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Man-Made Target Detection from Polarimetric SAR Data via Nonstationarity and Asymmetry Deliang Xiang, Tao Tang, Yifang Ban, Member, IEEE, and Yi Su, Senior Member, IEEE
Abstract—Detection of man-made targets in urban areas using polarimetric synthetic aperture radar (PolSAR) data has become a promising research area since it has a close relationship with urban planning, rescue service, etc. This paper presents an improved man-made target detection method for PolSAR data based on nonstationarity and asymmetry. Nonstationarity in azimuth direction is already utilized to separate man-made and natural targets in urban areas. However, there are still some drawbacks. Some small man-made targets and roads cannot be effectively detected. In addition, nonstationarity can also occur in some other natural surfaces, such as cropland with Bragg resonance. Therefore, to resolve these problems, we incorporate reflection asymmetry into the azimuth nonstationarity extraction method to improve the man-made target detection accuracy, i.e., removing the natural areas and detecting the small targets. Airborne ESAR data and spaceborne PALSAR data are used to validate the performance of the proposed method. The result obtained by our proposed method shows a 20% higher accuracy than the result based on original nonstationarity extraction method. Natural areas with Bragg resonance are removed. Moreover, most of the buildings and some metallic fences along the road can also be accurately detected. Index Terms—Man-made target detection, nonstationarity, polarimetric SAR (PolSAR), reflection asymmetry.
I. I NTRODUCTION
A
LONG with the launch of airborne and spaceborne polarimetric SAR (PolSAR) sensors, man-made target extraction from PolSAR data has been increasingly used in various applications, such as city expansion, earthquake or tsunami damage assessment, military surveillance, etc. [1]–[5]. However, it is still challenging to detect man-made targets using PolSAR data because of the sensitivity to target orientation and the complicated backscatter produced by dense buildings and vegetation. In general, many man-made targets with dominant double-bounce scattering, such as buildings aligned along radar Manuscript received June 25, 2015; revised December 21, 2015; accepted January 07, 2016. Date of publication February 10, 2016; date of current version March 11, 2016. This work was supported in part by the National Natural Science Foundation of China under Grant 61201338, in part by Doctoral Innovation Project of National University of Defense Technology under Grant B130406, and in part by Swedish National Space Board. (Corresponding author: Deliang Xiang.) D. Xiang is with the Division of Geoinformatics, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden, and also with the College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China (e-mail:
[email protected]). T. Tang and Y. Su are with the College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China. Y. Ban is with the Division of Geoinformatics, KTH Royal Institute of Technology, 114 28 Stockholm, Sweden. Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSTARS.2016.2520518
flight direction, can be extracted effectively in the polarimetric dimension. However, when the buildings do not align along radar flight direction, significant cross-polarized component is produced, which can lead to confusion with forests [6]– [8]. Moreover, some small man-made targets such as metallic fences along the road are also very difficult to be extracted. In recent years, many effective algorithms have been proposed for man-made target extraction from PolSAR data. For instance, Lee et al. [9] analyzed phase-difference characteristics of urban areas for various orientation angles and proposed an effective phase-difference parameter to detect buildings. In this method, co-polarized phase-difference is adequate for aligned urban areas, while cross-polarized phase-difference is suitable for 45◦ oriented urban areas. Kajimoto and Susaki [10] proposed a novel building detection method that utilizes polarization orientation angle (POA), volume scattering power, and total power, where the POA randomness parameter between neighboring pixels is used to discriminate urban areas from forest areas. Yang et al. [11] utilized scattering mechanism-based statistical features from adaptive model decomposition [12] to extract urban buildings. Xiao et al. [13] maximized correlation coefficient between two polarimetric channels by rotating a polarimetric coherence matrix in the rotation domain around the radar line of sight. Then the coherence of oriented man-made targets is enhanced while that of forests remains relatively low. It can be seen that these methods all concentrate on distinguishing oriented man-made targets from forest areas since these two land covers are easily misinterpreted by the conventional model-based decomposition techniques. Nonstationarity analysis based on time-frequency decomposition is a useful tool in PolSAR image information extraction [14]. Complex man-made targets