Wilcoxon-gauss-odds nonparametric detection method for ship target in sar image based on max selection logic

Abstract

This invention discloses a Wilcoxon-GO nonparametric detection method for ship targets in SAR images based on a large-scale selection logic, belonging to the field of image detection technology. The steps of this invention include: first, setting a detection window, which includes a detection unit, a reference window composed of a leading edge window and a trailing edge window, and a protection region; then, using the detection window to traverse the SAR ship remote sensing image pixel by pixel, with each movement distance equal to the width of the detection unit; subsequently, calculating the mean ratio (MR) of the leading edge and trailing edge windows, and using a threshold to determine whether the clutter is uniform or non-uniform; then, selecting a reference window based on the determination result; for uniform clutter, selecting the entire reference window, and for non-uniform clutter, selecting the leading edge / trailing edge window with the larger mean and widening it by 1; finally, calculating the detection statistic. R Set detection threshold T ,according to R and T The relationship between the detection unit and the target is determined. This invention maintains an approximately constant false alarm rate in non-uniform backgrounds at clutter edges, significantly outperforming the excessive increase in false alarms observed with traditional Wilcoxon technology, thus greatly improving false alarm control capabilities.

Description

Technical Field

[0001] This invention relates to the field of image detection technology, specifically to a Wilcoxon-GO nonparametric detection method for ship targets in SAR images based on the selection of the largest value logic. Background Technology

[0002] In the field of modern marine monitoring, synthetic aperture radar (SAR) has become a core technology for ship target identification and tracking due to its unique advantages such as all-weather, all-day operation, wide coverage, high resolution, and ability to penetrate clouds and fog. With the increasing frequency of global marine activities, the demand for accurate, rapid, and stable detection of ship targets in SAR images is becoming increasingly urgent, whether in civilian areas such as maritime traffic supervision and fisheries resource protection, or in other areas such as ship detection and situational awareness.

[0003] Among various techniques for ship target detection in SAR images, the constant false alarm rate (CFAR) detection technique has become the most widely used and effective method due to its ability to stably control the false alarm probability in complex backgrounds. Since the introduction of the two-parameter CFAR detection method based on Gaussian distribution, research on ship target detection in SAR images has long revolved around parametric CFAR detection algorithms. The core idea is to assume that the sea clutter background in the SAR image follows a specific statistical distribution (such as Gaussian distribution, negative exponential distribution, Rayleigh distribution, K distribution, alpha stable distribution, G0 distribution, log-normal distribution, Gamma distribution, or mixed Rayleigh distribution, etc.), then estimate the distribution parameters using clutter sample selection or elimination techniques, and finally set the detection threshold based on the parameters to achieve target discrimination.

[0004] However, sea clutter in SAR images is highly complex and variable due to the influence of marine environment and imaging conditions such as wind, waves, ocean currents, and internal waves. The statistical characteristics of actual sea clutter often deviate from the distribution model assumed by parametric CFAR. Once a mismatch occurs, the detection performance of parametric CFAR will deteriorate significantly, and even a large number of missed or false detections will occur. At the same time, parametric CFAR requires complex calculations to estimate distribution parameters, which has the inherent defects of excessive computation and difficulty in meeting real-time detection requirements, thus limiting its application in high-speed SAR data processing scenarios.

[0005] To overcome the limitations of parametric CFAR, the applicant previously attempted to use Wilcoxon nonparametric CFAR detection technology for ship target detection in SAR images. However, practice has shown that Wilcoxon nonparametric CFAR still has significant technical shortcomings: in ship target detection scenarios in SAR images, due to factors such as wind speed changes, ocean currents, internal ocean waves, or oil pollution on the sea surface, a special phenomenon of boundary between bright and dark areas often appears on the sea surface, i.e., clutter edges, resulting in a non-uniform distribution of background clutter. Under this non-uniform background, the false alarm probability of Wilcoxon nonparametric CFAR will increase excessively—a large number of clutter signals from non-target areas will be misidentified as ship targets, which greatly limits its practical value in complex marine environments.

[0006] Therefore, how to improve the false alarm control capability of Wilcoxon nonparametric CFAR detection technology in non-uniform backgrounds with clutter edges has become a key technical problem that urgently needs to be solved in the field of ship target detection in SAR images, and it is also the core improvement direction of this invention.

[0007] The information disclosed in this background section is intended only to enhance the understanding of the overall background of the invention and should not be construed as an admission or in any way implying that the information constitutes prior art known to those skilled in the art. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention provides a Wilcoxon-GO nonparametric detection method for ship targets in SAR images based on selection logic, thereby resolving the issues raised in the background section.

