SAR remote sensing detection performance evaluation method

Abstract

The invention discloses an SAR remote sensing detection performance evaluation method, and belongs to the technical field of remote sensing detection. According to the evaluation method, factors influencing the SAR detection performance are divided into two types, namely space performance influence factors and radiation performance influence factors, the space performance influence factors are represented by adopting Johnson's criterion, resolution and target feature size, and the radiation performance influence factors are represented by adopting Johnson's criterion. The radiation performance influence factors are characterized by adopting a backscattering coefficient difference, a target scattering characteristic and a background scattering characteristic, a resolution-limited probability model and a backscattering-limited probability model are respectively established, the probability of discovery, identification and confirmation for characterizing the SAR detection performance is further obtained, and a technical basis is provided for quantitative evaluation of the SAR detection performance.

Description

Technical Field

[0001] The invention relates to a SAR remote sensing detection performance evaluation method, belonging to the technical field of remote sensing detection. Background Art

[0002] Synthetic Aperture Radar (SAR) is one of the research hotspots in the field of international radar remote sensing, and its detection performance evaluation has always been difficult. SAR detection performance is affected by two factors: one is from the SAR system itself, including the radar system, correction system, imaging algorithm, etc.; the other is related to the ground object and its scene, including the target's geometric shape, movement speed, and dielectric constant. Unlike optical images, SAR images reflect the backscattering coefficient of electromagnetic waves of objects, so they have strong speckle noise, and also have unique quality problems such as ghosting, non-uniform gain, and motion blur.

[0003] At present, the evaluation of SAR detection performance is mainly based on the evaluation of SAR image quality. SAR detection performance is reflected by SAR images of different qualities. At present, the evaluation of SAR image quality is mainly an objective evaluation method. By constructing a mathematical model associated with the meaning of image capabilities, the image to be evaluated is calculated and analyzed to obtain the evaluation results. It is convenient and fast to use and the calculation is accurate. SAR image quality involves many independent quality factors, such as resolution, noise level, etc. Each quality factor can be quantitatively described by one or several values. However, a specific quality factor can generally only reflect the characteristics of a certain aspect of the SAR image, and it is difficult to fully and directly reflect the SAR imaging ability to discover, identify, and confirm the target. Moreover, this method belongs to the post-evaluation method, that is, it must be based on SAR images, and it is impossible to effectively evaluate the detection performance in the demonstration and design stages. Summary of the invention

[0004] The technical problem solved by the present invention is: to overcome the shortcomings of the prior art, and to propose a SAR remote sensing detection performance evaluation method, according to the backscatter coefficient difference and the corrected equivalent strip line logarithm, to establish a backscattering limited probability model and a resolution limited model respectively, to further calculate the backscattering limited probability and the resolution limited probability, and to quantitatively evaluate the SAR's target discovery, recognition, and confirmation detection capabilities.

[0005] The technical solution of the present invention is:

[0006] A SAR remote sensing detection performance evaluation method, comprising:

[0007] According to the backscattering characteristics of the target and background, and the signal-to-noise ratio within the radar synthetic aperture time, the detection index value is calculated to characterize the detection capability of SAR limited by backscattering.

[0008] Based on the detection index, the backscattering characteristic difference between the target and the background, a backscattering limited probability model is constructed, and the values ​​of the above two are substituted to obtain the backscattering limited probability;

[0009] A resolution-limited probability model is established to calculate the equivalent size of the target. According to the ground resolution of SAR remote sensing, the number of equivalent strip line pairs of the target is obtained. The number of equivalent strip line pairs required for the detection level of discovery, identification, and confirmation is substituted into the resolution-limited probability model to obtain the resolution-limited probability at the detection level of discovery, identification, and confirmation.

[0010] The probabilities of target discovery, identification and confirmation are calculated respectively by using the backscattering limited probability and the resolution limited probability at the discovery, identification and confirmation detection levels.

[0011] Furthermore, based on the detection index, the backscattering characteristic difference between the target and the background, a backscattering limited probability model is constructed, and the backscattering limited probability model is:

[0012]

[0013] Where P σ is the backscattering limited probability, MRΔσ is the detection index, Δσ is the backscattering characteristic difference between the target and the background, and E σ =2.7+0.7(Δσ / MRΔσ).

[0014] Furthermore, the calculation method of the detection index MRΔσ is:

[0015]

[0016] Where P t is the radar peak power, G t is the antenna gain, R is the distance from the point target to the radar, λ is the wavelength, l s is the system loss factor, k is the Boltzmann constant, T s is the system equivalent noise temperature, B s is the system receiving bandwidth, SNR DT is the threshold signal-to-noise ratio for human eyes to distinguish targets, n r is the range pulse compression gain, n a is the azimuth pulse compression gain, and M is the number of views.