with anisotropic geometrical structures are illuminated from different positions and may show a changing electromagnetic characteristic [15]. Therefore, more information can be provided for buildings not facing the radar and man-made targets can effectively discriminated from forest areas. Based on this theory, Ferro-Famil et al. [16], [17] developed an azimuth nonstationarity extraction method over the Wishart distribution and then utilized it to detect man-made targets from PolSAR data. After that, to achieve better detection result, Wu et al. [18], [19] further modified this method by employing Rician distribution and nonzero-mean statistical model instead of Wishart distribution, which are more suitable for high-resolution PolSAR data. Although nonstationarity analysis can describe the anisotropic characteristic of manmade targets, there are some deficiencies, as stated in [18]. One reason is that anisotropy can also occur with other natural surfaces. For instance, cropland with Bragg resonance also
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exhibits the behavior of a nonstationary target [16], resulting in some false alarms of the man-made target detection. Another reason is the number of subaperture images. Small number of subapertures cannot effectively describe the target anisotropy, while large number of subapertures can seriously degrade the image resolution, leading to the detection omissions of small man-made targets and roads. As we know, backscatter from natural areas is often reflection symmetric, i.e., characterized by near zero values for coherency matrix off-diagonal terms and their conjugates due to ∗ ≈ SHV SV∗ V ≈ 0. In contrast, backscatter from SHV SHH complex man-made structures in urban areas often occurs from different geometries; therefore, reflection symmetry is often broken [20], [21]. The coherency matrix mixed polarization terms (e.g., T13 , T23 , and their conjugates) can be quite large for these scattering cases, which was also discussed by Yamaguchi et al. [22] and Ferro-Famil and Lavalle [23]. Migliaccio et al. [24] developed a symmetry-based detector in a processing chain to provide two added-value products: sea oil slick maps and metallic targets at sea maps. Ainsworth et al. [20] normalized the conventional RR-LL correlation coefficient by a circular-pol RR-LL correlation coefficient, which was constructed from the same covariance or coherency matrix terms, ∗ , SHV SV∗ V artificially but with the mixed terms SHV SHH set to zero, i.e., reflection symmetry. This normalized circularpol correlation coefficient can effectively detect scattering from reflection asymmetric structures. Nevertheless, the man-made target details are reduced, which may omit the small targets and roads. To improve the man-made target detection accuracy of original azimuth nonstationarity approach, inspired by Ainsworth’s idea, we incorporate the target reflection asymmetry into azimuth nonstationarity extraction and proposed a new manmade target detection method for PolSAR data. Nonstationary and reflection asymmetric man-made structures are detected based on time-frequency decomposition. In this way, the false alarms and detection omissions are significantly reduced. This paper is organized as follows. The reflection asymmetry of man-made targets is discussed in Section II. The proposed approach is described in Section III. Section IV describes the experimental results and comparisons. Section V reports our conclusion.
II. R EFLECTION A SYMMETRY OF M AN -M ADE TARGETS PolSAR system measures the complex scattering matrix S. This matrix is formed as the sum of the individual back scatterers return in each polarization, which can be written as [25] SHH SHV (1) S= SV H SV V where the subscripts H and V represent horizontal and vertical polarizations, respectively. The first index represents the polarization of received signal and the second denotes that of the transmitted signal in each scattering component. According to the reciprocity theorem, we can get SHV = SV H . The target
vector k can be formed as T √ k = SHH , 2SHV , SV V
(2)
where the superscript “T” denotes the matrix transpose. The corresponding covariance matrix C can be created from k as ⎡ ⎤ C11 C12 C13 † ∗ C22 C23 ⎦ [C] = kk = ⎣C12 ∗ ∗ C13 C23 C33 ⎡ ⎤ (3) T11 T12 T13 ∗ T22 T23 ⎦ then ⇒ [T ] = ⎣T12 ∗ ∗ T13 T23 T33 where † denotes the complex conjugation and transposition, ∗ is the complex conjugation, and · represents the ensemble average. It has been verified that the corresponding sample coherency matrix T obeys a scaled multivariate complex Wishart distribution with density given by [26] nqn |T |n−q exp(−T r(nΣ−1 T )) K(n, q)|Σ|n q q(q−1)/2 K(n, q) = π Γ(n − j + 1)
P (T |Σ) =
(4)
j=1