[0009] This invention provides the following technical solution: a Wilcoxon-GO nonparametric detection method for ship targets in SAR images based on the selection of the largest value logic, comprising the following steps:

[0010] The detection sliding window includes a detection unit located in the middle, a reference sliding window surrounding the detection unit, and a protection area between the detection unit and the reference sliding window; the reference sliding window includes a front edge sliding window and a rear edge sliding window;

[0011] A detection sliding window is used to traverse the SAR-acquired ship target remote sensing images pixel by pixel. The distance the detection sliding window moves each time is equal to the width of the detection unit, i.e. t 1 pixel;

[0012] Obtain the mean ratio MR of the leading edge sliding window and the trailing edge sliding window;

[0013] Set threshold MR and and Compare;

[0014] like If so, the reference samples of the leading edge sliding window and the trailing edge sliding window are determined to be uniform clutter;

[0015] like If the reference samples of the leading edge sliding window and the trailing edge sliding window are determined to be non-uniform clutter, then the clutter edge situation in the detection window (such as the boundary between light and dark areas of the sea surface caused by wind speed, ocean current, internal waves or sea surface oil pollution) is detected.

[0016] If it is determined to be uniform clutter, the entire reference sliding window is selected as the reference sample for threshold calculation;

[0017] If the noise is determined to be non-uniform clutter, the leading or trailing edge sliding window with a larger sample mean is selected as the reference sample for threshold calculation. When selecting the leading or trailing edge sliding window, its width is doubled, becoming 2... q ;

[0018] Doubled to 2 q Subsequently, when the Wilcoxon-GO nonparametric detector sets the detection threshold using the entire reference window, the leading edge window, or the trailing edge window, the number of reference cells in the entire reference window, the leading edge window, or the trailing edge window will be the same, all being [missing information]. n .

[0019] The statistical measures for detecting ship targets in SAR images are calculated according to equation (1). R ;

[0020] Equation (1) is: ;

[0021] in, It is the test sample of the detection unit. It is a reference sample for the sliding window. u It is a unit step function. m It refers to the number of samples tested. n This is the reference sample size;

[0022] Set detection threshold T ,like R > T If so, it is determined that a ship target exists in the detection unit; if R ≤ T If no ship target is detected, it is determined that there is no ship target in the detection unit.

[0023] It should be noted that this invention proposes an improved Wilcoxon nonparametric detector for SAR image ship targets based on the "Greatest Of" logic. Therefore, the method of this invention is simply referred to as the Wilcoxon-GO nonparametric detector.

[0024] Preferably, the width of the detection unit ist Each pixel has a width of [number] for both the leading and trailing edges of the sliding window. q pixels, the width of the protected area is . g 1 pixel.

[0025] Preferably, the mean ratio MR is calculated according to equation (2); equation (2) is:

[0026] ;

[0027] in, and These are the mean values ​​of the reference samples in the leading edge sliding window and the trailing edge sliding window, respectively.

[0028] Preferably, the detection threshold T Based on the preset false alarm probability P fa is calculated according to equation (3); equation (3) is:

[0029] ;

[0030] Indicates from 1 to m + n Selected from m There are 10 values ​​and their sum is 1 k The number of possible choices; n This is the number of reference samples. m It is the number of samples tested. N = m + n .

[0031] Preferably, in equation (2) The calculation formula is obtained from formula (4); formula (4) is:

[0032] .

[0033] Preferably, the reference sliding window is used to set the detection threshold; the protection unit is used to prevent the pixel energy of adjacent ship targets from leaking into the detection unit.

[0034] Preferred threshold The probability of a uniform background being incorrectly identified as non-uniform is determined based on 0.1.

[0035] The Wilcoxon-GO nonparametric detection method for ship targets in SAR images based on the maximum selection logic provided in this invention has the following beneficial effects:

[0036] (1) The Wilcoxon-GO nonparametric detection method of the present invention maintains the original detection capability of Wilcoxon in a uniform background, and the false alarm rate in the clutter edge is approximately constant, which is significantly better than the excessive increase of false alarm in the traditional Wilcoxon nonparametric detector, and the false alarm control capability is greatly improved.

[0037] (2) In the case of non-uniform background (clutter edges), select the leading / trailing edge sliding window with a larger sample mean and widen it to 2. q Because the average value of strong clutter regions is large in clutter edge scenes, "selecting a larger value" can set a higher detection threshold based on strong clutter samples, avoiding the increase in false alarm rate caused by low thresholds in weak clutter regions. Attached Figure Description

[0038] Figure 1 A schematic diagram of the detection sliding window of the Wilcoxon-GO nonparametric detector for ship targets in SAR images based on the selection logic;

[0039] In the diagram, the yellow area on the right is the leading edge sliding window, and the green area on the left is the trailing edge sliding window.