[0017] Furthermore, the method for obtaining the number of equivalent strip line pairs required for discovery, identification and confirmation detection levels is as follows: based on the Johnson criterion, the number of equivalent strip line pairs required for discovery, identification and confirmation detection levels is obtained, and then the above-mentioned number of equivalent strip line pairs is corrected to obtain the final number of equivalent strip line pairs required for discovery, identification and confirmation detection levels for SAR remote sensing detection performance evaluation; wherein the correction factor takes a value of 2-4.

[0018] Furthermore, a resolution-limited probability model is established, and the resolution-limited probability model is:

[0019]

[0020] Where N is the number of target equivalent strip line pairs; n ε is the number of equivalent strip line pairs required at different detection levels, and the detection levels of discovery, identification, and confirmation are selected respectively; E ε =2.7+0.7(N / n ε );P J is the resolution-limited probability at the corresponding detection level.

[0021] Furthermore, the probability of target discovery, identification and confirmation is calculated respectively, and the calculation method is: backscatter limited probability P σ The resolution-limited probability P at the corresponding detection level J The product obtained by multiplying is the probability of the target at the corresponding detection level.

[0022] Furthermore, the probability of the target being discovered, identified, and confirmed is calculated respectively, and the calculation method is:

[0023] P=(1-P σ )×(1-P J )

[0024] Where P σ is the backscattering limited probability, P J is the resolution-limited probability at the corresponding detection level, and P is the probability of the target at the corresponding detection level.

[0025] The advantages of the present invention compared with the prior art are:

[0026] (1) The SAR detection performance evaluation method of the present invention takes into account both spatial performance factors and radiation performance factors, and uses resolution-limited probability and backscatter-limited probability to characterize them respectively. Finally, the detection performance of SAR is evaluated by the joint probability of discovery, recognition, and confirmation. Compared with the traditional evaluation method of SAR image quality indicators, the evaluation results are quantitative and intuitive, and can guide the design of SAR detection systems.

[0027] (2) The backscattering limited probability model proposed in the present invention can adaptively modify parameters such as the number of views and the threshold signal-to-noise ratio at which the human eye can distinguish targets according to the actual application scenario to adapt to different evaluation scenarios. It is more flexible in application and has strong operability.

[0028] (3) The traditional Johnson criterion is mainly used for evaluating the interpretability of optical detection, but not for application in the field of SAR detection. The present invention innovatively uses a correction factor to correct the traditional Johnson criterion based on the empirical relationship between optical detection capability and SAR detection capability to meet the needs of SAR detection performance evaluation. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0030] Figure 1 This is a flow chart of the SAR remote sensing detection performance evaluation method of the present invention. DETAILED DESCRIPTION

[0031] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0032] The present invention proposes a SAR remote sensing detection performance evaluation method, which divides the factors affecting the SAR detection performance into two categories, namely, spatial performance influencing factors and radiation performance influencing factors. The spatial performance influencing factors are characterized by Johnson criterion, resolution and target characteristic size, and the radiation performance influencing factors are characterized by minimum resolvable backscatter coefficient difference (MRΔσ), target scattering characteristics and background scattering characteristics. Resolution-limited and backscatter-limited probability models are established respectively, and the joint probability of discovery, recognition and confirmation that characterizes the SAR detection performance is further obtained, providing a technical basis for quantitative evaluation of SAR detection performance.

[0033] The present invention realizes the process as follows Figure 1 As shown, the specific steps are as follows:

[0034] Step 1: Assume that the backscattering coefficients of the target and background are σ t and σ b (m 2), which are used to characterize the scattering characteristics of the target and the background respectively. The backscattering difference Δσ=|σ t -σ b |.

[0035] Step 2: The signal-to-noise ratio within the synthetic aperture time is derived according to the radar equation, and then MRΔσ is derived by combining the signal-to-noise ratio threshold that the human eye can distinguish targets. MRΔσ is used as a comprehensive indicator to characterize the detection capability of SAR limited by backscattering.

[0036]

[0037] P t is the radar peak power, G t is the antenna gain, R is the distance from the point target to the radar, λ is the wavelength, l s is the system loss factor (l s >1), k is the Boltzmann constant, T s is the system equivalent noise temperature, B s is the system receiving bandwidth, SNR DT is the threshold signal-to-noise ratio for human eyes to distinguish targets, n r is the range pulse compression gain, n a is the azimuth pulse compression gain, and M is the number of views.

[0038] Step 3: To construct a backscatter-limited probability model and quantitatively evaluate the detection capability of SAR limited by backscatter, substitute MRΔσ and Δσ into the probability transfer function to obtain the backscatter-limited probability P σ :

[0039]

[0040] Among them, E σ =2.7+0.7(Δσ / MRΔσ).