where n represents the number of looks, and q represents the number of elements of the target vector k. Γ(·) is the gamma function. Σ denotes the averaged sample coherence matrix with Σ = E(kk H ). T r and | · | denote the trace and the determinant, respectively. Ainsworth et al. [20] pointed out the above full coherency matrix determines the conventional RR-LL correlation coefficient |ρ|. Forcing reflection symmetry in (3) by artificially setting matrix elements T13 = T23 = 0 defines the normalized RR-LL correlation coefficient |ρ0 |. Therefore, for reflection symmetric scattering, the ratio |ρ| / |ρ0 | is one since both linear co-pol and cross-pol correlations are already zero. In contrast, the ratio has larger values for scattering from reflection asymmetric structures. Since this normalized circular-pol correlation coefficient reduces the man-made target details and may omit the small targets and roads, we consider the asymmetry test for man-made targets using the coherency matrix directly. The averaged sample coherency matrix for each pixel is obtained using a sliding local window. Under the hypothesis of reflection symmetry, the maximum likelihood estimate of a symmetric coherency matrix is given by ⎤ ⎡ T11 T12 0 ∗ T22 0 ⎦ . (5) ΣSym = ⎣T12 0 0 T33 Then the likelihood ratio is λ=
P (T |Σ) . P (T |ΣSym )
(6)
The denominator of (6) is the probability evaluated under the hypothesis of reflection symmetry, whereas the numerator is the probability evaluated under the hypothesis of reflection asymmetry. Thus, we can conclude that the ratio λ is
XIANG et al.: MAN-MADE TARGET DETECTION FROM POLSAR DATA VIA NONSTATIONARITY AND ASYMMETRY
one or nearly one for reflection symmetric structures and has larger values for reflection asymmetric structures. However, the results are not always satisfactory if we do not consider the time-frequency decomposition. Some complicated man-made targets which mixed with vegetation areas cannot be detected at one azimuth look angle. Since the nonstationarity analysis can provide much more information about the complex man-made targets, based on the time-frequency decomposition, we combine target reflection asymmetry and nonstationarity analysis to detect man-made targets. III. M ETHODOLOGY A. Time-Frequency Decomposition Full-PolSAR images are generally selected for timefrequency analysis. We can use the decomposition technique in both azimuth and range directions. A set of coarser-resolution subaperture images containing different parts of the SAR Doppler spectrum can be obtained in azimuth direction decomposition and another set of subaperture images with different observation frequencies are derived from the range direction. As man-made target scattering is more significantly affected by radar looking directions than frequency effects [27], as well as for the sake of improving the efficiency, time-frequency decomposition in azimuth direction is enough to deal with man-made target extraction problem. The procedure of full PolSAR data time-frequency decomposition is as follows. First, 2-D Fourier transform is utilized to transform a PolSAR image into the spectral domain. Then the total frequency spectrum is divided into regions, called subspectrums, centered around specific spectral locations using window function such as Hamming window. At last, using a 2D inverse Fourier transform, every subspectrum is transformed back into the spatial domain, and thus we can get a subaperture image representing the focused PolSAR response around a specific spectral location. The subaperture images can be used to characterize some properties of the man-made target scatters. B. Likelihood Ratio Test Based on Nonstationarity and Target Reflection Asymmetry The likelihood ratio test based on nonstationarity and reflection asymmetry here is similar to the original azimuth nonstationarity detection method [17], the difference is the null hypothesis. This hypothesis test is employed to extract the manmade target characteristic via testing whether the backscatter is nonstationary and asymmetry or not in different subaperture images. Therefore, we can have null hypothesis as H0 : Σ1 = · · · = ΣR = ΣSym , where Σi is the original sample coherence matrix of ith subaperture image. The likelihood ratio in the ith subaperture image is defined as the ratio of the probability calculated by the ith subimage and the probability calculated by the average of all the subimages, as depicted in (7). Σ0 is the averaged symmetry coherence matrix obtained from all the subaperture images λi =
P (Ti |Σi ) . P (Ti |Σ0 )
(7)
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Similar to (6), the denominator of (7) is also under the hypothesis of reflection symmetry whereas the numerator is under the hypothesis of reflection asymmetry. If the scatter is stationary and reflection symmetric, then this ratio is close to one. If the scatter is nonstationary or reflection asymmetric, this ratio becomes larger. If the scatter is nonstationary and reflection asymmetric, this ratio is even larger. Therefore, the test will select the alternative hypothesis if this test statistic exceeds a discrimination threshold value. In that case, we can remove the natural areas and detect man-made targets. Then, the overall likelihood ratio is calculated as the multiplication of all λi , where R is the total number of subaperture images. Afterward, the examination operator shown in (9) is obtained to simplify the calculation Λ=
R
λi
(8)
i=1
log Λ = Rn log |Σ0 | − n
R
log |Σi |.
