CN115015926A - Multichannel SAR image false target rapid screening and removing method - Google Patents
Multichannel SAR image false target rapid screening and removing method Download PDFInfo
- Publication number
- CN115015926A CN115015926A CN202210580600.4A CN202210580600A CN115015926A CN 115015926 A CN115015926 A CN 115015926A CN 202210580600 A CN202210580600 A CN 202210580600A CN 115015926 A CN115015926 A CN 115015926A
- Authority
- CN
- China
- Prior art keywords
- targets
- target
- false
- sar image
- group
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
- G01S13/9004—SAR image acquisition techniques
- G01S13/9005—SAR image acquisition techniques with optical processing of the SAR signals
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
Abstract
The invention provides a method for quickly screening and eliminating false targets of a multi-channel SAR image, belonging to the technical field of radar signal processing, which comprises the following specific processes: firstly, calculating the distance between the false targets on the two sides of a real target, sequencing all targets detected in an SAR image according to the azimuth direction, calculating the distance interval between two adjacent targets, and grouping the targets based on the distance, the distance interval and a fault-tolerant threshold; secondly, performing uniformity judgment on the attributes of the same group of targets, and removing targets without the same attribute; and finally, for each group of targets, false target elimination is carried out based on the scattering characteristics of the targets, so that the multichannel SAR image false target quick screening and elimination are realized.
Description
Technical Field
The invention belongs to the technical field of radar signal processing, and particularly relates to a method for quickly screening and eliminating false targets of a multi-channel SAR image.
Background
Synthetic Aperture Radar (SAR) is an active microwave remote sensing device with all-weather and all-time earth observation capability. With the increasing application range of the SAR in the military and civil fields, the requirements of each field on the mapping bandwidth and the resolution of the SAR are higher and higher. The azimuth multi-channel technology well solves the contradiction between the resolution ratio and the swath width of the traditional SAR, realizes high-resolution wide swath imaging, and becomes an important technical means for realizing high-resolution wide swath imaging of the next generation of satellite-borne SAR. However, in an actual system, amplitude and phase errors among channels are caused by factors such as amplitude and phase inconsistency of multiple receiving channels of a radar system, instability of satellite postures and rapid movement of a ship, and therefore false targets are introduced.
The presence of SAR multi-channel false targets presents a significant challenge to SAR ship detection. These false objects are highly bright relative to the sea surface and can be falsely detected when low scattering regions such as the sea surface occur, causing false alarms. Although the multi-channel false target has a certain attenuation in brightness compared with the corresponding real target, the multi-channel false target has no clear bright-dark relation with other non-corresponding real targets, and therefore cannot be identified according to absolute brightness. More seriously, in the SAR imaging process, the movement of the ship causes the change of Doppler parameters, and further causes the mismatch of matching filtering in the azimuth direction, so that the ship target generates defocusing phenomena of different degrees in the SAR image. How to distinguish false targets caused by multiple channels from real defocused targets in a full-aperture image has great significance for reducing the false alarm rate in SAR ship detection. However, only a few scholars have made corresponding research on the aspect at present, and marlin and the like propose a SAR multi-channel false target identification method based on sub-aperture and full-aperture feature learning aiming at the problem, wherein the full-aperture feature is extracted by utilizing a deep convolutional neural network, the sub-aperture decomposition is carried out on a complex SAR image, the sub-aperture feature is extracted by utilizing a stacked convolutional self-coding network, and the real target and the multi-channel false target are distinguished through identification information contained in the sub-aperture feature. However, the method utilizes two deep convolution networks to extract features, has large calculation amount, and is difficult to realize particularly under the condition of limited resources such as satellite borne and the like.
Disclosure of Invention
In view of the above, the invention provides a method for quickly screening and removing false targets of a multi-channel SAR image, which can accurately remove the false targets.
The technical scheme for realizing the invention is as follows:
a method for quickly screening and eliminating false targets of a multi-channel SAR image comprises the following specific processes:
calculating the distance between the false target and the real target;
sequencing all targets detected in the SAR image according to the azimuth direction, and calculating the distance interval between two adjacent targets;
grouping targets based on the distance, the distance interval, and a fault tolerance threshold;
carrying out uniformity judgment on the attributes of the same group of targets, and removing targets without the same attribute;
and aiming at each group of targets, carrying out false target elimination based on the scattering characteristics of the targets.
Further, the present invention removes the group where only one object is located when grouping objects.