[0041] Step 4: The detection capability limited by the resolution is related to the size of the target. The spatial characteristics of the target are characterized by an equivalent size. Assuming that the target size is L (length) and W (width), the present invention adopts a two-dimensional representation method and uses the geometric mean of the target length and width as the equivalent size of the target (a one-dimensional representation method can also be used, that is, using length or width as the equivalent size of the target), that is, the equivalent size is (L×W) 1 / 2 , then the target equivalent strip line pair number N is:

[0042]

[0043] Among them, GSD is the ground resolution of SAR remote sensing.

[0044] Step 5: Since the traditional Johnson criterion is based on the number of equivalent strip line pairs n required for different detection levels proposed by optical images, the traditional Johnson criterion is modified for SAR detection applications. The modified equivalent strip line pair number n is ε =ε·n, ε is the correction factor. According to the interpretation experience of professional image judges on optical images and SAR images, in general, to achieve the same image interpretation effect, the resolution of optical images is 3 times that of SAR images. It may vary in different situations. The present invention proposes that the optimal value range of ε is 2-4, and the value can be selected according to the situation. When the SAR resolution is high and the target scattering is strong, a number less than or equal to 3 can be selected, otherwise a number greater than or equal to 3 can be selected.

[0045] Step 6: Construct a resolution-limited probability model to quantitatively evaluate the detection capability of SAR limited by resolution. ε Substituting into the probability transfer function, we get the resolution-limited probability P J :

[0046]

[0047] Among them, E ε =2.7+0.7(N / n ε ).

[0048] Step 7: Based on the backscattering limited probability P σ and resolution-limited probability P J , constructing a joint probability model, the present invention adopts the method of multiplying the two probabilities as the joint probability, that is, it is considered that the target can be detected only when both restriction factors meet the conditions, and the probability of target discovery, recognition and confirmation is: P = P σ ×P J .

[0049] The following is further described by specific examples:

[0050] Taking the indicators of a hypothetical SAR satellite as an example, the detection performance of a certain type of tank is evaluated.

[0051] Step 1: Assuming the tank target is on wet soil, the difference Δσ between the target and the background backscatter is shown in Table 1:

[0052] Table 1 Statistical table of the difference Δσ between the backscatter of the target and the background

[0053] Azimuth (°) 0-20 20-40 40-60 60-80 80-100 100-120 120-140 140-160 160-180 <![CDATA[Median value (m 2 )]]> 2.88 0.58 0.47 0.87 24.90 0.63 0.14 0.19 3.59

[0054] Step 2: Assume that the SAR satellite indicator is: radar peak power P t 1200W, antenna gain G tis 30dB, the distance R from the target to the radar is 550km, the wavelength λ is 0.0306m, and the system loss factor is l s is 6dB, and the Boltzmann constant k is 1.38054×10 -23 J / K, system equivalent noise temperature T s is 290K, the system receiving bandwidth is B s 1×10 -9 Hz, the threshold signal-to-noise ratio (SNR) at which the human eye can distinguish targets DT is 2.25, and the distance pulse compression gain n r 5×10 4 , azimuth pulse compression gain n a 1.68×10 5 , the viewing number M is 1.

[0055] The calculated SAR MRΔσ is 0.6897m 2 .

[0056] Step 3: Substitute MRΔσ and Δσ into the probability transfer function to obtain the backscattering limited probability P σ , the results are shown in Table 2:

[0057] Table 2 Backscattering limited probability P σ Statistics

[0058] Azimuth (°) 0-20 20-40 40-60 60-80 80-100 100-120 120-140 140-160 160-180 <![CDATA[P σ ]]> 1.00 0.36 0.23 0.70 1.00 0.42 0.01 0.02 1.00

[0059] Step 4: Assuming the target size L is 9.83m and W is 3.66m, the equivalent size of the target is (L×W) 1 / 2 If the target distance is 6m and the GSD is 0.5m, then the target equivalent strip line pair number N is 6.

[0060] Step 5: The number of equivalent strip line pairs n required for the traditional Johnson criterion to find, identify and confirm the detection level is 0.75, 3, and 6 respectively. Assuming the correction factor ε is 3, the corrected number of equivalent strip line pairs n ε They are 2.25, 9 and 18 respectively.

[0061] Step 6: N and n ε Substituting into the probability transfer function, we get the resolution-limited probability P J , see Table 3.

[0062] Table 3 Resolution limited probability P J Statistics

[0063]

[0064] Step 7: Based on the backscattering limited probability P σ and resolution-limited probability P J, the target discovery, recognition and confirmation probabilities are shown in Table 4.

[0065] Table 4 Target discovery, identification and confirmation probability statistics

[0066]

[0067] Alternative solutions:

[0068] (1) The backscatter difference Δσ between the target and the background in step 1 can be real collected data or obtained by simulation.

