CN108830885B - Detection false alarm suppression method based on multi-directional differential residual energy correlation - Google Patents

Detection false alarm suppression method based on multi-directional differential residual energy correlation Download PDF

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CN108830885B
CN108830885B CN201810547751.3A CN201810547751A CN108830885B CN 108830885 B CN108830885 B CN 108830885B CN 201810547751 A CN201810547751 A CN 201810547751A CN 108830885 B CN108830885 B CN 108830885B
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false alarm
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images
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target
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CN108830885A (en
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金挺
王世涛
王虎妹
董小萌
高宏霞
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Beijing Institute of Spacecraft System Engineering
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
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    • G06T7/254—Analysis of motion involving subtraction of images

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Abstract

The invention discloses a detection false alarm suppression method based on multi-directional differential residual energy correlation, which is used for identifying a false alarm target according to the characteristic that background clutter energy is unstable under different registration precisions: and performing multi-directional difference and residual energy distribution correlation calculation on the suspected target area, and quickly and effectively distinguishing the false alarm target according to the energy distribution change condition of the multi-directional difference image. For the false alarm target, the higher the registration precision is, the smaller the difference image residual energy is, the lower the registration precision is, and the larger the difference image residual energy is; for a real moving target, the difference image residual energy distribution under different registration precisions is almost unchanged. The multi-direction differential residual energy correlation false alarm suppression method can effectively distinguish the false alarm target caused by the background clutter, thereby realizing the suppression of the false alarm.

Description

Detection false alarm suppression method based on multi-directional differential residual energy correlation
Technical Field
The invention relates to a detection false alarm suppression method based on multi-directional differential residual energy correlation, and belongs to the field of target detection processing.
Background
The research of the detection technology of the moving weak and small target under the complex background has important application in civil use, aerospace and military. Due to the long imaging distance and the influence of a complex background, the target is dotted in the image, enough texture information and shape information are lacked, and the signal to noise ratio of the target in the image is low, so that the difficulty of detecting the weak and small targets is greatly increased. At present, an optical detection system generally performs complex background clutter suppression on an acquired image by adopting a filtering-based method, a wavelet-based method, a morphology-based method, a background difference-based method and the like, performs suspected target extraction on a background suppression image, and then performs target trajectory fitting by using a multi-frame association mode, thereby realizing detection of a moving weak and small target.
In the target detection method based on image differential background suppression, the most critical step is to realize high-precision registration of the background, so that complex background clutter can be eliminated as much as possible through the difference of two images, and only target and system noise components are remained in a difference image. However, due to the complexity of the actual scene image, it is impossible to adapt to the complex characteristics of intensity, distribution and the like of various backgrounds by the background registration differential suppression technology, so as to completely eliminate the complex backgrounds. Therefore, in the background difference image, there is a background clutter remaining due to the registration error, which causes a detection false alarm.
Disclosure of Invention
The technical problem of the invention is solved: the invention provides a detection false alarm suppression method based on multi-directional differential residual energy correlation, which solves the problem of high false alarm of low signal-to-noise ratio weak and small target detection under a complex background.
The technical solution of the invention is as follows:
a detection false alarm suppression method based on multi-direction differential residual energy correlation comprises the following specific steps:
(1) the optical detection system acquires two images with a certain time interval, performs image registration on the two images, and performs differential calculation on the registered images to obtain a residual image;
(2) performing suspected target detection on the residual image to obtain position coordinates of all suspected targets;
(3) intercepting a first subregion difference image on the residual image in the step 1 according to the suspected target position;
(4) on the registration image, a multi-directional differential area is drawn by taking the suspected target position coordinate obtained in the step 2 as a center, the area is traversed, a plurality of subarea images with the same area as the first subarea differential image in the step 3 are intercepted, the difference calculation is carried out on the two corresponding subarea images to obtain second subarea differential images, and each second subarea differential image is subjected to correlation calculation with the first subarea differential image in the step 3 to form a correlation coefficient sequence;
(5) calculating the variation of the correlation coefficient sequence in the step 4, if the variation is larger than a set threshold, determining the suspected target as a false alarm, and deleting the suspected target from the position of the suspected target obtained in the step 2; and repeating the steps 3-5 to finish the discrimination of all suspected targets and realize the false alarm suppression.
And 5, obtaining the variation of the correlation coefficient sequence by calculating the root mean square value of the correlation coefficient sequence.
In step 4, traversing the region according to the step length s, wherein the step length s is 0.5-1.5D, and the selection of D is smaller than the moving distance D of the target in the two images, namely 1< D < D.
In step 5, the threshold value is 0.01.
Compared with the prior art, the invention has the advantages that:
according to the image difference target detection basic principle, the false alarm target identification is carried out by utilizing the characteristic that background clutter energy is unstable under different registration precisions. The method can effectively inhibit false alarm targets caused by background clutter, and the false alarm rate of single background differential target detection can be reduced by more than two orders of magnitude.
Drawings
FIG. 1 is a flow chart of the present invention
FIG. 2 is a diagram of target detection results before false alarm suppression;
fig. 3 false alarm suppression results of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings.
The present embodiment provides a method for suppressing a detection false alarm based on multi-directional differential residual energy correlation, which mainly includes the steps shown in fig. 1, and the specific implementation process is as follows:
(1) the optical detection system acquires two images with a certain time interval, and for the line scanning detection system, a double-line detection system can be adopted, and two detection images with a certain time interval are acquired by one-time scanning; for an area array detection system, a video imaging mode is adopted, and a certain time interval exists between two continuous images.
(2) Performing image registration on the two images by adopting a sub-pixel registration method, wherein the two registered images are represented as Ir1，Ir2The image registration method can adopt a sub-pixel image registration algorithm;
(3) carrying out difference processing on the registered image, and inhibiting a complex background to obtain a residual image;
(4) and (3) carrying out target detection on the residual image in the step (3) by utilizing a threshold segmentation method to obtain all suspected target positions, and marking as (x)i,yi) N, where n is the number of suspected targets, and the partition threshold may be calculated by using an iterative threshold algorithm, an adaptive threshold method based on a constant false alarm, or the like.
(5) For the ith suspected target, its position (x)i,yi) Is centered at Ir1In a sub-area of size a x a, denoted as R1i(ii) a In Ir2In a sub-area of size a x a, denoted as R2i(ii) a For weak and small target detection, the size of the sub-region is recommended to be 9 multiplied by 9-15 multiplied by 15; r1i，R2iDifference calculation is carried out, and the difference image is recorded as Diffi；
(6) In Ir2In (x)i,yi) For the center, in the area range of (a + d) × (a + d), d should be selected to be smaller than the moving distance of the target in the two imagesFrom D, i.e. 1<d<D; traversing the region according to the step length s, wherein the step length s can be 0.5-1.5d, and when s is a non-integer, intercepting the sub-region by sub-pixel resampling; truncating sub-regions of size a x a and R1iCarrying out difference calculation to obtain a multidirectional difference image sequence, which is recorded as Seqi；
(7) Will SeqiAll the difference images in (1) are respectively compared with DiffiPerforming correlation calculation to form a correlation coefficient sequence, and recording the correlation coefficient sequence as Cori；
(8) Calculating CoriCalculating the root mean square value of all correlation coefficients, if the root mean square value is greater than a threshold value Tc, judging the ith suspected target as a false alarm, and deleting the ith suspected target from the suspected target result; generally, the value of Tc can be 0.01, the value of Tc needs to be adjusted according to the complexity of the background, and when the complexity of the background is high and the texture is rich, the value of Tc can be improved;
(9) and (5) repeating the steps 5-8 for each suspected target to finish the discrimination of all the suspected targets and realize the suppression of the false alarm target.
The results of simulation experiments for suppressing the infrared weak and small target detection false alarm by using the method are shown in fig. 2 and fig. 3; fig. 2 is a target detection result before false alarm suppression, where a green box represents a correct target point, and a red box represents a false alarm point, and the detection result contains more false alarm points due to weak target energy and strong background clutter; fig. 3 shows the result of false alarm suppression using the method of the present invention.
The simulation experiment result shows that the method can effectively remove the false alarm target caused by the background clutter and reduce the false alarm rate of the detection of the weak and small targets.
Those skilled in the art will appreciate that those matters not described in detail in the present specification are well known in the art.