[0040] Figure 2 A schematic diagram showing clutter edges entering the Wilcoxon-GO and the Wilcoxon nonparametric detector reference sliding window;

[0041] in, Figure 2 The upper part is the Wilcoxon nonparametric detector reference sliding window; Figure 2 The lower half of the image is the Wilcoxon-GO nonparametric detector reference sliding window;

[0042] Figure 3 False alarm probability of Wilcoxon-GO nonparametric detector P fa clutter edge position L The curve of change;

[0043] Figure 4 This is a schematic diagram illustrating the specific implementation process of the Wilcoxon-GO nonparametric detector. Detailed Implementation

[0044] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] The following is in conjunction with the appendix Figure 1-4The present invention will be further described in detail below, along with specific embodiments.

[0046] To address the problems mentioned in the background section, this invention provides a Wilcoxon-GO nonparametric detection method for ship targets in SAR images based on the selection of the largest value logic, in order to solve the aforementioned technical problems. The specific technical solution is as follows.

[0047] (a) Detection using the Wilcoxon-GO nonparametric detection method

[0048] The steps of the Wilcoxon-GO nonparametric detection method for ship targets in SAR images based on the selection of the largest value logic include:

[0049] Step 1: Initialize the detection sliding window and traversal method

[0050] For remote sensing images acquired by spaceborne synthetic aperture radar (SAR), a Wilcoxon-GO nonparametric detector is used for traversal detection:

[0051] The step size of the detection sliding window moving along the image each time is the width of the detection unit. t 100 pixels (of which) t =2, which is the recommended parameter);

[0052] The detection sliding window consists of two parts: a leading edge sliding window (right side area) and a trailing edge sliding window (left side area), both with an initial width of [missing information]. q (in q =3, which is the recommended parameter);

[0053] A protective area with a width of [missing information] is provided between the detection unit and the reference unit (front / back sliding window). g (in g =60, which needs to be greater than the maximum length of the ship in the SAR image to prevent energy leakage from adjacent targets.

[0054] Step 2: Refer to the dynamic selection and width adjustment of the sliding window.

[0055] The Wilcoxon-GO nonparametric detector selects the reference cell used to set the detection threshold through the following logic:

[0056] When "Entire Reference Sliding Window" is selected, all reference cells of the leading edge sliding window and the trailing edge sliding window are used directly;

[0057] When selecting "Front Edge Sliding Window" or "Rear Edge Sliding Window", its width needs to be increased by 1 to 2 times. q This ensures that regardless of which reference window is selected, the number of reference cells it contains is always the same. n (To ensure statistical consistency).

[0058] Step 3: Calculate the mean ratio (MR) and determine clutter uniformity.

[0059] Define the mean ratio (MR): Calculate the mean of the reference sample in the leading-edge sliding window. M A and the mean of the trailing edge sliding window reference sample M B, the ratio of the two is the mean ratio:

[0060] ;

[0061] Determine clutter uniformity: Compare MR with threshold and its reciprocal K Comparison (where) =1.143, determined through simulation, the probability of a uniform background being misclassified as non-uniform is 0.1).

[0062] like The reference samples for the leading and trailing edges of the sliding window are determined to be a uniform background.

[0063] like It is determined to be a non-uniform background (i.e., there are "clutter edges").

[0064] Step 4: Select a reference sliding window based on clutter type

[0065] Uniform background: The detection threshold is set using the reference cell of the entire reference sliding window;

[0066] Non-uniform background (clutter edges): Select the leading edge sliding window or the trailing edge sliding window with a larger mean (i.e., the "select larger (GO)" logic) to set the detection threshold.

[0067] Step 5: Calculate the detection statistic R And determine whether the target exists.

[0068] Define detection statistics R Let the sample of the detection unit be... x 1, x 2,…, The sample of the reference unit is y 1, y 2,…, Statistic R The total number of times the detected sample was greater than the reference sample:

[0069] ;

[0070] Determine the detection threshold T And determine: threshold T Based on the set false alarm probability The solution is obtained using the following formula:

[0071] ;

[0072] in, Indicates from 1 to m + n Selected from m There are 10 values ​​and their sum is 1 k The number of possible choices; n This is the number of reference samples. m It is the number of samples tested. N = m + n .

[0073] like R >T indicates that a ship target is detected in the detection unit;

[0074] like R ≤ T The system determined that there were no ship targets in the detection unit.

[0075] Additional notes: Clutter edge modeling parameters

[0076] In this method, clutter edge scenes are modeled based on the following parameters (used to verify method performance):

[0077] Strong clutter region: conforms to shape parameters c =1.2 Weibull distribution;

[0078] Weak clutter region: conforms to shape parameters c =2.0 Weibull distribution (i.e. Rayleigh distribution);

[0079] The power ratio of strong clutter to weak clutter is CNR = 5dB.

