Change Detection in Multilook Polarimetric SAR Imagery with Determinant Ratio Test Statistic N

Change Detection in Multilook Polarimetric SAR Imagery With Determinant Ratio Test Statistic N. Bouhlel, V. Akbari, S. Meric

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N. Bouhlel, V. Akbari, S. Meric. Change Detection in Multilook Polarimetric SAR Imagery With Determinant Ratio Test Statistic. IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2020, pp.1-15. 10.1109/TGRS.2020.3043517. hal-03164088

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 1 Change Detection in Multilook Polarimetric SAR Imagery With Determinant Ratio Test Statistic Nizar Bouhlel , Member, IEEE, Vahid Akbari , Member, IEEE, and Stéphane Méric , Member, IEEE

Abstract— In this article, we propose a determinant ratio information-theoretic measures in order to design a distance test (DRT) statistic to measure the similarity of two covariance between images. In doing so, surrounding pixels are modeled matrices for unsupervised change detection in polarimetric radar by a given distribution and distance measures are applied to images. The multilook complex covariance matrix is assumed to follow a scaled complex Wishart distribution. In doing so, obtain a comparative statistic. With this family, the change we provide the distribution of the DRT statistic that is exactly detection is less sensitive to acquisition conditions since the Wilks’s lambda of the second kind distribution, with density used methodology considers information from the neighbor- expressed in terms of Meijer G-functions. Due to this distribution, hood. This information is relevant when dealing with data the constant false alarm rate (CFAR) algorithm is derived in corrupted by speckle or/and the number of looks is low: order to achieve the required performance. More specifically, a threshold is provided by the CFAR to apply to the DRT to name a few of statistical information distances: mutual statistic producing a binary change map. Finally, simulated and information [1], [2], variational and mixed information [1], [3], real multilook polarimetric SAR (PolSAR) data are employed to and stochastic distances such as the Kullback–Leibler, Rényi, assess the performance of the method and is compared with the Bhattacharyya, and Hellinger distances [4]. Hotelling–Lawley trace (HLT) statistic and the likelihood ratio The second family aims to compute a covariance equality test (LRT) statistic. test and to test the hypotheses of change versus no-change Index Terms— Change detection, complex Wishart distrib- where an asymptotic distribution is usually employed by the ution, determinant ratio test (DRT), Hotelling–Lawley trace test statistic. The simplest one is to compute the ratio of (HLT), likelihood ratio test (LRT), multilook polarimetric synthetic aperture radar (SAR) data, Wilks’s lambda of the second SAR amplitudes or intensities observed at different times. kind distribution. This kind of ratios is a well-known test statistic in single- channel SAR-based change detection [5]. A large number of test statistics have been developed and described in the I. INTRODUCTION literature for automatic and unsupervised change detection, HE synthetic aperture radar (SAR) image change detec- such as mean ratio/log-ratio measures [6], [7], Gauss log ratios Ttion has become very important in remote sensing for [8], multitemporal coherence analysis [6], and maximum- monitoring dynamic processes on the Earth, for instance, likelihood ratios [9]. damage assessment in urban areas, deforestation and clear- Polarimetric SAR (PolSAR) gives more scattering informa- cut detection, flooding, and monitoring of glaciers. Change tion than single-polarization channel SAR data, which can be detection is a process that analyzes multitemporal remote used to detect the change and increase the quality of the change sensing images acquired on the same geographical area for detection map. The first work on test statistics for change identifying changes that occurred at distinct observation dates. detection in multilook PolSAR data was proposed by Conrad- The result is a generation of a change detection map in which sen et al. [10]. They proposed a likelihood ratio test (LRT) for changed areas are explicitly identified. the equality of two complex covariance matrices 1 and 2 Several unsupervised change detection methods have been and gave the approximated distribution of the LRT statistic. proposed in the literature. Two families of methods handle The LRT algorithm worked by comparing two hypotheses: the change detection process: the statistical information theory the null hypothesis (H0) corresponding to ”no-change” and and the hypothesis test theory. The first family is based on an alternative hypothesis (H1) corresponding to ”change.” Manuscript received June 1, 2020; revised October 19, 2020; accepted The LRT approach was extended to the multitemporal case November 29, 2020. (Corresponding author: Nizar Bouhlel.) [11], [12] and multifrequency data [13]. Kersten et al. [14] Nizar Bouhlel was with the Team Signal Communication and Embedded compared three test statistics: the LRT statistic also called Electronics (SCEE), Institut d’Electronique et de Telecommunications de Rennes (IETR), CentraleSupélec, 35042 Rennes, France. He is now with the the Bartlett test, the contrast ratio test, and the ellipticity test. Institut Agro, Statistics and Computer Science Department, 49045 Angers, The second one was based on the largest and the smallest France (e-mail: [email protected]; [email protected]). −1 eigenvalues of 1 , and then, the change was represented Vahid Akbari is with the Department of Computing Science and Mathemat- 2 ics, University of Stirling, Stirling 9037, U.K. by these eigenvalues. The third test statistic was based on the −1 Stéphane Méric is with the Department of Image and Remote Sensing, IETR combination of the determinant and the trace of 1 2 by (UMR CNRS 6164), 35708 Rennes, France. using the eigenvalues. Color versions of one or more figures in this article are available at https://doi.org/10.1109/TGRS.2020.3043517. Later, Akbari et al. [5] proposed a simpler test statistic Digital Object Identifier 10.1109/TGRS.2020.3043517 to detect changes in many scenarios. The test assumed the

