image, (x, y) sets up contiguous range k × k, wherein k=3, and namely with pixel, (x, centered by y), selects the region L of k × k size；In acquisition region L, (x, y), in acquisition region L, (x y), obtains gradient to the intermediate value g of the gray value of all pixels to the meansigma methods f of the gray value of all pixels $G(x,y)=\sqrt{G_x^2+G_y^2},$ Wherein $G_x=\left[\begin{array}{ccc}-1& 0& 1\\ -2& 0& 2\\ -1& 0& 1\end{array}\right]*L,G_y=\left[\begin{array}{ccc}1& 2& 1\\ 0& 0& 0\\ -1& -2& -1\end{array}\right]*L,$ Wherein * is convolution symbol；

The three-dimensional coordinate being this pixel with the average f of each pixel, intermediate value g, gradient G, is mapped in the three-dimensional cartesian coordinate system with f, g, G respectively three axles, builds the three-dimensional co-occurrence matrix model of pending satellite image；And segmentation times N is set, wherein N is the positive integer more than 1；

2) in step 1) the three-dimensional co-occurrence matrix model set up arranges initial segmentation point s (f₀,g₀,G₀)；To cross some s (f₀,g₀,G₀) and be parallel to the plane of fOg, cross some s (f₀,g₀,G₀) and be parallel to the plane of gOG and cross some s (f₀,g₀,G₀) and be parallel to the plane of fOG three-dimensional co-occurrence matrix is divided into eight sub spaces；The definition four sub spaces in fOg plane are lower floor subspace, and all the other four sub spaces are subspace, upper strata；In the sub spaces of upper strata four, the subspace that definition is intersected with G axle is region A₀, the subspace symmetrical with this subspace angulation is reference zone B₀；In lower floor four sub spaces, region, the subspace C that definition is intersected with G axle₀, the subspace symmetrical with this subspace angulation is zoning D₀；

3) move s point along G axle, moving process obtains reference zone A in real time₀、B₀The sum of interior entropy, the maximum of points taking sum is the optimal partition point s on note G axle_G(f₀,g₀,G₀'), and regain reference zone A_0GAnd B_0G；S is moved along f axle_GPoint, obtains reference zone A in real time in moving process_0GAnd B_0GThe sum of interior entropy, the maximum of points taking sum is designated as the optimal partition point s on f axle_fG(f₀',g₀,G₀'), and regain reference zone A_0fGAnd B_0fG；S is moved along g axle_fGPoint, obtains reference zone A in real time in moving process_0fGAnd B_0fGThe sum of entropy, the maximum of points taking sum is designated as the optimal partition point s on g axle_fgG(f₀',g₀',G₀'), and regain reference zone A_0fgGAnd B_0fgG；By optimal partition point s_fgGIt is designated as s', by reference zone A_0fgGAnd B_0fgG, it is designated as reference zone A₁And B₁, it is thus achieved that eight sub spaces of three-dimensional co-occurrence matrix first order segmentation, and obtain the zoning C of first order segmentation₁And D₁；

4) in step 3) in the zoning C that obtains₁、D₁In each region in repeat step 2), step 3), obtain the zoning of four second level segmentation；

5) step 4 is repeated) until the iterations N arranged is finished, obtain final 2^NZoning；

6) calculation procedure 5) in the average of the grey scale pixel value corresponding to each zoning that obtains and variance, adopt standard moment-matching method, recalculate the gray value of pixel corresponding to this region, the final Banded improvement removing pending satellite image.