Claims (3)

1. A detection false alarm suppression method based on multi-directional differential residual energy correlation is characterized by comprising the following specific steps:
(1) the optical detection system acquires two images with a certain time interval, performs image registration on the two images, and performs differential calculation on the registered images to obtain a residual image;
(2) performing suspected target detection on the residual image to obtain position coordinates of all suspected targets;
(3) intercepting a first subregion difference image on the residual image in the step (1) according to the suspected target position;
(4) on the registration image, drawing a multi-directional differential area by taking the suspected target position coordinate obtained in the step (2) as a center, traversing the area, intercepting a plurality of subarea images with the same area as the first subarea differential image in the step (3), and carrying out differential calculation on two subarea images corresponding to the two registration images to obtain second subarea differential images, wherein each second subarea differential image is subjected to correlation calculation with the first subarea differential image in the step (3) to form a correlation coefficient sequence;
(5) calculating the variation of the correlation coefficient sequence in the step (4), if the variation is larger than a set threshold, judging the suspected target as a false alarm, and deleting the suspected target from the suspected target position obtained in the step (2); repeating the steps (3) to (5), finishing the discrimination of all suspected targets and realizing the false alarm suppression;
and (5) obtaining the variation of the correlation coefficient sequence in the step (5) by calculating the root mean square value of the correlation coefficient sequence.
2. The method as claimed in claim 1, wherein in step (4), the region is traversed according to a step s, the step s is 0.5-1.5D, and D is selected to be smaller than the moving distance D of the target in the two images, i.e. 1< D.
3. The method as claimed in claim 2, wherein the threshold in step (5) is 0.01.
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