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scaled complex Wishart distribution for the covariance matrix with sxy representing the complex scattering coefficients, data. It was based on the complex-kind Hotelling–Lawley where x is the transmit and y is the receive polarization. More- trace (HLT) that was used to measure the similarity of two over, h denotes horizontal, v denotes vertical [21], [.]T means covariance matrices. They applied a decision threshold to the transposition, and d = dim(s) is the vector dimension. The test statistic to detect changes. The threshold was determined vector s is a single-look polarimetric complex format represen- using the constant false alarm rate (CFAR) algorithm [15]. tation of PolSAR data. It is assumed that s is a d-dimensional Akbari et al. proposed the Fisher–Snedecor (FS) distribution as speckle vector, which follows a circular complex Gaussian ∼ N C( , ) the approximation of the sampling distribution of the HLT test distribution (s d 0 ), with a zero-mean vector and statistic. The parameters of the FS distribution depended only a covariance matrix . The multilooking of PolSAR data on the dimension of the polarimetric data and the equivalent reduces the speckle effect characteristic of coherent imaging number of looks (ENL) estimated for images. They used the systems. The polarimetric multilooking operation is given by method of the matrix log cumulants [16] to estimate the ENL. L In this article, the ratio of the determinant of two matrices is 1 H X = ss , L ≥ d (2) proposed as a new test statistic to measure the similarity of two L =1 covariance matrices that are assumed to follow scaled complex Wishart distributions. The new test is called the determinant where L is the number of looks, (.)H denotes the Hermitian d×d ratio test (DRT) statistic and it is able to produce a scalar value, operator, and X ∈ + ⊂ C is the multilook polarimetric to which a threshold is applied. The distribution of the test covariance matrix considered as a random matrix defined statistic under the null hypothesis is exactly Wilks’s lambda on the cone, denoted +, of the positive definite complex of the second kind distribution with density expressed in Hermitian matrices. When L ≥ d, the unnormalized sample terms of Meijer G-functions [17]. Wilks’s lambda distribution covariance matrix defined as Z = LX follows the nonsingular ∼ WC( , ) corresponds to the product of d independent Beta distributed complex Wishart distribution [22] denoted as Z d L of the second kind random variables, where d is the number of and X follows a scaled complex Wishart distribution, denoted ∼ WC( , ) polarimetric channels. The latter distribution depends on the X s d L , with a probability density function (pdf) d2 dimension of the polarimetric data and the ENL. given by fX(X) = fZ(LX)|JZ→X|,where|JZ→X|=L is the For the production of a binary change detection map, Jacobian determinant of the transformation Z = LX [23]. The a threshold is applied to the test statistic. Several thresholding pdf of X is methods have been suggested in the literature to determine L Ld|X|L−d the threshold in a completely unsupervised manner: to name f (X) = (−L−1X) X L etr (3) a few of them, CFAR algorithm [15], Otsu’s method [18], d (L)|| and Kittler and Illingworth (K&I) algorithm [19], [20]. In this where etr(.) = exp(tr(.)) is the exponential trace operator, article, we limit ourselves to the CFAR algorithm since we are |.| is the determinant operator, and d (L) is the multivariate more interested in the test statistic rather than the thresholding gamma function of the complex kind defined as method. Consequently, the final binary change map is made at a predefined false alarm rate (FAR). To further illustrate d−1 d(d−1)/2 the potential of our method in change detection for multilook d (L) = π (L − i) (4) PolSAR data, the well-known HLT and LRT test statistics for i=0 measuring the equality of two multilook covariance matrices where (L) is the standard Euler gamma function. are compared with our approach. Simulated and real multilook PolSAR data are used for this comparison. This article is organized as follows. Section II introduces the III. THEORY statistical model for the covariance matrix data. Section III This section describes some properties related to the scaled describes some properties related to the scaled complex complex Wishart distribution and the DRT for equality of two Wishart distribution, the DRT for equality of two complex complex scaled Wishart distributions. scaled Wishart distributions, and its sampling distribution. Theorem 1 (Goodman [24]): Let X be a complex Her- In Section IV, the proposed polarimetric change detection mitian positive definite random d × d matrix that follows a algorithm based on the DRT is presented, followed by the ∼ WC( , ) scaled complex Wishart distribution, X s d L .The CFAR principle to determine the threshold. The HLT and random variable (2L)d |X|/|| is distributed as the product LRT methods are briefly shown for a later comparison with of d independent chi-square (χ 2) distributed random variables our method. Section V demonstrates the performance of the with 2L,2(L − 1),...,2(L − d + 1) degrees of freedom, method with a simulated and real PolSAR data set and respectively, i.e., discusses the results. Section VI is dedicated to a summary − and a conclusion. |X| d1 ( L)d ∼ χ 2( (L − i)). 2 || 2 (5) i=0 II. MULTILOOK POLSAR IMAGE MODEL Proof: The characteristic functions of the random variable The polarimetric scattering vector is defined as − (( )d | |/||) ( d 1 χ 2( ( − ))) ln 2L X and ln i=0 2 L i are computed and T d s =[shh, shv, svh, svv] ∈ C (1) seen to be equal (for more details, see Appendix A).

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BOUHLEL et al.: CHANGE DETECTION IN MULTILOOK POLSAR IMAGERY WITH DRT STATISTIC 3

Corollary 2: Let X and Y be two complex Hermitian positive deﬁnite random d × d matrices that follow scaled complex Wishart distributions deﬁned as: ∼ WC( , ) ∼ WC( , ). X s d L x and Y s d L y (6)

