CN113805146A - High-orbit SAR image space-variant phase error estimation method - Google Patents
High-orbit SAR image space-variant phase error estimation method Download PDFInfo
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Abstract
The invention provides a space-variant phase error estimation method for a high-orbit SAR image, which can meet the high-precision imaging requirements of high-orbit SAR on high resolution and large-scale wide scenes. Estimating phase errors in a blocking mode along a distance direction and an azimuth direction of the high-orbit SAR image; respectively carrying out phase error pre-compensation on each image block by using the phase error obtained by estimation to obtain pre-compensated image blocks; improving the phase error corresponding to the image block with larger image quality, and calculating to obtain a polynomial coefficient corresponding to the two-dimensional curved surface of the phase error of the whole scene; according to the obtained polynomial coefficient, fitting a phase error two-dimensional curved surface of the whole scene, compensating the phase error fitting curved surface of the whole scene into the whole image of the high-orbit SAR, finishing the final correction of the phase error, and solving the problems that the estimation precision of the traditional PGA algorithm changes along with the contrast of the scene under the large-amplitude wide scene of the high-orbit SAR and the focusing effect of the compensated image is inconsistent.
Description
Technical Field
The invention relates to the technical field of Synthetic Aperture Radar (SAR) signal processing, in particular to a high-orbit SAR image space-variant phase error estimation method.
Background
The high resolution and the wide surveying and mapping band are important indexes for measuring the performance of the satellite-borne SAR, the azimuth resolution and the surveying and mapping bandwidth of the low-orbit SAR are mutually restricted under a conventional pulse working system, and the improvement of the orbit height is an effective means for solving the restriction relation. With the improvement of the related technical levels of large-aperture antenna technology, high-power supply and heat dissipation technology, high-orbit SAR imaging and the like, the feasibility of a high-orbit SAR system is internationally and widely recognized. The high-track SAR can greatly improve the system performance of the low-orbit SAR, can compensate the low-orbit SAR with each other in a space position, and has the capabilities of wide-area monitoring, wide-swath high-quality imaging, differential interference, rapid response of emergency and the like.
Since the satellite-borne SAR system works on the ionized layer, the wireless signals are influenced by the ionized layer effect in the process of passing through the ionized layer. With the improvement of high-orbit SAR resolution, the increase of synthetic aperture time and the increase of mapping bandwidth, the influence of the time-space change of Total Electron Content (TEC) of the ionized layer on high-orbit SAR imaging must be considered. At present, a phase gradient self-focusing (PGA) algorithm is mainly used in a high-orbit SAR ionosphere correction method, and the algorithm performs optimal estimation on a phase error based on a maximum likelihood estimation theory. However, the estimation accuracy of the PGA algorithm is greatly influenced by scene contrast, and when isolated strong scattering points exist in a scene, the estimation accuracy of the PGA algorithm is higher; and when the scene scatter is uniform (e.g., farmland), the PGA algorithm estimation accuracy is degraded. The imaging width of the high-orbit SAR satellite can reach 2500km, the scene content is rich, the contrast distribution interval is large, meanwhile, the ionosphere influence introduces strong space variability, the problem that the focusing effect of the whole high-orbit SAR image is inconsistent in the using process of the existing PGA correction method is caused, and the high-precision imaging requirements of high-resolution and large-amplitude wide scene of the high-orbit SAR cannot be met.
Disclosure of Invention
In view of this, the invention provides a high-orbit SAR image space-variant phase error estimation method, which can meet the high-precision imaging requirements of high-orbit SAR on high resolution and a large-scale wide scene.
In order to achieve the purpose, the technical scheme of the invention is as follows:
the invention discloses a high-orbit SAR image space-variant phase error estimation method, which comprises the following steps:
firstly, partitioning a high-orbit SAR image along a distance direction and an azimuth direction;
respectively estimating the phase error of each image block of the high-orbit SAR image; respectively carrying out phase error pre-compensation on each image block by using the phase error obtained by estimation to obtain pre-compensated image blocks;
evaluating the image quality improvement condition of the image blocks after pre-compensation, establishing M multiplied by P equation sets by utilizing the phase errors corresponding to P multiplied by Q image blocks before the image quality is improved, and calculating to obtain a polynomial coefficient corresponding to a two-dimensional curved surface of the phase error of the whole scene; wherein, P multiplied by Q is a set value, P is less than M, Q is less than N, M is the total number of the distance direction blocks, and N is the total number of the azimuth direction blocks;
and step four, fitting the phase error two-dimensional curved surface of the whole scene according to the obtained polynomial coefficient, and compensating the phase error fitted curved surface of the whole scene into the whole high-orbit SAR image to finish the final correction of the phase error.