Further, the grouping of the targets based on the distance, the distance interval and the fault tolerance threshold of the present invention is as follows: if | d-n is satisfied 1 If l is less than or equal to l, two adjacent targets are divided into a group, wherein ll is a fault tolerance threshold value, and n is 1 n 1 The distance between the false target and the real target is shown.
Furthermore, the false targets are at intervals n distributed at equal intervals on both sides of the real target 1 Comprises the following steps:
wherein N is a single zero padding As a single channel squareThe number of points after zero padding in the bit direction, m is the number of range gates where the target is located, R min Is the minimum pitch, f s For sampling frequency, T p Is the pulse width, R ref For reference slope distance, c is the speed of light.
Further, the method for judging the uniformity of the attributes of the same group of targets comprises the following steps: and performing uniformity judgment according to the target direction and the target size.
Further, the target direction and the target size of the present invention are determined according to the following manner:
cutting out a slice with a set size by taking the target central point as a center;
carrying out Otsu threshold segmentation on the slice containing the target to obtain a segmented binary image;
and calculating the minimum circumscribed rectangle of the binary image target area to obtain the target direction and the target size.
Further, after removing the objects without the same attribute, if only one object exists in the group, the group is removed.
Further, the specific process of the false target elimination based on the target dispersion characteristic of the invention is as follows: and respectively carrying out mean value and variance statistics on the targets in each group, wherein the mean value reflects the brightness information of the targets, the variance reflects the scattering degree of the targets, the target with the maximum mean value and variance is regarded as a real target, and other targets are judged as false targets, so that the false targets are removed.
Advantageous effects
Firstly, according to the characteristic that false targets are symmetrically distributed on two sides of a real target at equal intervals, the detection result is subjected to suspected false target grouping, and then further target elimination is performed based on target attributes.
Secondly, the method and the device perform identity judgment based on the attribute characteristics such as the orientation, the length and the like of the targets, screen and remove the suspected false target groups, and can quickly remove the targets which do not belong to the same attribute, thereby avoiding grouping errors caused by over-dense grouping.
Thirdly, the false target is removed through the scattering characteristic difference between the false target and the real target, so that the SAR image false target is rapidly screened and removed.
Drawings
FIG. 1 is a schematic diagram of a false target and a real target.
FIG. 2 is a schematic diagram of the distribution of false targets and real targets.
FIG. 3 is a diagram illustrating a false target elimination result.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.
It should be noted that, in the case of no conflict, the features in the following embodiments and examples may be combined with each other; moreover, based on the embodiments in the present disclosure, all other embodiments obtained by a person of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present disclosure.
It is noted that various aspects of the embodiments are described below within the scope of the appended claims. It should be apparent that the aspects described herein may be embodied in a wide variety of forms and that any specific structure and/or function described herein is merely illustrative. Based on the disclosure, one skilled in the art should appreciate that one aspect described herein may be implemented independently of any other aspects and that two or more of these aspects may be combined in various ways. For example, an apparatus may be implemented and/or a method practiced using any number of the aspects set forth herein. Additionally, such an apparatus may be implemented and/or such a method may be practiced using other structure and/or functionality in addition to one or more of the aspects set forth herein.
The invention provides a method for quickly screening and eliminating false targets of a multi-channel SAR image based on position information, which comprises the following steps:
the method comprises the following steps: acquiring a multi-channel SAR image, imaging related parameters and a detection result, wherein a target to be detected is positioned at the upper left corner and the lower right corner of four-world coordinates in the multi-channel SAR image as shown in figure 1.
Step two: calculating the distance between the false target and the real target; sequencing all targets detected in the SAR image according to the azimuth direction, and calculating the distance interval between two adjacent targets; grouping targets based on the distance, the distance interval, and a fault tolerance threshold.
The basis of grouping in this step is: according to the imaging characteristics of the multi-channel false target, the false targets are distributed at equal intervals on two sides of the real target, and as shown in fig. 2, if the target-target interval meets the fault-tolerant threshold range, the false targets are grouped into one group.
When the step is implemented, the target is separated from the target by n 1 Can be expressed as:
wherein N is a single zero padding The number of points after zero padding is carried out on the single channel azimuth direction, m is the number of distance gates where the target is located, R min Is the minimum pitch, f s For sampling frequency, T p Is the pulse width, R ref For reference slope distance, c is the speed of light.
Detecting all targets, and grouping the detection results meeting interval conditions, wherein the method comprises the following specific steps of:
(1) and arranging the coordinates of the target center points of the detection results of the same scene image in an ascending order according to the azimuth direction.