The random variable defined by the determinant ratio of L x X and L yY is distributed as the product of d independent Beta distributed of the second kind (also called Beta prime) random variables with parameters (L x − i) and (L y − i) − |L X| d 1 x ∼ BetaII(L − i, L − i) (7) |L Y| x y y i=0 II where the pdf for Beta (L x − i, L y − i) is given by − − ( + − ) L x i 1 ( ) = L x L y 2i x fi x + − (8) ( − )( − ) ( + )L x L y 2i L x i L y i 1 x − + = 1 1,1 L y i G1,1 − − x (9) (L x − i)(L y − i) L x i 1 · 1,1 where G1,1 · · is the Meijer G-function defined in Section II-A. Proof: Corollary 2 is the consequence of (5) and the relationship between the ratio of two independent χ 2 random Fig. 1. PDF of Wilks’s lambda distribution of second kind, (2L, d, L) = = variables and the Fisher (F) distribution with two parameters with several values of L and different cases. (a) d 2. (b) d 4. given as follows. ∼ χ 2( ) ∼ χ 2( ) − 1) If X d1 and Y d2 are two independent Theorem 4: The pdf g(z) of the product Z = d 1 X of d (( / )/( / )) ∼ ( , ) i=0 i random variables, then X d1 Y d2 F d1 d2 . independent X variables Beta distributed of the second kind, ∼ ( α, β) (α/β) ∼ i 2) If X F 2 2 has an F-distribution, then X X ∼ BetaII(L −i, L −i), is a Meijer G-function multiplied II(α, β) i x y Beta . by a normalizing constant A,i.e., − , −( − ),...,−( −( − )) ( ) = × d,d L y L y 1 L y d 1 g z A Gd,d z A. Meijer G-Function L x −1, L x −2,...,L x −d The Meijer G-function is a generalization of the generalized (12) hypergeometric function that is defined using the contour − where A = d 1(1/((L − i)(L − i))). integral representation [25] i=0 x y The cumulative distribution function is given by m,n a1,...,ap G , x ( ) = × p q b ,...,b G z Az 1 q m n d,d+1 −L y, −(L y − 1),...,−(L y − (d − 1)), 0 1 = (b j − s) = (1 − a j + s) . = × j 1 j 1 Gd+1,d+1 − , − ,..., − , − z q p L x 1 L x 2 L x d 1 2πi L (1 − b + s) (a − s) j=1+m j j=1+n j (13) × x sds (10) The kth moment E{Z k} is given by { }p { }q where a j j=1 and b j j=1 are, in general, complex-valued. − Contour L is a suitable integration contour that separates the d1 (L − i − k) (L − i + k) E{Z k}= y x . (14) poles of function (b j −s) from the poles of (1−a j +s).The (L y − i) (L x − i) Meijer G-function has been implemented in some commercial i=0 software packages, such as MATLAB and MAPLE. Proof: See Appendix B. With reference to Definition 3, the distribution of B. Wilks’ Lambda Distribution of Determinant Ratio Statistic ((|L x X|)/(|L yY|)) is given as follows: Definition 3: For noninteger values of n and q and integer |L X| values of p with n ≥ q ≥ p, the pdf of Wilks’s lambda x ∼ (L + L , d, L ). | | x y y (15) distribution of the second kind, denoted as (n, p, q),isgiven L yY as [17] It is worthy to note that the determinant ratio depends on = = , −q, −(q − 1),...,−(q − (p − 1)) the parameters L x and L y. In the case L x L y L, f (x) = A × G p p x p,p n − q − 1, n − q − 2,...,n−q− p Wilks’s lambda distribution of the second kind is given by ( , , ) ( , , ) (11) 2L d L . Fig. 1(a) and (b) shows the pdf of 2L d L , with multiple values of L, and different cases: d = 2and = p−1( /(( − − )( − ))) where A j=0 1 n q j q j . d = 4, respectively.

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IV. POLARIMETRIC CHANGE DETECTOR We adopt the same approach presented in the paper of We consider X and Y two statistically independent Her- Akbari et al. [5] where changes from X to Y and reversely mitian positive definite random d × d matrices that follow from Y to X were considered in the CFAR change detector. : scaled complex Wishart distributions with different distribu- This results in using the following two ratios : |L X| |L Y| tion parameters defined as τ = x , τ = y . DRT | | and DRT | | (20) ∼ WC( , ) ∼ WC( , ). L yY L x X X s d L x x and Y s d L y y (16) The combined test is given by Multilook PolSAR images acquired over the same geo- H τ ,τ ≷1 . graphical area before event (at time tx ) and after event (at max DRT DRT T (21) H time ty) are used to detect any change by comparing at 0 each position (i, j) the full polarimetric information before The combined threshold T is derived from change and after change given, respectively, by X(i, j) and +∞ = τ (τ| ) τ. Y(i, j). Here, we suppose that at each (i, j),wehavetwo Pfa 2 f DRT H0 d (22) T matrices X(i, j) and Y(i, j). As a consequence, we resort to When P is specified, the threshold is obtained by compute the determinant ratio of L X and L Y described in fa x y solving (22), and then, the CFAR change detector is obtained. Section IV-A. The proposed unsupervised change detection based on DRT between two multilook PolSAR data acquired before and after A. Determinant Ratio Statistic change is summarized in the following steps.

The determinant ratio statistic is defined by 1) Find a global estimation of Lˆ x and Lˆ y. 2) Generate the DRT statistics image using |L X| τ = x . max{τ ,τ } computed from two multilook DRT | | (17) DRT DRT L yY PolSAR data. DRT is used to measure the similarity between the two 3) Compute the CFAR threshold for a specific Pfa . polarimetric covariance matrices X and Y and perform change 4) Apply the threshold and obtain the binary change detec- detection by choosing between hypotheses [5] tion map. It is important to mention that the quality of the change detec- H0 : x = y (18) tion map depends on the estimation accuracy of L x and L y. : = . H1 x y For an efficient estimation of the ENL, many methods have been put forward in the literature for automatic estimate. Null hypothesis (H ) corresponds to no-change, and hypoth- 0 To name a few of them, we mention the following. First, esis (H ) corresponds to change. To quantify the differ- 1 the method of Anfinsen et al. [16] was based on the maxi- ence between H and H , a threshold selection procedure 0 1 mum likelihood estimator for the ENL under the assumption is applied to the test statistic τ . It is worth mentioning DRT that data follow the complex Wishart distribution. Second, that the hypothesis tests are developed with distinct ENLs, the method proposed by Tao et al. [26] was based on the i.e., L = L . Nevertheless, in the first experiment with sim- x y development of trace moments (DTMs). This ENL estimator ulations, we assume that the ENL is the same for both images. cancels the textural variation using trace moments. Finally, the The exact distribution of the DRT statistic under null method developed by Bouhlel [27] was based on the fractional hypothesis (H ) is Wilks’s lambda distribution of the second 0 moments of the determinant of the multilook polarimetric kind given by covariance (FMDC) matrix. The FMDC estimator of the ENL τDRT ∼ (L x + L y, d, L y). (19) had the particularity of being independent of the distribution of the texture model. All these methods consisted in performing a = = For particular case where L x L y L, the DRT statistic local estimate of the ENL by using a sliding window covering τ ∼ ( , , ) becomes DRT 2L d L . the whole image. Then, the distribution of estimates was drawn and the mode value of the density corresponds to the desired B. CFAR Thresholding Method global estimate of the ENL. It is worthy to note that the presence of texture and correlation between samples can affect In our study, change detection is realized by applying a the estimation of the ENL. decision threshold to the test statistics [5]. We choose the In Section IV-C, we briefly describe the HLT and the LRT CFAR algorithm [15] as a thresholding method in order to statistics in order to compare them with our proposed statistic. perform a fair comparison between our proposed approach and the HLT method since we are interested in the test (τ) C. HLT Statistic statistic rather than the thresholding method. Let fτDRT be the distribution of the ratio τDRT under the hypothesis H0.The The complex-kind HLT statistic is defined as significance level of the test αc, expressed in percent, is given −1 τHLT = tr(Y X). (23) as a function of the desired false alarm probability Pfa and then given by αc = 100Pfa. The threshold is determined from The exact distribution of the HLT statistic is difficult to derive the distribution of the determinant ratio statistic. and an approximation was put forward by Akbari et al. [5].