In the third step, an image entropy evaluation coefficient is used to evaluate the image quality improvement condition of the pre-compensated image block, where the image entropy evaluation coefficient η (x, y) is:
wherein E (-) represents the operation of solving the entropy of the image, I (x, y) is the image block obtained by partitioning in the step one, IPGA(x, y) is the pre-compensated image block, x is 1,2, …, M; y is 1,2, … N.
In the second step, a PGA algorithm is respectively adopted to estimate the phase error of each image block of the high-orbit SAR image to obtain an MXN phase error estimation value
x＝1,2,…,M；y＝1,2,…N。
In the third step, the M × P equation sets are:
wherein the content of the first and second substances,
phase errors corresponding to the P × Q image blocks before image quality improvement, where x is 1,2, …, P; y is 1,2, … Q.
In the fourth step, the phase error of the whole scene is fitted with a curved surface
Comprises the following steps:
wherein x is 1,2, …, M; y is 1,2, … N.
Has the advantages that:
the phase error is estimated block by block along the distance direction and the azimuth direction of the high-orbit SAR image; respectively carrying out phase error pre-compensation on each image block by using the phase error obtained by estimation to obtain pre-compensated image blocks; evaluating the image quality improvement condition of the image blocks after pre-compensation, establishing a plurality of equation sets by utilizing the phase error corresponding to the image blocks with larger image quality improvement, and calculating to obtain a polynomial coefficient corresponding to the phase error two-dimensional curved surface of the whole scene; according to the obtained polynomial coefficient, fitting a phase error two-dimensional curved surface of the whole scene, compensating the phase error fitting curved surface of the whole scene into the whole image of the high-orbit SAR, and completing final phase error correction, so that an image with improved focusing effect and uniform imaging effect under the condition of a large-breadth scene is obtained, and the problems that the estimation precision of the traditional PGA algorithm changes along with the scene contrast ratio under the condition of the large-breadth scene of the high-orbit SAR and the focusing effect of the compensated image is inconsistent can be solved.
The method utilizes the image entropy evaluation coefficient to evaluate the image quality improvement condition of the image block after the pre-compensation, and the evaluation can be quantized and is more visual. The larger the image entropy evaluation coefficient is, the higher the image quality improvement of the image block is represented, and the higher the estimation accuracy of the phase error is reflected.
The invention estimates the phase error of each image block by utilizing the PGA algorithm, can estimate the space-variant phase error introduced by the ionized layer with high precision, overcomes the problems that the estimation precision of the traditional PGA method is influenced by the scene contrast and the adaptability to a large-breadth wide scene is insufficient, and meets the requirements of high-orbit SAR on large-breadth high-resolution imaging.
Drawings
Fig. 1 is a flowchart of a high-orbit SAR space-variant phase error estimation method according to an embodiment of the present invention.
Detailed Description
The invention is described in detail below by way of example with reference to the accompanying drawings.
The embodiment provides a high-precision estimation method for a space-variant phase error of a high-orbit SAR large-scene image, which comprises the following specific steps:
partitioning a high-orbit SAR image with an ionosphere error along a distance direction and an azimuth direction;
in this embodiment, the distance is to M blocks, and the direction is to N blocks, so that M × N image blocks I (x, y) can be obtained, where x is 1,2, …, M; a typical value for M and N is 16 × 16, with y being 1,2, … N.
Respectively estimating the phase error of each image block of the high-orbit SAR image; respectively carrying out phase error pre-compensation on each image block by using the phase error obtained by estimation to obtain pre-compensated image blocks;
in this embodiment, a phase gradient self-focusing (PGA) algorithm is used to estimate a phase error, and specifically, a PGA algorithm is used to estimate a phase error for each image block I (x, y) to obtain an mxn estimated phase error value
Respectively carrying out phase error pre-compensation on each image block by utilizing the phase error obtained by estimation to obtain a pre-compensated image block IPGA(x,y)。
Thirdly, evaluating the image quality improvement condition of the image block after the pre-compensation by utilizing the image entropy evaluation coefficient eta (x, y):
wherein E (-) represents the entropy operation of the image. The larger the image entropy evaluation coefficient is, the higher the image quality improvement of the image block is represented, and the higher the estimation accuracy of the phase error is reflected.