(9)
i=1
At last, this operator is asymptotically distributed as a chisquared distribution [28], and the discrimination threshold can be obtained from the quantile (1 − α) for an arbitrary false alarm rate α similar to [16]. That means the hypothesis is accepted and the target is considered to be isotropic and refection symmetric with an arbitrarily chosen probability of false alarm α if Λ < T hreshold
with P (Λ > T hreshold) = α
(10)
then we have P (Λ ≤ T hreshold) = 1 − α.
(11)
Then the threshold can be determined from the chi-squared distribution. The main difference between our proposed approach and the original nonstationarity detection method [16] is the incorporation of asymmetry. There are two advantages to incorporate asymmetry into nonstationarity detection method. One is the reduction of false alarms. Some natural surfaces that show anisotropy are regarded as man-made targets in original nonstationarity detection method. For instance, some sand dunes with repetitive patterns in the desert and croplands with Bragg resonance may also appear to be nonstationary [16]–[18]. However, this false alarm can be diminished using asymmetry since the likelihood ratio of man-made targets becomes larger whereas that of natural surfaces with Bragg resonance remains unchanged. Another advantage is the reduction of omissions. Since having too few subapertures may not be effective to reflect the anisotropic properties; however, too many will lead to a serious reduction of the spatial resolution, which can decrease the detection accuracy of small man-made targets. By incorporating the reflection asymmetry, the likelihood ratio of small man-made targets will significantly increase even though they may show stationarity. Therefore, a better man-made target detection result can be achieved with few subapertures, which will be discussed in Section III.
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Fig. 1. Framework of the proposed man-made target detection procedure. (a) Likelihood ratio test for subaperture image. (b) Overall flowchart.
Fig. 2. Study area and ESAR data. (a) Optical image from Google Earth. (b) Pauli coded ESAR image with L-band (red: HH–VV, green: HV, blue: HH+VV). (c) Yamaguchi four-component decomposition with rotation of coherency matrix (red: double-bounce and helix scattering, green: volume scattering, blue: surface scattering).
The flowchart for the proposed man-made target detection approach is shown in Fig. 1. After time-frequency decomposition, R subaperture images are obtained. In this paper, the influence of R on detection performance will also be discussed. Then we can get the ratio image via likelihood ratio test for each pixel. From the ratio image, the detection results are analyzed, especially for natural areas and small man-made targets. Through threshold determination and postprocessing, the target detection map is generated and registered with corresponding SAR image for comparison. IV. E XPERIMENTAL R ESULTS A ND A NALYSIS A. Experimental Results With ESAR Data The first experimental dataset is full-polarization L-band SAR data acquired by the German Aerospace Center (DLR) airborne E-SAR system on July 22, 1999. The SAR data have a spatial resolution of 3 m in both azimuth and range direction and the azimuth look angle is about 7.5◦ . The test area is
close to the Oberpfaffenhofen Airport, Germany, as shown in Fig. 2. Fig. 2(a) shows an optical image obtained from Google Earth, and Fig. 2(b) shows the Pauli coded PolSAR image with size 1104 × 724. The red channel describes double-bounce scattering, the green channel describes volume scattering, and the blue channel describes single-bounce scattering. These two images are coregistered for the convenience of display and comparison. The image rows correspond to azimuth direction, and the columns correspond to range direction. Fig. 2(c) gives the Yamaguchi four-component decomposition with rotation of coherency matrix. From the three images, we can see that buildings not aligned along the azimuth direction (e.g., buildings in the middle and lower part of the image, called ortho buildings) show volume scattering, which is the same as forests. This is because these oriented buildings have strong cross-polarized scattering powers [29]. Moreover, the roads also show volume scattering, the man-made grounds and some small targets show surface scattering, indicating that these kinds of man-made targets are difficult to be discriminated from natural areas.
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Fig. 3. Man-made target reflection asymmetry illustration. (a) Log modulus of the normalized circular-pol correlation coefficient. (b) Log-likelihood ratio of reflection asymmetry.