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BOUHLEL et al.: CHANGE DETECTION IN MULTILOOK POLSAR IMAGERY WITH DRT STATISTIC 5

It is the FS1 distribution with three parameters used as an approximation to τHLT and is given as follows:

τHLT ∼ FS(ξ, ζ, μ). (24) The expression of the FS distribution is given by ξ− ξτ 1 (ζ + ξ) ξ μ(ζ −1) fτ (τ) = (25) HLT (ξ)(ζ) μ(ζ − ) ξ+ζ 1 + ξτ 1 μ(ζ −1) where μ = E{τHLT} > 0 is a scale parameter and ξ,ζ > 0are Fig. 2. Pauli decomposition of simulated ﬁve-look quad-pol PolSAR data. two shape parameters. The parameters of the FS distribution (a) Before change. (b) After change. (c) Binary truth change map. are computed in terms of distribution parameters of scaled Wishart matrices X and Y. The solutions for μ, ξ,andζ are τ deﬁned by the following equation system [5]: The distribution of LRT under the null hypothesis (H0)is approximated by using the associated asymptotic distribution (FS) (HLT) mν (μ,ξ,ζ)= mν (L x , L y, d), ν = 1, 2, 3 (26) of the test statistic [10] (FS) (HLT) ν 2 2 2 2 2 2 where mν and mν are the th order moments of the τLRT ∼ χ (d ) + w2[χ (d + 4) − χ (d )] (33) FS distribution and the HLT statistic, respectively. For more χ 2( 2) χ 2 2 details about the expressions of the moments, reader can refer where d denotes a central distribution with d degrees of freedom and to [5] and [28]. Technically, to estimate the shape parameters ξ and ζ , a minimum distance optimization is used to solve the 2 2 2( 2 − ) w =−d − 1 + d d 1 following: 2 1 ρ 4 24 3 1 1 1 1 (ξ,ˆ ζ)ˆ = (FS) − (HLT) 2. × + − . arg min mν mν (27) 2 2 2 2 (34) (ξ,ζ ) L L (L x + L y) ρ ν=2 x y

Finally, a good ﬁtting of the FS distribution depends on the The test with a desired Pfa is given by estimation accuracy of (μ,ξ,ζ), which in turn depends on the H1 estimates of L x and L y [5]. It is worth noticing that the FS τLRT ≷ T (35) distribution of the HLT detector for a low number of looks H0 does not provide a good approximation and ﬁtting as it will where the threshold T is determined through the equation be seen in the experiments. +∞ : = (τ| ) τ. The combined test is derived as follows Pfa fτLRT H0 d (36) T H τ ,τ ≷1 . max HLT HLT T (28) It is worth mentioning that the LRT is a one-sided test; H0 however, the DRT and HLT are two-sided test. The threshold is determined from the resolution of the follow- : ing equation V. E XPERIMENTAL RESULTS +∞ = (τ| ) τ. The performance of the proposed DRT statistic is evaluated Pfa 2 fτHLT H0 d (29) T on both simulated and real PolSAR images. The HLT and the LRT tests are implemented for comparison with our test.

D. LRT Statistic A. Simulated Data The Wishart LRT statistic was derived as [10] We simulate two quad-pol data containing two L-look Pol- ( + ) × ( + )d L x L y | |L x | |L y SAR images of 250 250 pixels and having four polarimetric = L x L y L x X L yY . Q + (30) channels (d = 4). The generated data follow a scaled Wishart dLx dLy | + |L x L y L x L y L x X L yY distribution with a covariance matrix of the speckle j deﬁned The test statistic for change detection based on the LRT is in Table I where j ={1,...,7}. Then, the polarimetric data given by contain seven different classes (areas). Area 7 corresponds to the polarimetric properties of a heterogeneous urban area. τ =−2ρ ln Q (31) LRT Area 3 is simulated with the polarimetric properties of a where homogeneous water region. The rest of the areas correspond 2d2 − 1 1 1 1 to the properties of agricultural crops and vegetation regions. ρ = 1 − + − . (32) Different values of the number of looks L areusedinthis 6d L L L + L x y x y study, L ∈{5, 6, 7, 8}. Fig. 2 shows the Pauli decomposition 1This notation is used for FS distribution with three parameters. However, of the two simulated ﬁve-look quad-pol PolSAR data corre- with two parameters, we use the notation (F). sponding to images before and after change and the binary

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Fig. 3. Comparison between DRT, HLT, and LRT statistics for simulated data with different levels of multilooking. The number of looks is L ∈{5, 6, 7, 8}, = = {τ ,τ } τ where the ﬁrst raw L 5andthelastrawL 8. (ai) Logarithm of max DRT DRT . (bi) Plots of DRT normalized histograms and estimated Wilk’s lambda {τ ,τ } τ of second kind pdfs under (H0) hypothesis. (ai’) Logarithm of max HLT HLT . (bi’) Plots of HLT normalized histograms and estimated FS pdfs under (H0) hypothesis. (ai”) τLRT. (bi”) Plots of τLRT normalized histograms and estimated distribution given by (33) under (H0) hypothesis. Change detection map obtained by rejection of hypothesis test at 1% signiﬁcance level for (ci) DRT, (ci’) HLT, and (ci”) LRT detector.

