CN106485677A - One kind rapidly and efficiently interferometric phase filtering method - Google Patents
One kind rapidly and efficiently interferometric phase filtering method Download PDFInfo
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Abstract
The present invention relates to one kind rapidly and efficiently interferometric phase filtering method, belong to the interleaving techniques field of remote sensing and signal processing, including the first step, data prepares；Second step, frequency domain filtering；3rd step, airspace filter.Present method solves the problem that existing interferometric phase filtering method can not be taken into account on filtering accuracy, interference fringe holding and filtration efficiency.
Description
Technical Field
The invention belongs to the technical field of crossing of remote sensing and signal processing, and particularly relates to a fast and efficient interference phase filtering method.
Background
And (4) carrying out conjugate multiplication on the registered main and auxiliary synthetic aperture radar images, and taking the phase of the multiplication result to obtain an interference phase. The interference phase is an important physical quantity in the interferometry of the synthetic aperture radar, and the quality of the interference phase determines the precision of a final product, namely a digital elevation model or a terrain deformation quantity. However, due to the decorrelation factors, there is always severe space-variant noise in the interference phase. The space-variant noise not only introduces residual error points but also destroys the distribution of interference phases, thereby increasing the difficulty of subsequent phase unwrapping, and finally reducing the precision of products, so that the residual error points must be filtered by interference phase filtering.
The existing interference phase filtering method can be mainly divided into a spatial domain interference phase filtering method and a transform domain (frequency domain, wavelet domain, etc.) interference phase filtering method. The spatial domain interference phase filtering method is carried out pixel by pixel, and adjacent or homogeneous (obeying the same distribution) pixel points are selected by taking a pixel to be filtered as a center to carry out average or weighted average processing, so that a filtering result is obtained. The transform domain interference phase filtering method firstly converts interference phases into a transform domain, and then performs contraction or threshold processing on transform coefficients according to different distribution characteristics of the interference phases and noise in the transform domain, thereby completing filtering.
Because the noise in the interference phase is space-variant, namely different pixel points are polluted by the noise with different degrees, and the corresponding noise variances are different; in addition, the interference phase is wound between (-pi, pi), and the winding characteristic causes a large amount of dense fringes at a steep terrain, and the characteristics of the two interference phases make it difficult for a single-domain interference phase filtering method based on a space domain or a transform domain to effectively filter noise and maintain useful phase information, for example, the interference phase filtering method based on the space domain is easy to destroy the dense fringes, the filtering parameters of the interference phase filtering method based on the transform domain are difficult to select, and the adaptability of the parameters is also the key of filtering, if the adaptability is not enough, the filtering or under-filtering is easy to cause, but the cost is that the operation efficiency of the method is very low, and the method cannot be used for rapidly processing mass measured data. Therefore, it is still necessary to develop a fast and efficient interference phase filtering method.
Disclosure of Invention
The invention provides a fast and efficient interference phase filtering method, which aims to solve the problem that the existing interference phase filtering method cannot give consideration to filtering precision, interference fringe maintenance and filtering efficiency.
The technical scheme of the invention is as follows: a fast and efficient interference phase filtering method is characterized by comprising the following steps:
first step, data preparation:
and generating a structure similarity graph, a coherence coefficient graph and an interference phase graph based on the registered primary and secondary synthetic aperture radar images.
And secondly, frequency domain filtering:
converting the interference phase diagram into a complex field to obtain a complex interference phase diagram, dividing the complex interference phase diagram into square image blocks with overlapping areas, wherein the following image blocks all refer to the square image block;
for each image block, the following processing is respectively carried out:
calculating a two-dimensional frequency spectrum of the image block by using two-dimensional Fourier forward transform;
carrying out absolute value taking, self-adaptive segmented hard threshold processing and power exponent operation on the two-dimensional frequency spectrum of the image block to obtain a weight;
weighting the two-dimensional frequency spectrum of the image block by using the obtained weight so as to realize filtering;
performing two-dimensional inverse Fourier transform on the weighted two-dimensional frequency spectrum of the image block to obtain a complex image block after frequency domain filtering;
and splicing all the complex image blocks subjected to frequency domain filtering, wherein an overlapping area is smoothed by using an averaging method, so that a complex interference phase diagram subjected to frequency domain filtering is obtained, and then the phase of the complex interference phase diagram is obtained, so that the interference phase diagram subjected to frequency domain filtering is obtained.