(2) Selecting a first target as a first group, and calculating a distance interval between the first target and an adjacent second target, assuming that coordinates of center points of the first and second targets are (x) 1 ,y 1 ) And (x) 2 ,y 2 ) Then its distance interval is:
if the following conditions are met:
|d-n 1 |≤l
and if the detection result does not meet the fault-tolerant threshold value, another group is formed.
(3) And repeating the steps, calculating the distance interval between each next target and the last target of each group, judging the conditions, if the fault-tolerant threshold is met, merging the targets into the corresponding group, and if the fault-tolerant threshold is not met, starting another group until all the targets are grouped.
(4) The target group with only one target is removed because when there is only one target in the group, it means that the target is a real target.
Step three: carrying out uniformity judgment on the attributes of the same group of targets, and removing targets without the same attribute;
because a plurality of real targets exist in the image, in order to avoid grouping errors caused by over-dense targets, the direction, size and other attributes of the targets are calculated for each group of targets respectively, the identity is judged, and the target coordinates which do not meet the identity are removed from the groups, so that the false targets are further grouped according to the identity.
The specific process of the step is as follows:
(1) and cutting out 200 × 200 or 400 × 400 slices by taking the coordinates of the target center point as the center according to the detection result information, wherein the size of the slice is determined according to the image resolution.
(2) And carrying out Otsu threshold segmentation on the slice containing the target to obtain a segmented binary image.
(3) And calculating the minimum circumscribed rectangle of the binary image target area to obtain the attributes of the target direction, size and the like.
(4) And D, judging the identity of the same group of target attributes in the step two, and removing the targets which do not belong to the same attribute from grouping.
(5) The target group with only one target is removed because there is only one target in the group, meaning that the target is a real target.
Step four: and performing false target elimination on each group of targets based on the scattering characteristics of the targets.
In the step, each group of targets obtained in the step three is considered to comprise a real target and a plurality of false targets in the same group, and the false targets are judged according to the brightness and scattering characteristic difference between the false targets and the real targets of the same target.
The specific process of removing the false target based on the dispersion characteristic of the target comprises the following steps: for the targets in each group, mean and variance statistics (based on the four-boundary coordinate range of the targets) are respectively carried out, wherein the mean reflects the brightness information of the targets, and the variance reflects the scattering degree of the targets. And (3) considering the target with the maximum mean value and variance as a real target, and judging other targets as false targets, so as to eliminate the false targets, as shown in fig. 3.
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 (8)
1. A method for quickly screening and eliminating false targets of a multi-channel SAR image is characterized by comprising the following steps:
calculating the distance between the false target and the real target;
sequencing all targets detected in the SAR image according to the azimuth direction, and calculating the distance interval between two adjacent targets;
grouping targets based on the distance, the distance interval, and a fault tolerance threshold;
carrying out uniformity judgment on the attributes of the same group of targets, and removing targets without the same attribute;
and aiming at each group of targets, carrying out false target elimination based on the scattering characteristics of the targets.
2. The method for quickly screening and eliminating the false targets of the multi-channel SAR image according to claim 1, characterized in that when the targets are grouped, a group where only one target is located is removed.
3. The method for quickly screening and removing the false target of the multi-channel SAR image according to claim 1, wherein the grouping of the targets based on the distance, the distance interval and the fault-tolerant threshold comprises: if | d-n is satisfied 1 Dividing two adjacent targets into a group if | ≦ l, wherein l is a fault tolerance threshold, and n is 1 The distance between the false target and the real target is shown.
4. The method for rapidly screening and eliminating the false target of the multi-channel SAR image as claimed in claim 1, wherein the false target is at an interval n distributed at equal intervals on both sides of the real target 1 Comprises the following steps:
wherein N is a single zero padding The number of points after zero padding is carried out on the single-channel azimuth direction, m is the number of range gates where the target is located, R min Is the minimum pitch, f s For sampling frequency, T p Is the pulse width, R ref For reference slope distance, c is the speed of light.
5. The method for rapidly screening and eliminating the false targets of the multi-channel SAR image according to claim 1, wherein the uniformity judgment of the attributes of the same group of targets is as follows: and performing uniformity judgment according to the target direction and the target size.
6. The method for quickly screening and eliminating the false target of the multi-channel SAR image according to claim 1, characterized in that the target direction and the target size are determined according to the following modes:
cutting out a section with a set size by taking the target central point as a center;
carrying out Otsu threshold segmentation on the slice containing the target to obtain a segmented binary image;
and calculating the minimum circumscribed rectangle of the binary image target area to obtain the target direction and the target size.