TABLE I and for different cases of L. As it can be seen, each row COVARIANCE MATRIX FOR EACH REGION OF SIMULATED DATA corresponds to a particular value of L starting from 5 for the first row to 8 for the last row. Fig. 3(ai), (ai’), and (ai”) where i∈{1, 2, 3, 4} show, respectively, the logarithm of DRT, the logarithm of HLT, and the LRT statistics for different values of L. In addition, Fig. 3(bi) shows the comparison between the normalized histograms of τDRT and the estimated Wilks’s lambda of the second kind pdfs computed over the nonchange area. It is clear that the estimated pdf curves fit well with the normalized histograms. The same comparison is made in Fig. 3(bi’) between τHLT and the estimated FS truth change map where the change is marked by the white pdfs used as approximations. It is shown that for a low and the no-change by the black. number of looks smaller than 7, the FS distribution of the By computing the DRT, HLT, and LRT statistics, Fig. 3 HLT detector is not providing a good fitting. However, the shows the results of change detection relative to these methods DRT detector works better for the small number of looks.

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BOUHLEL et al.: CHANGE DETECTION IN MULTILOOK POLSAR IMAGERY WITH DRT STATISTIC 7

TABLE II TABLE III VALUES OF KS AND p-VALUES (%) OBTAINED BY USING WILK’SOF CHANGE DETECTION PERFORMANCE FOR SIMULATED DATA.THE BEST SECOND KIND DISTRIBUTION FOR SIMULATED DATA WITH VALUES ARE MARKED BY RED DIFFERENT LEVELS OF MULTILOOKING

Another comparison is made in Fig. 3(bi”) between τLRT and the estimated pdf given by (33). Furthermore, Fig. 3(ci), (ci’), and (ci”) shows the corresponding binary change maps obtained by the thresholding CFAR algorithm. Indeed, binary change detection map is obtained by the rejection of the hypothesis test at a 1% significance level for the DRT, HLT, and LRT detectors. It is noted that changes can be seen in these statistics for each case of L-values. Moreover, the proposed method detects more efficiently the changed area even for highly speckled cases with a low number of looks. It is also worth noticing that at the central part of the binary images (region 2), where heterogeneous change detection is present, the DRT is better than the others, but all three detectors seem not to work properly. This leads us to extend this work under non-Wishart case [29], [30] or relaxed Wishart distribution where the ENL is a local parameter [31]. The fit ability between the estimated Wilks’s of the second kind pdfs and the normalized histograms of τDRT is evaluated qualitatively by using the Kolmogorov–Smirnov (KS) hypothesis test. The smaller value of KS indicates better the hypothe- sized model fits with the empirical distribution. A small value of p-value of the test indicates strong incompatibilities of the data with the employed distribution hypothesis. Table II lists the values of KS and the p-values of the test (in percentage) obtained for the four cases of L under null hypothesis (H0). It is evident from the p-value that the fitted Wilk’s of the second kind distribution can perfectly model the no-change area. A quantitative evaluation of the change detection performance is also provided at four different significance levels or specified FARs. Table III shows the measured FAR [false positive rate (FPR)] and the detection rate [true positive rate (TPR)] for DRT, HLT, and LRT statistics for various levels of multilooking and for different specified FARs. As shown in Table III, the DRT statistic realizes higher detection rates and lower overall error rates than the HLT and LRT statistics curves for DRT, HLT, and LRT statistics and for different at specified FARs, especially when the speckle is strong and values of L. It is shown that the DRT statistics give the best the number of looks L is low. As L increases to reach 8 and performance for this example followed by the HLT statistics at specified FARs, the detection rates for these statistical tests in all cases and then by the LRT statistics. The area under increase and the overall error rates decrease. Likewise, when ROC curve (AUC) is also considered in this study. The larger L passes to 8, the detection rate of both the DRT and HLT the AUC, the better the performance. Accordingly, the AUC becomes close. It is also worth noticing that the measured values are shown in Table IV for these statistics and multiple FAR is close to the specified FAR regardless of the detector values of L, and we can easily conclude that the proposed DRT used. statistic outperforms both the HLT and the LRT statistics for Another quantitative evaluation of the performance of the low values of L. When the ENL increases, the AUC of the method is provided by the receiver operating characteris- HLT reaches the AUC of the DRT statistic. Consequently, the tic (ROC) curves that are plotted for these statistics using two statistics give us similar results. The performance of the the ground truth. The ROC curve is the evolution of the proposed change detection algorithm is further evaluated with TPR as a function of the FPR [32]. Fig. 4 shows the ROC different real multilook PolSAR data sets in Section V-B.

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Fig. 4. ROC curves comparison of three statistical detectors DRT, HLT, and LRT for different levels of multilooking L ∈{5, 6, 7, 8}. The probability of detection (TPR) is plotted as a function of the probability of a false alarm (FPR) for simulation data. The DRT statistic performs better than HLT and LRT statistics. (a) L = 5. (b) L = 6. (c) L = 7. (c) L = 8.

Fig. 5. Pauli RGB (R = HH-VV, G = HV, and B = HH + VV) of quadpol RADARSAT-2 images captured over Suzhou city, East China on (a) April 9, 2009 and (b) June 15, 2010, multilooked with 24-looks. (c) Truth change map. The white means no-change, the gray means change, and the black means unlabeled pixels.

TABLE IV In contrast, the black designates urban areas that do not satisfy AUC FOR SIMULATED DATA COMPUTED WITH VARIOUS METHODS (DRT, the Wishart assumption. The ENL for both images before HLT, AND LRT) AND VARIOUS NUMBER OF LOOKS.THE BEST and after changes is estimated to be 7.2 and 6.9, respectively. VALUES ARE MARKED BY RED Fig. 6(a), (a’), and (a”), respectively, shows the logarithm of the DRT, the logarithm of the HLT, and the LRT statistic. The plots of the normalized histograms and the estimated distributions for DRT, HLT, and LRT detectors are given, respectively, in Fig. 6(b), (b’), and (b”). Fig. 6(c), (c’), and (c”) shows their corresponding binary change map obtained by the thresholding CFAR algorithm. The binary change detection map is obtained by the rejection of the hypothesis test at a 1% significance level for the DRT, HLT, and LRT detectors. Regarding the quantitative assessment, the ROC curves for the DRT, HLT, and LRT statistics used in the experiments are given in Fig. 7. In this example, the obtained results show that the ROC curves of the HLT and LRT are above the ROC curve of the DRT. The measured AUC for these B. Real PolSAR Data methods based on the DTR, HLT, and LRT statistics is 99.68%, 1) Data set 1: For real data, we use a set of two 99.45%, and 99.13%, respectively. Different significance levels RADARSAT-2 fully PolSAR images over an urban area in of the test are chosen where αc ∈{0.5%, 1%, 5%, 10%} for Suzhou city, East China, acquired on April 9, 2009, and June evaluation. The detection rate, the measured FAR, and the 15, 2010. The two images corresponding to before and after overall error rate for the three methods are shown in Table V. changes are multilooked with 24-looks (six looks in range and At each specified FAR level, the best values are highlighted four looks in azimuth directions). Fig. 5(a) and (b) shows the in red. We can clearly see that the DRT always gives the best Pauli decomposition images of these two PolSAR images. The detection rate for any specified FAR level. Also, for the overall ground-truth map is represented in Fig. 5(c) and contains no- error rate, the DRT gives good results in comparison with the change test pixels with white and change test pixels with gray. other statistics for the case of a low value of significance levels