Thirdly, spatial filtering:
and subtracting the interference phase image after frequency domain filtering by using the interference phase image, and then winding to obtain a residual interference phase image. And multiplying the residual interference phase diagram by the coherent coefficient diagram, adding the residual interference phase diagram and the interference phase diagram after frequency domain filtering, and winding to obtain a spatial domain interference phase diagram to be filtered. Converting the space domain to-be-filtered interference phase diagram into a complex domain to obtain a space domain to-be-filtered complex interference phase diagram, filtering the space domain to-be-filtered complex interference phase diagram by using mean filtering to obtain a space domain filtered complex interference phase diagram, and taking a phase of the space domain filtered complex interference phase diagram to obtain a filtered interference phase diagram.
The invention can achieve the following technical effects:
according to the method, the structure similarity graph is adopted to calculate the filtering parameters of frequency domain filtering, the adaptive power exponent based on the structure similarity graph is provided according to the space-variant of noise, and the low-frequency part (the interference phase graph after frequency domain filtering) of the interference phase graph can be extracted in a self-adaptive mode. Then, the coherent coefficient map is multiplied by the high frequency part (residual interference phase map) of the interference phase map and added to the low frequency part of the interference phase map, the space-variant noise is converted into approximate space-invariant noise, and finally the final filtering result is obtained by mean filtering processing. The method can effectively filter noise and simultaneously lose useful phase information less, and the efficiency of the method is far higher than that of an NL-InSAR method and a Sparse Coding method.
Drawings
FIG. 1 is a schematic flow chart of a fast and efficient interferometric phase filtering method provided by the present invention;
FIG. 2 is an interference phase diagram to be filtered;
FIG. 3 is a filtered interference phase diagram obtained by a 3 × 3 pixel window size mean filtering method;
FIG. 4 is a filtered interference phase diagram obtained by a mean filtering method with a window size of 11 × 11 pixels;
FIG. 5 is a filtered interferometric phase diagram obtained by NL-InSAR method;
FIG. 6 is a filtered interference phase diagram obtained by the spark Coding method;
FIG. 7 is a diagram of the interference phase after filtering according to the present invention;
fig. 8 is a graph comparing the performance of fig. 3, 4, 5, 6 and 7.
Detailed Description
The present invention will be described in detail with reference to fig. 1.
The first step is as follows: data preparation
The registered main and auxiliary synthetic aperture radar images are respectively x and y, and a structural similarity graph, a correlation coefficient graph and an interference phase can be obtained based on the x and y and respectively are as follows:
wherein SSIM is a structural similarity map, μxAnd muyAre the local means of the images x and y respectively,andlocal variance, 2 σ, of the images x and y, respectivelyxyIs the local covariance, C, of the images x and y1＝(0.01×2π)2，C2＝(0.03×2π)2To avoid blockiness, μ is calculatedx、μx、And σxyThe weighting is performed by a gaussian function of 11 × 11 pixels in window size, the standard deviation of the gaussian function is 1.5, gamma is a coherence coefficient map, x is the complex conjugate, is the phase estimated from the local frequency, the window M-N-5 pixels, M and N are the row coordinate and column coordinate indices, respectively;is the interference phase diagram and angle () is the phase operator. Note that SSIM is distributed at [ -1,1 [ ]]In the latter use we use SSIM +1, i.e.
SSIM＝SSIM+1
The specific calculation method is as follows:
tempim＝exp(j·angle(x·y*))
sizex＝max(size(tempim))
tfim＝FFT2(tempim,L)
hase(1:1:sizex,1:1:sizex)＝ex(1:1:sizex)′·ey(1:1:sizex)
phaa＝tempim·hase*
phi＝mean(phaa)
in the above formula, size () is a two-dimensional size operator of the matrix, max () is a maximum value operator, ceil () is a nearest integer operator, FFT2() is a two-dimensional fourier transform, abs () is an absolute value operator, and mean () is a mean value operator.