[0069] (2) The equivalent size in step 4 can also be represented in one dimension, that is, length or width is used as the target equivalent size.

[0070] (3) The correction factor ε in step 5 is generally 2-4 and can be selected according to the actual detection environment.

[0071] (4) Step 7 can also adopt P = (1-P σ )×(1-P J ) is taken as the joint probability. At this time, it is considered that the target can be detected if one of the two limiting factors meets the condition.

[0072] The above-described embodiments are only preferred specific implementations of the present invention. Common changes and substitutions made by those skilled in the art within the scope of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. A SAR remote sensing detection performance evaluation method, characterized in that: include: According to the backscattering characteristics of the target and background, and the signal-to-noise ratio within the radar synthetic aperture time, the detection index value is calculated to characterize the detection capability of SAR limited by backscattering. Based on the detection index, the backscattering characteristic difference between the target and the background, a backscattering limited probability model is constructed, and the values ​​of the above two are substituted to obtain the backscattering limited probability; A resolution-limited probability model is established to calculate the equivalent size of the target. According to the ground resolution of SAR remote sensing, the number of equivalent strip line pairs of the target is obtained. The number of equivalent strip line pairs required for the detection level of discovery, identification, and confirmation is substituted into the resolution-limited probability model to obtain the resolution-limited probability at the detection level of discovery, identification, and confirmation. The probabilities of target discovery, identification and confirmation are calculated respectively by using the backscattering limited probability and the resolution limited probability at the discovery, identification and confirmation detection levels.

2. A SAR remote sensing detection performance evaluation method according to claim 1, characterized in that: Based on the detection index, the backscattering characteristic difference between the target and the background, a backscattering limited probability model is constructed. The backscattering limited probability model is: Where P σ is the backscattering limited probability, MRΔσ is the detection index, Δσ is the backscattering characteristic difference between the target and the background, and E σ =2.7+0.7(Δσ / MRΔσ).

3. A SAR remote sensing detection performance evaluation method according to claim 1 or 2, characterized in that: The calculation method of the detection index MRΔσ is: Where P t is the radar peak power, G t is the antenna gain, R is the distance from the point target to the radar, λ is the wavelength, l s is the system loss factor, k is the Boltzmann constant, T s is the system equivalent noise temperature, B s is the system receiving bandwidth, SNR DT is the threshold signal-to-noise ratio for human eyes to distinguish targets, n r is the range pulse compression gain, n a is the azimuth pulse compression gain, and M is the number of views.

4. The SAR remote sensing detection performance evaluation method according to claim 1, characterized in that: According to the equivalent size of the target and the ground resolution of SAR remote sensing, the number of equivalent strip line pairs of the target is obtained. The calculation method is: Where N is the number of equivalent strip line pairs of the target, GSD is the ground resolution of SAR remote sensing, and S is the equivalent size of the target.

5. A SAR remote sensing detection performance evaluation method according to claim 4, characterized in that: The target equivalent size S is (L×W) 1 / 2 , the target length is L and the width is W.

6. A SAR remote sensing detection performance evaluation method according to claim 4, characterized in that: The target equivalent size S is the target length or width.

7. The SAR remote sensing detection performance evaluation method according to claim 1, characterized in that: The method for obtaining the number of equivalent strip line pairs required for discovery, identification and confirmation detection levels is as follows: based on the Johnson criterion, the number of equivalent strip line pairs required for discovery, identification and confirmation detection levels is obtained, and then the above equivalent strip line pairs are corrected to obtain the final number of equivalent strip line pairs required for discovery, identification and confirmation detection levels for SAR remote sensing detection performance evaluation; wherein the correction factor is 2-4.

8. The SAR remote sensing detection performance evaluation method according to claim 1, characterized in that: A resolution-limited probability model is established. The resolution-limited probability model is: Where N is the number of target equivalent strip line pairs; n ε The number of equivalent strip line pairs required at different detection levels is selected for the detection levels of discovery, identification, and confirmation respectively; E ε =2.7+0.7(N / n ε );P J is the resolution-limited probability at the corresponding detection level.

9. The SAR remote sensing detection performance evaluation method according to claim 1, characterized in that: The probability of target discovery, identification and confirmation is calculated respectively. The calculation method is: backscatter limited probability P σ and the resolution-limited probability P at the corresponding detection level J The product obtained by multiplying is the probability of the target at the corresponding detection level.

10. The SAR remote sensing detection performance evaluation method according to claim 1, characterized in that: The probability of the target being discovered, identified, and confirmed is calculated as follows: P=(1-P σ )×(1-P J ) Where P σ is the backscattering limited probability, P J is the resolution-limited probability at the corresponding detection level, and P is the probability of the target at the corresponding detection level.

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