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BOUHLEL et al.: CHANGE DETECTION IN MULTILOOK POLSAR IMAGERY WITH DRT STATISTIC 9

{τ ,τ } Fig. 6. Comparison between DRT, HLT, and LRT statistics for real experimental data set over Suzhou city. (a) Logarithm of max DRT DRT . (a’) Logarithm {τ ,τ } τ of max HLT HLT . (a”) LRT. Plots of normalized histograms and estimated distribution for (b) DRT, (b’) HLT, and (b”) LRT detectors. Change detection map at 1% signiﬁcance level obtained from (c) DRT, (c’) HLT, and (c”) LRT statistic.

TABLE V CHANGE DETECTION PERFORMANCE FOR REAL POLSAR DATA SET 1

Fig. 7. ROC curves comparison between DRT, HLT, and LRT statistics for real PolSAR data set 1. The DRT detector performs better than the HLT and LRT detectors. of the test αc ∈{0.5%, 1%}. For large values of αc,theHLT wins. The measured FAR are close to the speciﬁed FAR for the 2) Data set 2: Two coregistered pair of L-band polarimetric = DRT and LRT statistics when αc ∈{5%, 10%}.However,for images (d 3) corresponding to scenes 1 and 2 were acquired low values of αc, the HLT gives similar values for measured by the Jet Propulsion Laboratory/National Aeronautics and and speciﬁed FAR. Space Administration UAVSAR (1.26 GHz) over the city of

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Fig. 8. UAVSAR images (in Pauli decomposition) over the city of Los Angeles, California. (a) and (a’) April 23, 2009. (b) and (b’) May 11, 2015. (c) and (c’) ground truths, where white is change and black is no-change. Top: Scene 1. Bottom: Scene 2.

Los Angeles, California, on April 23, 2009, and May 11, 2015. TABLE VI The images are 2 × 3 multilooked. Fig. 8 shows the Pauli CHANGE DETECTION PERFORMANCE FOR REAL POLSAR decomposition of these two scenes with two images each are DATA SET 2: SCENES 1 AND 2 obtained by the JPL’s UAVSAR sensor at two different times. Fig. 8(c) and (c’) shows the ground truths used to compute the ROC curves. We recall that the white corresponds to change and the black to no-change. As we can see, the interesting area of this data set is an urban area where the changes occurred due to the effects of urbanization. Fig. 9 compares the change detection results relative to DRT, HLT, and LRT statistics. As it can be noted, the logarithms of {τ τ } {τ ,τ } τ max DRT DRT ,max HLT HLT ,and LRT are first computed and shown, respectively, in Fig 9(ai), (ai’), and (ai”), where i ∈{1, 2} representing scenes 1 and 2. The areas with change are marked in green (small change) and red (strong change). The areas without change are marked in blue. Fig. 9(b1) and (b2) show a good fitting between the normalized histograms of τDRT and the estimated Wilks’s lambda of the second kind pdf computed over the nonchange area, respectively. As shown in Section II, Wilks’s lambda of the second kind distribution depends on the ENL and dimension (d) of the covariance matrix. The comparison between the normalized histograms of τHLT and the estimated FS pdf computed over the nonchange area is shown in Fig. 9(b1’) and (b’2). We can see here a good fitting of the FS distribution with the normalized histograms. The same comparison is realized in Fig. 9(b”1) and (b”2) with the LRT statistic and the approximated pdf given by (33) and computed over the nonchange area. As we map obtained by the thresholding CFAR algorithm applied to can see, the estimated pdf curves based on χ 2 distributions do the DRT, HLT, and LRT statistics, where the significance level not fit well the normalized histograms. As consequence, the of the test αc is chosen equal to 1%. It is worth to notice that CFAR algorithm fails to provide the best threshold for the LRT with the DRT statistics, more regions are detected than those statistic. Fig. 9(ci), (ci’), and (ci”) shows the binary change using the HLT statistics.

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BOUHLEL et al.: CHANGE DETECTION IN MULTILOOK POLSAR IMAGERY WITH DRT STATISTIC 11

{τ ,τ } Fig. 9. Comparison between DRT, HLT, and LRT statistics for real experimental data set over Los Angeles. (ai) Logarithm of max DRT DRT for scene i, ∈{ , } {τ ,τ } τ i 1 2 . (ai’) Logarithm of max HLT HLT . (ai”) LRT. Plots of normalized histograms and estimated distribution for (bi) DRT, (bi’) HLT, and (bi”) LRT detector. Change detection map at 1% signiﬁcance level obtained from: (ci) DRT, (ci’) HLT, and (ci”) LRT statistic.

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evaluated quantitatively using the KS goodness-of-fit test. With this statistic test distribution, the decision threshold can be efficiently determined at a specified probability of false alarm by using the CFAR-threshold method. The performance of the method has been evaluated on simulated and real multilook PolSAR data where ground-truth data were available and has been compared to the performance of the known HLT and LRT detectors. The results in terms of measured FAR, detection rate, ROC curve, and AUC have shown that the proposed DRT statistic outperforms the HLT and LRT performances especially for low ENL values and can perform a binary change detection map very close to the ground-truth data. The method can be extended to a heterogeneous model by considering the texture in the change detection process. The presence of texture can be useful to improve the detection, but additional texture parameters need to be estimated.