7. The method for rapidly screening and rejecting the false targets of the multi-channel SAR image according to claim 1, wherein after the targets which do not have the same attribute are removed, if only one target exists in the group, the group is removed.
8. The method for rapidly screening and eliminating the false target of the multi-channel SAR image according to claim 1, wherein the false target elimination based on the target dispersion characteristic comprises the following specific processes: and respectively carrying out mean value and variance statistics on the targets in each group, wherein the mean value reflects the brightness information of the targets, the variance reflects the scattering degree of the targets, the target with the maximum mean value and variance is regarded as a real target, and other targets are judged as false targets, so that the false targets are removed.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210580600.4A CN115015926A (en) | 2022-05-25 | 2022-05-25 | Multichannel SAR image false target rapid screening and removing method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210580600.4A CN115015926A (en) | 2022-05-25 | 2022-05-25 | Multichannel SAR image false target rapid screening and removing method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115015926A true CN115015926A (en) | 2022-09-06 |
Family
ID=83070888
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210580600.4A Pending CN115015926A (en) | 2022-05-25 | 2022-05-25 | Multichannel SAR image false target rapid screening and removing method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115015926A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115840226A (en) * | 2023-02-27 | 2023-03-24 | 中国科学院空天信息创新研究院 | Method for quickly detecting target by using azimuth multi-channel ScanSAR |
-
2022
- 2022-05-25 CN CN202210580600.4A patent/CN115015926A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115840226A (en) * | 2023-02-27 | 2023-03-24 | 中国科学院空天信息创新研究院 | Method for quickly detecting target by using azimuth multi-channel ScanSAR |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109270497B (en) | Multidimensional parameter pre-sorting method for radar pulse signals | |
| DE60028433T2 (en) | Method for determining the location of terrestrial mobile radio devices from an aircraft | |
| CN107909002B (en) | Sea-land segmentation method of infrared remote sensing image based on coastline matching | |
| CN107025654B (en) | SAR image self-adaptive ship detection method based on global iterative inspection | |
| CN112699315B (en) | AIS data-based ship abnormal behavior detection method | |
| CN107942329B (en) | Method for detecting sea surface ship target by maneuvering platform single-channel SAR | |
| CN108845300B (en) | Constant false alarm processing method for scene surveillance radar | |
| CN110866926B (en) | Infrared remote sensing image rapid and fine sea-land segmentation method | |
| CN103197302A (en) | Target location extraction method applicable to through-the-wall radar imaging | |
| Welch et al. | The effect of spatial resolution upon texture‐based cloud field classifications | |
| CN115015926A (en) | Multichannel SAR image false target rapid screening and removing method | |
| CN107392887A (en) | A kind of heterogeneous method for detecting change of remote sensing image based on the conversion of homogeneity pixel | |
| CN114330509A (en) | Method for predicting activity rule of aerial target | |
| CN108983194B (en) | Target extraction and condensation method based on ground monitoring radar system | |
| CN112989940B (en) | Raft culture area extraction method based on high-resolution third satellite SAR image | |
| CN112363144A (en) | Distance fuzzy and azimuth fuzzy identification method for ring scan radar | |
| CN116299195A (en) | Radar signal processing method based on TOA sequence relativity | |
| CN111768442B (en) | Track initiation method and system based on hierarchical clustering and logic method | |
| CN105389794B (en) | SAR target detection false-alarm minimizing technologies based on priori scene knowledge | |
| RU2596610C1 (en) | Method of search and detection of object | |
| CN114152939A (en) | Simultaneous multi-beam radar point trace condensation method | |
| CN113420594A (en) | SAR image ship detection method based on improved Faster R-CNN | |
| CN111797940A (en) | Image identification method based on ocean search and rescue and related device | |
| CN114419453B (en) | Group target detection method based on electromagnetic scattering characteristics and topological configuration | |
| Wang et al. | DBSCAN clustering algorithm of millimeter wave radar based on multi frame joint |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 100081 Room 401, science and technology building, building 683, zone 2, No.5 Zhongguancun South Street, Haidian District, Beijing Applicant after: BIT RACO ELECTRONIC INFORMATION TECHNOLOGY CO.,LTD. Applicant after: Beijing Institute of Technology Leike Aerospace Information Technology Co.,Ltd. Address before: 100081 Room 401, science and technology building, building 683, zone 2, No.5 Zhongguancun South Street, Haidian District, Beijing Applicant before: BIT RACO ELECTRONIC INFORMATION TECHNOLOGY CO.,LTD. Applicant before: BEIJING RACO RADAR TECHNOLOGY RESEARCH INSTITUTE Co.,Ltd. |