The second step is that: frequency domain filtering
Step (1), interference phase diagramConversion to complex domain to obtain complex interference phase pattern
Where ψ is a complex interference phase diagram. The complex interference phase diagram is partitioned, the size of an image block is determined according to actual conditions, the size of the image block is a square of 32 x 32 pixels in the embodiment, the partitions and the partitions are overlapped, 14 pixel points are overlapped in each direction, and then the step (2) is completed for each image block.
Step (2): and performing frequency domain filtering on each image block.
First, the two-dimensional spectrum of the image block is calculated:
B＝FFT2(P)
where P is an image block and B is a two-dimensional spectrum of the image block and includes a coordinate index range of pixels of [ -15,16] × [ -15,16 ].
And secondly, performing absolute value taking, adaptive segmented hard threshold processing and power exponent operation on the two-dimensional frequency spectrum of the image block to obtain a weight. Calculating the absolute value A of the two-dimensional frequency spectrum of the image block:
A＝abs(B)
and (3) carrying out self-adaptive segmentation hard threshold processing on the pixels in the A:
wherein A ishtThe absolute value of the two-dimensional frequency spectrum of the image block after the adaptive segmentation hard threshold processing is obtained. Then, for AhtPerforming power exponent operation to obtain weight:
wherein, the weight α is calculated based on the structural similarity map corresponding to the image block, and specifically as follows:
wherein, SSIMPIs the structural similarity graph corresponding to the image block P, min () is the minimum operator, SSIMP_meanIs SSIMPIs measured.
Then, the two-dimensional spectrum of the image block is weighted:
BW＝B·W
wherein, BWIs the weighted image block two-dimensional spectrum.
Finally, for BWCalculating a frequency-domain filtered complex image block P by performing two-dimensional inverse Fourier transformW：
PW＝IFFT2(BW)
Where IFFT2(·) is a two-dimensional inverse fourier transform.
Step (3), generating a frequency domain filtered interference phase diagram
After all the frequency domain filtered complex image blocks are obtained, the complex image blocks are spliced to form the frequency domain filtered complex image blockComplex interference phase diagram psifrequencyAnd processing the parts with the overlapped areas in an averaging mode. Then to psifrequencyObtaining a frequency domain filtered interference phase diagram by taking the phase:
wherein,is a frequency filtered interference phase diagram.
Third, spatial filtering
Generating a residual interference phase diagram and a spatial domain interference phase diagram to be filtered:
wherein,is a residual interference phase diagram, wrap () is a wrap operator, wrapping the input value to the interval (-pi, pi)]；Is a spatial domain interference phase diagram to be filtered.
Generating a complex interference phase diagram to be filtered in a space domain and carrying out mean filtering:
wherein,is a complex interference phase diagram to be filtered in a space domain, cfis a complex interference phase diagram after spatial filtering,is the mean filter operator, which in the present invention has a filter window size of 3 × 3 pixels.
Step (3), generating a filtered interference phase diagram
Wherein,is a filtered interference phase map.
Detailed description with respect to fig. 2-8:
fig. 2 is an interference phase diagram to be filtered. The interference phase intercepts the measured data of Italian Etna volcano obtained from SIR-C/X-SAR, the size is 1000 multiplied by 1000 pixels, wherein the abscissa is a column index, and the unit is a pixel; the ordinate is the row index and the unit is the pixel.