APPENDIX A The characteristic function of the random variable V = ln((2L)d (|X|/||)) is given as follows: ( )d |X| itV it ln 2L || ψV (t) = E{e }=E e (37) |X| it = E ( L)d 2 || (38) Fig. 10. ROC curves comparison between DRT, HLT, and LRT statistics for real PolSAR data set 2. Top: Scene1. Bottom: Scene2. The DRT detector |LX| it performs better than the HLT and LRT detectors, especially for a low FPR. = E d . 2 || (39) For a final comparison between these change detectors, Let us consider the Hermitian matrix Z = LX, which follows we draw Table VI. It reports the detection rate, the measured the nonsingular complex Wishart distribution. The pdf of Z is FAR, and the overall error rate computed for the three detec- as follows: tors for scenes 1 and 2, at four different significance levels L−d of the test α . The DRT statistic obtains the highest detection |Z| − c f (Z) = (− 1Z). Z L etr (40) rate in this example, followed by the HLT statistic. The LRT d (L)|| statistic gives the worst performance. The DRT also achieves Then, the integral over all the positive definite complex a lower overall error rate compared to the HLT and LRT Hermitian matrices verifies statistics except for scene 1 when αc ≥ 5%. Regarding the measured FAR, the DRT gives values close to the specified L−d −1 L |Z| etr(− Z)dZ = d (L)|| . (41) FAR in most cases (cases of scene 2), but in other cases, the + values are far apart (cases of scene 1). The performance of We can write that the DRT statistic is evaluated as well through the ROC curve and compared with that of the HLT and LRT statistic. Fig. 10 L+it−d −1 L+it |Z| etr(− Z)dZ = d (L + it)|| . (42) shows the corresponding ROC curves of the three detectors, + indicating a better performance for the DRT statistic for low As a consequence, (39) can be written as FPR values followed by the HLT and then by the LRT. The AUC is also provided for this example. For scene 1, it is ψ (t) V 81.60% for the DRT, 80.42% for the HLT, and 78.96% for | | it d Z the LRT. For scene 2, it is 78.31% for the DRT, 77.77% for = 2 fZ(Z)dZ (43) || the HLT, and 74.41% for the LRT. Indeed, the AUC given by + 2itd the DRT statistic is larger than the others. = |Z|L+it−d etr(−−1Z)dZ (44) ( )||L+it d L + itd VI. CONCLUSION 2 + = (L + it)||L it (45) ( )||L+it d The DRT statistic has been proposed for change detection d L (L + it) in multilook PolSAR images. Under the null hypothesis cor- = 2itd d (46) responding to no change, the statistic follows exactly Wilks’s d (L) lambda distribution of the second kind depending on the (L + it)(L + it − 1)...(L + it − d + 1) = 2itd . (47) ENL and the covariance matrix dimension. The fit ability is (L)(L − 1)...(L − d + 1)

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The pdf of the chi-squared distribution with 2L degrees of it follows that the density function of the Beta distribution of freedom χ 2(2L) is given by the second kind has the following Mellin transform: ( + L − − ) ( − − + ) 1 − − x 1 y i s L x i 1 s ( ) = L 1 2 . M{ f (x )}(s) = . (62) fχ 2(2L) x x e (48) i i ( − ) ( − ) 2L (L) L y i L x i ( ) Using the previous pdf, we can write Then, the Mellin integral transform of the pdf g z of the product of d independent random Beta type II variables is +∞ − − x − L 1 2 = L ( ). d1 x e dx 2 L (49) (1 + L y − i − s) (L x − i − 1 + s) 0 M{g(z)}(s) = . ( − ) ( − ) = L y i L x i The characteristic function of an ln χ 2(2L) random variable i 0 (63) is given by Accordingly, the g(z) pdf is written otherwise as a function ψ ( ) = { it ln X }= { it} ln χ 2(2L) t E e E X (50) of the inverse Mellin transform and is given as follows: +∞ it ( ) = x fχ 2(2L)(x)dx (51) g z 0 d−1 +∞ ( + − − ) ( − − + ) 1 −s 1 L y i s L x i 1 s 1 L+it−1 − x = z ds = x e 2 x L d (52) 2πi L (L y − i) (L x − i) 2 (L) 0 i=0 (L + it) (64) = it . 2 ( ) (53) d−1 L A s = z (1 + L y − i + s)(L x − i − 1 − s)ds 2πi L The characteristic function of the sum of independent i=0 χ 2( ) χ 2( ( − )) χ 2( ( − + )) ln 2L ,ln 2 L 1 ,…, ln 2 L d 1 random (65) variables is which is the Meijer G-function multiplied by constant A with ψ χ 2( )+ χ 2( ( − ))+···+ χ 2( ( − + ))(t) (54) b = L − i − 1anda =−(L − i) ln 2L ln 2 L 1 ln 2 L d 1 j x j y = ψ χ 2( )(t) × ψ χ 2( ( − ))(t) ×···×ψ χ 2( ( − + ))(t) − , −( − ),...,−( −( − )) ln 2L ln 2 L 1 ln 2 L d 1 ( ) = × d,d L y L y 1 L y d 1 g z A Gd,d z (55) L x − 1, L x − 2,...,L x − d (L + it)(L + it − 1)...(L + it − d + 1) (66) = 2itd . (L)(L − 1)...(L − d + 1) = d−1( /(( − )( − ))) where A i=0 1 L x i L y i . (56) ( ) = t ( ) The cumulative distribution function G t 0 g z dz is We can conclude that obtained by integrating (65) with respect to the inside contour integral by using ψ ( ) = ψ ( ). V t ln χ 2(2L)+ln χ 2(2(L−1))+···+ln χ 2(2(L−d+1)) t (57) t (1 + s) zs dz = (1 + s)−1t1+s = t ts . (67) 0 (2 + s) APPENDIX B Again, using the deﬁnition of the Meijer G-function (10), this The Mellin integral transform of the density function g(z) results in d−1 of the product i=0 Xi of d independent random variables Xi G(t) ( ) with density functions fi xi is given by [33] d−1 At s − = t ( + L − i + s)(L − i − − s) d 1 π 1 y x 1 2 i L = M{g(z)}(s) = M{ fi (xi )}(s). (58) i 0 = (1 + s) i 0 × s ( + ) d (68) Under suitable restrictions [34], [35] satisﬁed by all density 2 s = × functions, the density function g(z) is given by the following At − ,−( − ),...,−( − ( − )), inverse formula: d,d+1 L y L y 1 L y d 1 0 . Gd+1,d+1 t L x −1, L x −2,...,L x −d,−1 d−1 1 −s (69) g(z) = z M{ fi (xi )}(s)ds. (59) 2πi L i=0 The Meijer G-function is viewed as an inverse Mellin trans- − + − form Since the Mellin integral transform of 1/(1 + x)L x i L y i is ,..., given by m,n a1 ap = 1 −s ( ) G p,q z z Z s ds (70) b1,...,bq 2πi L ( − + − − )( ) M 1 ( ) = L x i L y i s s ( ) = M{ ( )}( ) − + − s where Z s g z s is the Mellin transform of the ( + )L x i L y i ( − + − ) 1 x L x i L y i g(z) pdf. The kth moment E{Z k} is given by (60) d−1 ( − − ) ( − + ) k L y i k L x i k ( ) E{Z }=Z (s)|s=k+1 = . and for any function f x (L − i) (L − i) i=0 y x L x −i−1 M{x f (x)}(s) = M{ f (x)}(s + L x − i − 1) (61) (71)