To discuss the reflection asymmetry of man-made targets, the normalized circular-pol correlation coefficient and reflection asymmetry likelihood ratio using (6) are displayed in Fig. 3. The window size for estimating the sample coherency matrix is 3 × 3. It can be seen from Fig. 3(a) that some forests are incorrectly detected and become the false alarms. This is because the normalized circular-pol correlation coefficient can be rewritten as a product of separable helicity and orientation angle dependencies. Some forests may have strong helicity due to the penetration ability of L-band. In contrast, reflection asymmetry likelihood ratio of the forests in Fig. 3(b) is quite low. In addition, the roads in Fig. 3(b) are clearer than those in Fig. 3(a). Similar problem in these two images is that reflection asymmetry tests of ortho buildings (marked with ellipse box A) are both unobvious. This is because buildings facing the radar show double-bounce scattering and have approximately symmetry reflection [20]. Further comparisons of the buildings and forests selected from Fig. 3 are shown in Fig. 4, which depicts the local histograms of Fig. 3 in three terrains. We can find that forests are easily mixed with oriented buildings using the normalized circular-pol correlation coefficient and mixed with ortho buildings using reflection asymmetry test. After that, we discuss the performance of azimuth nonstationarity detection with and without target reflection asymmetry. The improved azimuth nonstationarity detection method with nonzero-mean statistical model [19] is also used for comparison. The log-likelihood ratio images of three methods are shown in Fig. 5, where the light areas in Fig. 5(a) and (b) corresponds to nonstationary targets whereas light areas in Fig. 5(c) are targets with nonstationarity and reflection asymmetry. In this experiment, we set the number of subapertures to four for these three methods. From Fig. 5, we can clearly see that most of the buildings can be detected using three methods. However, in Fig. 5(a) and (b), some vegetation areas also show strong anisotropy, resulting in confusion for the man-made target detection. Since the nonzero-mean models are considered promising in high-resolution urban area SAR image processing [19], the nonstationarity of man-made targets in Fig. 5(b)
Fig. 4. Histograms of (a) log modulus of the normalized circular-pol correlation coefficient and (b) log-likelihood ratio of reflection asymmetry in three terrains.
is enhanced and the contrast between targets and backgrounds becomes more clear than Fig. 5(a). However, like the traditional azimuth nonstationarity extraction method, some natural areas are still incorrectly detected. In contrast, the result in Fig. 5(c) generated by the proposed method is much better, where most of natural targets are removed due to the reflection asymmetry. In addition, similar to Fig. 5(b), the log-likelihood ratios of roads and some small man-made targets (red ellipse boxes areas) are also much higher than the natural areas, which is not obvious in Fig. 5(a).
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Fig. 5. Log-likelihood ratio images of three different methods. (a) Original nonstationarity detection method. (b) Nonstationarity detection method with nonzeromean statistical model. (c) Proposed method.
Fig. 6. Histograms of the log likelihood ratio in three selected areas by (a) original nonstationarity detection method; (b) nonstationarity detection method with nonzero-mean statistical model; and (c) the proposed method.
To quantitatively evaluate the log-likelihood ratio values, in Fig. 6, we list the local histograms of three previous selected areas in Fig. 3, i.e., forests, ortho buildings, and oriented buildings. It is easy to see that unlike Fig. 6(a) and (b), the proposed method in Fig. 6(c) can clearly separate the buildings from natural areas, where the log-likelihood ratio values of buildings are much higher than natural areas due to incorporation of reflection asymmetry. Fig. 7 gives the detection results of these three methods. Some targets are selected in Fig. 7(c) for further comparison and the corresponding optical images of the targets are shown in Fig. 8. It is interesting to see that even though the area with label 1 shows surface scattering in Pauli image, it can be extracted by these three methods. The main reason is that this area is not flat, as shown in Fig. 8(a), subaperture images can describe the anisotropy at different observation angles. Nevertheless, compared with Fig. 7(a), the results in Fig. 7(b) and (c) are better due to the nonzeromean statistical model and reflectance asymmetry, respectively. The roads in Fig. 7(c) are clearly detected but are missing in Fig. 7(a). It is because long metallic fences have low anisotropy at limited observation angles. However, after introducing the asymmetry, the detection results are significantly improved. Even though nonzero-mean statistical model can improve the
detection accuracy, the detected roads are still not obvious, which will be discussed in the next section. The area with label 3 contains some small parking aprons. They are covered by trees and have approximate round shapes. Although they have isotropic property, as shown in Fig. 7(a), they can be extracted using the proposed approach. Actually there are still some false alarms of the proposed method, e.g., the rectangle areas in Fig. 7(c). In addition, some omissions exist. For instance, the man-made runways cannot be detected due to their very low backscatters. However, compared with Fig. 7(a) and (b), the detection results of our proposed method are satisfactory. In order to quantitatively compare the detection accuracies of two methods, the confusion matrices are listed respectively in Tables I–III. We choose 119 894 pixels from the registered optical image, of which 62 344 pixels are man-made targets and 57 550 pixels are natural areas. The rows of matrices represent the true classes and the columns represent the detection results. From Table III, we can see that the overall accuracy is about 84%, which is fairly good, and the kappa coefficient is 0.6892, indicating substantial agreement with the visual evaluation of the detection result. Although the original nonstationarity detection method and the method in [19] can extract most of the buildings, the confusion with vegetation is quite large, and