Thirdly, sequencing each image block according to the image entropy evaluation coefficient, selecting the image block with the larger image entropy evaluation coefficient, and storing the corresponding phase error estimation value;
in this embodiment, phase error estimation values corresponding to P × Q image blocks with larger entropy evaluation coefficients are selected
Wherein P is less than M and Q is less than N.
Step four, because the phase error caused by the ionosphere is slowly changed along the distance direction and the azimuth direction, a plurality of equation sets are established according to a plurality of groups of phase error estimated values which are screened out, and polynomial coefficients corresponding to the phase error two-dimensional curved surface of the whole scene are obtained through calculation;
the embodiment utilizes the P multiplied by Q phase error estimated values screened out in the third step
M × P equation sets are constructed:
wherein x is 1,2, …, P; y is 1,2, … Q.
Solving polynomial coefficient k of equation (2)0～k9And obtaining a polynomial coefficient corresponding to the phase error two-dimensional curved surface of the whole scene.
Step five, fitting the phase error two-dimensional curved surface of the whole scene according to the polynomial coefficient obtained in the step four, and in the embodiment, fitting the phase error two-dimensional curved surface of the whole scene according to the formula (3) to obtain the phase error fitting curved surface of the whole scene
Wherein x is 1,2, …, M; y is 1,2, … N.
Fitting the phase error of the whole scene to a curved surface
And compensating the phase error into the whole image of the high-orbit SAR, and finishing final correction of the phase error so as to obtain an image with improved focusing effect and uniform imaging effect under the condition of a large-width scene.
In summary, the above description is only a preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (5)
1. A high-orbit SAR image space-variant phase error estimation method is characterized by comprising the following steps:
firstly, partitioning a high-orbit SAR image along a distance direction and an azimuth direction;
respectively estimating the phase error of each image block of the high-orbit SAR image; respectively carrying out phase error pre-compensation on each image block by using the phase error obtained by estimation to obtain pre-compensated image blocks;
evaluating the image quality improvement condition of the image blocks after pre-compensation, establishing M multiplied by P equation sets by utilizing the phase errors corresponding to P multiplied by Q image blocks before the image quality is improved, and calculating to obtain a polynomial coefficient corresponding to a two-dimensional curved surface of the phase error of the whole scene; wherein, P multiplied by Q is a set value, P is less than M, Q is less than N, M is the total number of the distance direction blocks, and N is the total number of the azimuth direction blocks;
and step four, fitting the phase error two-dimensional curved surface of the whole scene according to the obtained polynomial coefficient, and compensating the phase error fitted curved surface of the whole scene into the whole high-orbit SAR image to finish the final correction of the phase error.
2. The estimation method according to claim 1, wherein in the third step, the image quality improvement condition of the pre-compensated image block is evaluated by using an image entropy evaluation coefficient, and the image entropy evaluation coefficient η (x, y) is:
wherein E (-) represents the operation of solving the entropy of the image, I (x, y) is the image block obtained by partitioning in the step one, IPGA(x, y) is the pre-compensated image block, x is 1,2, …, M; y is 1,2, … N.
3. The estimation method according to claim 1 or 2, wherein in the second step, PGA algorithm is used for estimation respectivelyObtaining the phase error estimation value of M multiplied by N phase errors of each image block of the high-orbit SAR image
x＝1,2,…,M；y＝1,2,…N。
4. The estimation method according to claim 1,2 or 3, wherein in the third step, the M x P equation sets are:
wherein the content of the first and second substances,
phase errors corresponding to the P × Q image blocks before image quality improvement, where x is 1,2, …, P; y is 1,2, … Q.
5. The estimation method of claim 4, wherein in the fourth step, the phase error of the whole scene is fitted to a surface
Comprises the following steps:
wherein x is 1,2, …, M; y is 1,2, … N.