[0080] (ii) Detection using the Wilcoxon nonparametric detection method

[0081] The conventional Wilcoxon nonparametric detection method was used for detection; it shares the same experimental scenario as the Wilcoxon-GO nonparametric detection method: that is, the clutter edge is modeled as a Weibull distribution, and the strong clutter region is a region with shape parameters of . c The shape parameter of the weak clutter region is given by the Weibull distribution with a value of 1.2. c =2.0 (i.e. Rayleigh distribution), the power ratio of strong clutter to weak clutter is called CNR=5dB.

[0082] Furthermore, the parameters of both the Wilcoxon nonparametric detector and the Wilcoxon-GO nonparametric detector are set to... t =2, q =3 and g =60. Among them, Figure 2The diagram shows the clutter edge entering the Wilcoxon-GO nonparametric detector and the Wilcoxon nonparametric detector reference sliding window from the right side (leading sliding window). Strong clutter regions are shown in gray, and the locations of the strong clutter are indicated by "". L ".

[0083] (III) Test Results

[0084] Figure 3 The false alarm probabilities of the Wilcoxon-GO nonparametric detector and the Wilcoxon nonparametric detector are given. clutter edge position L The curves show the changes in the false alarm probability of the detectors. It can be seen that when the detectors are not in the strong clutter region, their false alarm probabilities decrease. However, when the detectors enter the strong clutter region, the false alarm probability of the Wilcoxon nonparametric detector increases excessively, which severely violates the constraint requirements for the detector's false alarm probability.

[0085] However, after the detection unit enters the strong clutter region, the false alarm probability of the Wilcoxon-GO nonparametric detector remains approximately constant near the set value, with a lower "secondary false alarm spike" only appearing after the strong clutter region enters the tail of the reference sliding window.

[0086] Therefore, the Wilcoxon-GO nonparametric detector has significantly improved false alarm control capability at clutter edges compared to the Wilcoxon nonparametric detector. This is mainly because the Wilcoxon-GO nonparametric detector uses a leading-edge sliding window with a large sample mean to set the detection threshold with a higher probability after strong clutter enters the reference sliding window.

[0087] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A Wilcoxon-GO nonparametric detection method for ship targets in SAR images based on the selection of the largest value logic, characterized in that, Includes the following steps: The detection sliding window includes a detection unit located in the middle, a reference sliding window surrounding the detection unit, and a protection area between the detection unit and the reference sliding window; the reference sliding window includes a front edge sliding window and a rear edge sliding window; The detection sliding window is used to traverse the SAR-acquired ship target remote sensing images pixel by pixel, and the distance the detection sliding window moves each time is equal to the width of the detection unit; Obtain the mean ratio MR of the leading edge sliding window and the trailing edge sliding window; The mean ratio MR is calculated according to equation (2); equation (2) is: ； in, and These are the mean values ​​of the reference samples in the leading edge sliding window and the trailing edge sliding window, respectively. Set threshold K MR Threshold K MR The probability of a uniform background being incorrectly classified as non-uniform is determined based on 0.1; MR is compared with K respectively. MR and K -1 MR Compare; If K -1 MR ≤MR≤K MR If so, the reference samples of the leading edge sliding window and the trailing edge sliding window are determined to be uniform clutter; If MR <K -1 MR Or MR>K MR If so, the reference samples of the leading edge sliding window and the trailing edge sliding window are determined to be non-uniform clutter, that is, the clutter edge situation is detected in the sliding window; If it is determined to be uniform clutter, the entire reference sliding window is selected as the reference sample for threshold calculation; If it is determined to be non-uniform clutter, select the leading edge window or trailing edge window with a larger sample mean as the reference sample for threshold calculation, and double the width of the leading edge window or trailing edge window when selecting it. Calculate the statistic R for detecting ship targets in SAR images according to equation (1); Equation (1) is: ; Where, x i It is the detection sample of the detection unit, y j is the reference sample of the reference sliding window, u is the unit step function, m is the number of detection samples, and n is the number of reference samples; Set a detection threshold T. If R>T, then it is determined that there is a ship target in the detection unit; if R≤T, then it is determined that there is no ship target in the detection unit. The detection threshold T is based on the preset false alarm probability P. fa It is calculated from equation (3); equation (3) is: ； π m,n (k) represents the number of possible ways to select m values ​​from 1 to m+n such that their sum is k; N = m + n.

2. The Wilcoxon-GO nonparametric detection method for ship targets in SAR images based on the maximum selection logic as described in claim 1, characterized in that, The detection unit has a width of t pixels, the leading edge sliding window and the trailing edge sliding window each have a width of q pixels, and the protected area has a width of g pixels.

3. The Wilcoxon-GO nonparametric detection method for ship targets in SAR images based on the maximum selection logic as described in claim 1, characterized in that, The reference sliding window is used to set the detection threshold; the protection unit is used to prevent the pixel energy of adjacent ship targets from leaking into the detection unit.

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