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Fig. 7. Man-made target detection results of (a) original nonstationarity detection method; (b) nonstationarity detection method with nonzero-mean statistical model; and (c) the proposed method. (d) Detection results (red) of the proposed method overlaid Pauli image. TABLE II ACCURACY A SSESSMENT OF THE R ESULT BY N ONSTATIONARITY D ETECTION M ETHOD WITH N ONZERO -M EAN
TABLE III ACCURACY A SSESSMENT OF THE R ESULT BY P ROPOSED M ETHOD
Fig. 8. Optical images (a)–(d) correspond to the selected patches 1–4 in Fig. 7(c). TABLE I ACCURACY A SSESSMENT OF THE R ESULT BY O RIGINAL N ONSTATIONARITY D ETECTION M ETHOD
the overall accuracy is 20% lower than the proposed approach, as shown in Tables I and II. These methods cannot effectively identify the forest vegetation with Bragg resonance, which is regarded as man-made targets. Moreover, some of the roads and small man-made targets are also not clearly detected. B. In-Depth Analysis of the Detection Results To further evaluate the performance of asymmetry on manmade target detection, the PolSAR image is decomposed into 2,
3, 4, 5, 6, 7, and 8 subaperture images. We select six test areas, i.e., ortho buildings, oriented buildings, forest, roads, bare soil, and small man-made targets, to compare their average log ratio values with different subapertures. Fig. 9 depicts log ratio values of three methods with different subapertures, respectively. From Fig. 9(a), it can be seen that with original nonstationarity detection method, the forest areas are mixed with two types of buildings all the time; in addition, roads, small man-made targets, and bare soils are also difficult to be distinguished. This indicates that some natural areas with Bragg resonance also have anisotropy and can easily be detected as man-made targets. Roads and small man-made targets are usually omitted because they have low anisotropy with limited subapertures; however, if we increase the number of subapertures, the resolution of subimages gets coarser, making it even more difficult to extract small targets. For example, in Fig. 9(a), when the number of subapertures is under four, roads and small man-made targets have slightly stronger anisotropy than bare soil, nevertheless, when the number exceeds four, they are stationary
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Fig. 10. Study area and PALSAR data. (a) Optical image from Google Earth. (b) Pauli coded PALSAR image (red: HH–VV, green: HV, blue: HH+VV).
Fig. 9. Log ratio values of three methods with different subapertures. (a) Original nonstationarity detection method. (b) Nonstationarity detection method with nonzero-mean statistical model. (c) Proposed method.
almost the same as bare soil. Therefore, original nonstationarity detection method is not effective to remove natural areas with Bragg resonance; furthermore, there are a lot of omissions for roads and small man-made targets. Fig. 9(b) has a similar result with Fig. 9(a) except for the roads and small man-made targets. Even though the nonzero-mean statistical model can enhance the anisotropy of man-made targets in high-resolution SAR data, the roads and small targets are still not as obvious as the proposed method. This is because their log ratio values are closer to bare soil in Fig. 9(b) than in Fig. 9(c), which is marked with red ellipse box. Fig. 9(c) gives log ratio values of the proposed approach, where the curve gap between man-made targets and natural areas is bigger, indicating that the result is much better than the other two methods. We can see that natural areas are clearly separated from man-made targets even though some of them are also nonstationary. The reason is that man-made targets have strong reflectance asymmetry whereas natural areas do not, resulting in lower log ratio values for man-made targets.
Small man-made targets and roads are also discriminated from bare soils. It is also interesting to see that their log ratio values are always lower than those of buildings. This is because large buildings usually have strong anisotropy. Another issue to be discussed is the number of subapertures. It can be seen from Fig. 9(c) that when the number gets larger, the difference between man-made targets and natural areas becomes bigger. This is because some man-made targets may have symmetry reflectance at one azimuthal look angle but have strong asymmetry reflectance at another look angle. Therefore, subaperture decomposition can help improve the man-made target detection performance of asymmetry. In [18] and [19], the subaperture size was set to four. The reason is that too many subapertures will lead to a serious reduction of the spatial resolution, which is not good for target detection. In addition, the computation load is also apparently higher with too many subapertures. However, this conclusion was not demonstrated with quantitatively analysis. From Fig. 9, it can be seen that the difference of log ratio values between man-made targets and natural areas does not change dramatically when the number of subapertures exceeds four. Considering time consuming and to compare the detection result with [18], four subapertures are suitable for man-made target detection in our experiment.