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Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008195140A (en) * | 2007-02-09 | 2008-08-28 | Fujitsu Ten Ltd | Radar system, target detection program and target detection method |
| US20090303111A1 (en) * | 2008-06-09 | 2009-12-10 | Cho Kwang M | Autofocus for minimum entry through multi-dimensional optimization |
| US20100149023A1 (en) * | 2008-12-15 | 2010-06-17 | The Boeing Company | Estimation and Correction of Error in Synthetic Aperture Radar |
| KR20110060626A (en) * | 2009-11-30 | 2011-06-08 | 서울시립대학교 산학협력단 | The method for measurign object's velocity using synthetic aperture radar image and the apparatus thereof |
| CN105974415A (en) * | 2016-06-24 | 2016-09-28 | 西安电子科技大学 | High precision compensation method for airborne SAR orientation space-variant motion error |
| CN106054188A (en) * | 2016-06-24 | 2016-10-26 | 西安电子科技大学 | Unmanned aerial vehicle synthetic aperture radar imaging range-dependant map drift method |
| WO2016169699A1 (en) * | 2015-04-23 | 2016-10-27 | Forest Vision As | A system, an apparatus and a method for determining mass change in a study area using remote sensing data |
| CN106855624A (en) * | 2016-12-21 | 2017-06-16 | 西安交通大学青岛研究院 | A kind of SAR image rectification building-out processing method |
| JP2019132643A (en) * | 2018-01-30 | 2019-08-08 | 住友電気工業株式会社 | Collimator, radio wave sensor, and adjustment method |
| CN110261833A (en) * | 2019-07-04 | 2019-09-20 | 中国人民解放军国防科技大学 | High-resolution spaceborne SAR imaging error estimation and compensation method |
| CN113093186A (en) * | 2021-03-31 | 2021-07-09 | 中国人民解放军国防科技大学 | Large scene high-resolution imaging method and device based on block imaging |
-
2021
- 2021-07-23 CN CN202110836910.3A patent/CN113805146B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008195140A (en) * | 2007-02-09 | 2008-08-28 | Fujitsu Ten Ltd | Radar system, target detection program and target detection method |
| US20090303111A1 (en) * | 2008-06-09 | 2009-12-10 | Cho Kwang M | Autofocus for minimum entry through multi-dimensional optimization |
| US20100149023A1 (en) * | 2008-12-15 | 2010-06-17 | The Boeing Company | Estimation and Correction of Error in Synthetic Aperture Radar |
| KR20110060626A (en) * | 2009-11-30 | 2011-06-08 | 서울시립대학교 산학협력단 | The method for measurign object's velocity using synthetic aperture radar image and the apparatus thereof |
| WO2016169699A1 (en) * | 2015-04-23 | 2016-10-27 | Forest Vision As | A system, an apparatus and a method for determining mass change in a study area using remote sensing data |
| CN105974415A (en) * | 2016-06-24 | 2016-09-28 | 西安电子科技大学 | High precision compensation method for airborne SAR orientation space-variant motion error |
| CN106054188A (en) * | 2016-06-24 | 2016-10-26 | 西安电子科技大学 | Unmanned aerial vehicle synthetic aperture radar imaging range-dependant map drift method |
| CN106855624A (en) * | 2016-12-21 | 2017-06-16 | 西安交通大学青岛研究院 | A kind of SAR image rectification building-out processing method |
| JP2019132643A (en) * | 2018-01-30 | 2019-08-08 | 住友電気工業株式会社 | Collimator, radio wave sensor, and adjustment method |
| CN110261833A (en) * | 2019-07-04 | 2019-09-20 | 中国人民解放军国防科技大学 | High-resolution spaceborne SAR imaging error estimation and compensation method |
| CN113093186A (en) * | 2021-03-31 | 2021-07-09 | 中国人民解放军国防科技大学 | Large scene high-resolution imaging method and device based on block imaging |
Non-Patent Citations (5)
| Title |
|---|
| YUNJUN, ZHANG: "Small baseline InSAR time series analysis: Unwrapping error correction and noise reduction", COMPUTERS & GEOSCIENCES * |
| 张晗: "SAR图像质量评估方法研究", CNKI优秀硕士学位论文 * |
| 李腾飞: "GEO SAR快视成像系统设计与实现", 空间电子技术, no. 1 * |
| 杨帅: "高分辨率极化SAR对象化目标分解与分类方法研究", CNKI优秀博士论文库 * |
| 王青松 等: "联合实、复相关函数的干涉ＳＡＲ图像配准方法", 测绘学报, vol. 41, no. 4 * |
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