Fig. 3 is a filtered interference phase diagram obtained by a 3 × 3 pixel window-sized mean filtering method, fig. 4 is a filtered interference phase diagram obtained by an 11 × 11 pixel window-sized mean filtering method, fig. 5 is a filtered interference phase diagram obtained by an NL-InSAR method, fig. 6 is a filtered interference phase diagram obtained by a spare Coding method, and fig. 7 is a filtered interference phase diagram obtained by the present invention. Comparing fig. 3 and fig. 7, it can be seen that the invention can better filter noise and the interference fringes are clearer than the mean filtering method of 3 × 3 pixel window size; comparing fig. 4, fig. 5 and fig. 7, it can be seen that the mean filtering method with a window size of 11 × 11 pixels and the NL-InSAR method destroy the continuity of the stripes although the denoising capability is strong, but the invention effectively filters the noise while maintaining the continuity of the stripes, especially the dense stripes. Comparing fig. 6 and fig. 7, it can be seen that both the Sparse Coding method and the present invention filter noise while maintaining continuity of dense stripes, and the filtering results of the two methods are almost the same.
FIG. 8 is a graph comparing the performance of FIGS. 3, 4, 5, 6 and 7; the performance comparison graph contains two performance indexes: method run time and residual points. For any 4 adjacent pixel points in the filtered interference phase image:we calculate the winding gradient values of two pixels respectively:
wherein, C {. is a winding operator, and the value range is [ -pi, pi) ], delta1，Δ2，Δ3，Δ4Is the winding gradient value. The 4 wrap gradient values are then summed:
Δtotal＝Δ1+Δ2+Δ3+Δ4
if ΔtotalNot equal to 0, the pixel points (m, n) are called residual points, and the smaller the number of the residual points, the better the interference phase quality after filtering.Meanwhile, the method is not as good as the mean filtering method in efficiency, but is far faster than the NL-InSAR method and the Sparse Coding method.
Claims (4)
1. A fast and efficient interference phase filtering method is characterized by comprising the following steps: firstly, preparing data; secondly, filtering in a frequency domain; and thirdly, spatial filtering.
2. A fast and efficient interferometric phase filtering method according to claim 1, characterized by: and the data in the first step is based on the registered primary and secondary synthetic aperture radar images to generate a structural similarity graph, a coherence coefficient graph and an interference phase graph.
3. A fast and efficient interferometric phase filtering method according to claim 2, characterized by: the specific operation method in the second step is as follows:
(1) converting the interference phase diagram into a complex field to obtain a complex interference phase diagram, dividing the complex interference phase diagram into square image blocks with overlapping areas, wherein the following image blocks all refer to the square image block;
(2) for each image block, the following processing is respectively carried out:
1) calculating a two-dimensional frequency spectrum of the image block by using two-dimensional Fourier forward transform;
2) carrying out absolute value taking, self-adaptive segmented hard threshold processing and power exponent operation on the two-dimensional frequency spectrum of the image block to obtain a weight;
3) weighting the two-dimensional frequency spectrum of the image block by using the obtained weight so as to realize filtering;
4) performing two-dimensional inverse Fourier transform on the weighted two-dimensional frequency spectrum of the image block to obtain a complex image block after frequency domain filtering;
5) and splicing all the complex image blocks subjected to frequency domain filtering, wherein an overlapping area is smoothed by using an averaging method, so that a complex interference phase diagram subjected to frequency domain filtering is obtained, and then the phase of the complex interference phase diagram is obtained, so that the interference phase diagram subjected to frequency domain filtering is obtained.
4. A fast and efficient interferometric phase filtering method according to claim 3, characterized by: the specific operation method of the third step is as follows:
(1) subtracting the interference phase image after frequency domain filtering by using the interference phase image, and then winding to obtain a residual interference phase image;
(2) multiplying the residual interference phase diagram by a coherent coefficient diagram, adding the multiplied residual interference phase diagram and the interference phase diagram after frequency domain filtering, and winding to obtain a spatial domain interference phase diagram to be filtered;
(3) converting the space domain to-be-filtered interference phase diagram into a complex domain to obtain a space domain to-be-filtered complex interference phase diagram, filtering the space domain to-be-filtered complex interference phase diagram by using mean filtering to obtain a space domain filtered complex interference phase diagram, and taking a phase of the space domain filtered complex interference phase diagram to obtain a filtered interference phase diagram.
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| CN112932508A (en) * | 2021-01-29 | 2021-06-11 | 电子科技大学 | Finger activity recognition system based on arm electromyography network |
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