C. Demonstration With PALSAR Data The second study area is located in San Francisco Bay, USA. This spaceborne ALOS PALSAR PolSAR data, with center frequency 1270 MHz, i.e., L-band., was acquired over the study area on November 11, 2009. The SAR data have the nominal pixel spacing of 9.36 and 3.54 m in the range and azimuth directions, respectively, where the image rows correspond to azimuth direction and the columns correspond to range direction. ALOS PALSAR polarimetric mode provides the full quad-pol scattering matrix with 21.5◦ off-nadir angle, which results in 30 km swath width and 22.8◦ –25.2◦ incidence angle range. Fig. 10(a) gives the optical image of the study area and Fig. 10(b) is the Pauli coded PolSAR image.
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Fig. 11. (a) First subaperture image. (b) and (c) Log-likelihood ratio images of the original nonstationarity detection method, nonstationarity detection method with nonzero-mean statistical model, and the proposed method, respectively.
Fig. 12. Man-made target detection results of (a) original nonstationarity detection method; (b) nonstationarity detection method with nonzero-mean statistical model; and (c) the proposed method. (d) Detection results (red) of the proposed method overlaid Pauli image.
Fig. 13. Detailed man-made target detection results in subarea 1. (a) Optical image. (b) Pauli image. (c)–(e) Detection results of original nonstationarity detection method, nonstationarity detection method with nonzero-mean statistical model, and the proposed method, respectively.
Fig. 14. Detailed man-made target detection results in subarea 2. (a) Optical image. (b) Pauli image. (c)–(e) Detection results of original nonstationarity detection method, nonstationarity detection method with nonzero-mean statistical model, and the proposed method, respectively.
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Fig. 11(a) shows the first subaperture image and Fig. 11(b)– (d) depicts the log-likelihood ratio images of three methods, respectively. In this experiment, the number of supapertures is set to four. It can be seen that the ortho and oriented buildings, as well as bridges and small targets, show strong anisotropy with original nonstationarity detection method. Nevertheless, similar to man-made targets, the natural forest areas also have strong anisotropy, which decreases the man-made target detection accuracy. Unlike the experimental analysis for ESAR data, the result in Fig. 11(c) is worse than Fig. 11(b). This is because the nonzero-mean statistical model is more suitable for high-resolution PolSAR data. The water areas may have strong waves and subaperture decomposition can describe the anisotropy property clearly, as shown in Fig. 11(a). The areas marked with red rectangle mainly show surface scattering in original PolSAR data whereas show slight double-bounce scattering in subaperture images. Therefore, the water areas in Fig. 11(c) are also nonstationary, making the small targets and bridges not obvious for detection. The result of our proposed method in Fig. 11(d) is much better than the other two methods, where the log-likelihood ratio values of man-made targets are large and those of natural areas are quite small, leading to a high ability of distinguishing man-made targets from natural backgrounds. Fig. 12(a)–(c) shows the man-made target detection results of three methods, and Fig. 12(d) overlays the proposed detection result on Pauli image. We can see that even though the original nonstationarity detection method can extract man-made targets effectively, the forests (marked with red circle) are also incorrectly detected. In contrast, the proposed method can remove most of the natural areas and also can detect the man-made targets. To compare the detection results in detail, two urban areas are selected and the enlarged results are shown in Figs. 13 and 14, respectively. What we can see from these two figures is that the proposed method performs the same as or even better than the original nonstationarity detection method in manmade target detection. Furthermore, the natural areas are almost removed with the proposed method, which can greatly increase the overall detection accuracy. In addition, we can also conclude that nonstationarity detection method with nonzero-mean statistical model is more suitable for high-resolution PolSAR data. The confusion matrices of above three detection results are listed in Tables IV–VI, respectively. We can see that the man-made target detection accuracy of original nonstationarity detection method is 88.47%, which is pretty good. However, about 30% natural areas are incorrectly detected as man-made targets, leading to a decrease in overall detection accuracy. The man-made target detection accuracy of our proposed method is 87.34%, which is slightly lower than that of original nonstationarity detection method; however, the false alarms of natural areas are dramatically diminished. The overall accuracy is 87.89%, which is about 10% and 15% higher than that of original nonstationarity detection method and nonstationarity detection method with nonzero-mean statistical model, respectively. This experiment validates the effectiveness of our proposed method on low-resolution spaceborne PolSAR data.
TABLE IV ACCURACY A SSESSMENT OF THE R ESULT BY O RIGINAL N ONSTATIONARITY D ETECTION M ETHOD
TABLE V ACCURACY A SSESSMENT OF THE R ESULT BY N ONSTATIONARITY D ETECTION M ETHOD WITH N ONZERO -M EAN S TATISTICAL M ODEL
TABLE VI ACCURACY A SSESSMENT OF THE R ESULT BY P ROPOSED M ETHOD
V. C ONCLUSION To solve the drawbacks of conventional nonstationarity detection method, in this paper, we considered the reflectance asymmetry of man-made targets and proposed an improved man-made target extraction method based on nonstationarity and asymmetry for PolSAR data. By incorporating the asymmetry, natural areas with Bragg resonance are removed from the detection results, even though they also have nonstationarity like man-made targets. Furthermore, small man-made targets and metallic fences along the road are also clearly extracted, which demonstrates that the proposed approach performs better than the original nonstationarity detection algorithm. The performance of asymmetry on original nonstationarity detection method, as well as the influence of subaperture decomposition on asymmetry, is also evaluated in this paper. Airborne ESAR data and spaceborne PALSAR data are utilized to demonstrate the performance of our proposed method. It can be concluded that subaperture decomposition is beneficial for man-made target detection, especially for oriented buildings. The nonstationarity detection method with nonzero-mean statistical model performs better than the original nonstationarity detection method with Wishart distribution for highresolution PolSAR data; however, these two methods cannot remove natural areas with Bragg resonance. In contrast, our proposed method has a better discriminative ability than the other two methods for airborne and spaceborne PolSAR data.
XIANG et al.: MAN-MADE TARGET DETECTION FROM POLSAR DATA VIA NONSTATIONARITY AND ASYMMETRY
ACKNOWLEDGMENT The authors would like to thank the three anonymous reviewers and the editor for their invaluable comments that greatly improved this paper and also thank JAXA and DLR for the relevant PolSAR data set and ESA for the Third Radar Polarimetry Training Course.
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Deliang Xiang received the B.S. degree in remote sensing science and technology from Wuhan University, Wuhan, China, in 2010, and the M.S. degree in electronic science and engineering from National University of Defense Technology, Changsha, China, in 2012. Currently, he is pursuing the Ph.D. degree in microwave remote sensing at the KTH Royal Institute of Technology, Stockholm, Sweden. His research interests include urban area remote sensing, PolSAR image processing, and pattern recognition.
Tao Tang received the B.S. degree in communication and M.S. degree in electronic science and engineering from National University of Defense Technology, Changsha, China, in 2002 and 2006, respectively. Currently, he is pursuing the Ph.D. degree at National University of Defense Technology. His research interests include SAR image processing, feature analysis, and pattern recognition.
Yifang Ban (M’02) received the B.Sc. degree in computer cartography, M.Sc. and Ph.D. degrees in remote sensing and GIS from Nanjing University, Nanjing, China, in 1984 and 1987, respectively, and the Ph.D. degree from the University of Waterloo, Waterloo, ON, Canada, in 1996. She has been the Chair Professor and Director with the Geoinformatics Division, KTH Royal Institute of Technology in Stockholm, Sweden, since 2004. Before joining KTH, she was a tenured Associate Professor with York University, Toronto, ON, Canada. She has authored over 100 publications with more than 50 papers in international peer-refereed journals and books. Her research interests include multitemporal remote sensing, SAR and optical image analysis, multisensor data fusion, image segmentation and classification, change detection, urban analysis, land cover mapping, and environmental impact assessment.
Yi Su (M’04–SM’13) was born in Shandong, China, in 1961. He received the B.S., M.S., and Ph.D. degrees in electronic engineering from the School of Electrical Engineering, National University of Defense Technology, Changsha, China, in 1982, 1988, and 2001, respectively. He has been a Professor with the School of Electronic Science Engineering, National University of Defense Technology. His research interests include signal processing, radar target characteristics, UWB radar, and remote sensing.