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Eltoft, “Estimation of the equiv- Nizar Bouhlel (Member, IEEE) received the engi- alent number of looks in polarimetric synthetic aperture radar imagery,” neering degree in electrical engineering and the IEEE Trans. Geosci. Remote Sens., vol. 47, no. 11, pp. 3795–3809, master’s degree in communication systems from Nov. 2009. the Ecole Nationale d’Ingénieurs de Tunis (ENIT), [17] T. Pham-Gia, “Exact distribution of the generalized Wilks’s statistic Tunisia, in 1997 and 1999, respectively, and the and applications,” J. Multivariate Anal., vol. 99, no. 8, pp. 1698–1716, Ph.D. degree in applied mathematics from the Uni- Sep. 2008. versity of Paris Descartes, MAP5, France, in 2006. [18] N. Otsu, “A threshold selection method from gray-level histograms,” From 2007 to 2011, he worked as a Researcher at IEEE Trans. Syst., Man, Cybern., vol. 9, no. 1, pp. 62–66, Jan. 1979. the Faculty of Sciences of Monastir, Communication [19] J. Kittler and J. Illingworth, “Minimum error thresholding,” Pattern Department. From 2012 to 2020, he was a Post- Recognit., vol. 19, no. 1, pp. 41–47, Jan. 1986. Doctoral Researcher in several French laboratories: [20] M. Ghanbari and V. Akbari, “Unsupervised change detection in polari- Laboratoire d’Imagerie Biomédicale (LIB), UPMC; Laboratoire des Sciences metric SAR data with the hotelling-lawley trace statistic and minimum- du Numérique (LS2N), ECN; Institute of Electronics and Telecommunications error thresholding,” IEEE J. Sel. Topics Appl. Earth Observ. Remote of Rennes (IETR) with the team SHINE; and INSA Rennes and IETR Sens., vol. 11, no. 12, pp. 4551–4562, Dec. 2018. at CentraleSupélec, Rennes, France. In December 2020, he was hired as [21] J.-S. Lee and E. Pottier, Polarimetric Radar Imaging: From Basics to an Assistant Professor at Institut Agro, Statistics and Computer Science Application. Boca Raton, FL, USA: CRC Press, 2009. Department, Angers, France, and at the Research Institute on Horticulture [22] N. R. Goodman, “Statistical analysis based on a certain multivariate and Seeds (IRHS), Team “Imagerie pour l’Horticulture et le Phénotypage” complex Gaussian distribution,” Ann. Math. Statist., vol. 34, no. 1, (ImHorPhen). His research focuses on stochastic signal and image processing, pp. 152–177, Mar. 1963. radar signal processing, and signal processing for digital communications.

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BOUHLEL et al.: CHANGE DETECTION IN MULTILOOK POLSAR IMAGERY WITH DRT STATISTIC 15

Vahid Akbari (Member, IEEE) received the M.Sc. Stéphane Méric (Member, IEEE) received the Elec- degree (summa cum laude) in remote sensing from trical Engineer Diploma from the National Insti- the University of Tehran, Iran, in 2009, and the Ph.D. tute for the Applied Sciences (INSA), Rennes, degree in physics, majoring in earth observation, France, and the M.S. degree in signal process- from the UiT The Arctic University of Norway, ing and telecommunications from the University of Tromsø, Norway, in 2013. Rennes 1, both in 1991, the Ph.D. degree in electron- He continued his research in radar remote sens- ics from INSA, in 1996, and the HDR (habilitation ing as a Post-Doctoral Research Fellow with the à diriger des recherches) from the University of Department of Physics and Technology, UiT The Rennes 1, in 2016. Arctic University of Norway and Norwegian Instiute Since 2000, he has been an Assistant Professor of Bioeconomy Research. He has been a Visiting at INSA. In 2005, he joined the SAPHIR Team Scientist with the Signal Processing and Telecommunications Laboratory, (IETR – CNRS UMR 6164, Rennes). He was interested in using SAR Department of Electrical, Electronic, Telecommunications Engineering and data in radargrammetric applications. Furthermore, he is working on radar Naval Architecture, University of Genoa, Italy, in 2011, and a Visiting system (CW, FMCW) dedicated to speciﬁc SAR applications (radar imaging Researcher with the German Geoscience Center (GFZ), Potsdom, Germany, in motorway context, remote sensing, MIMO conﬁguration, passive radar in 2008. He has been an Assistant Professor of Remote Sensing with the imaging) and remote sensing applications. His education activities are about University of Tehran in 2015. He is currently a Research Fellow with the analog electronics, signal processing, radar and radar imaging, as well as Division of Computing Science and Mathematics, University of Stirling. His electromagnetic diffraction. He is currently the Head Master of the Commu- research interests include development of methods in machine learning, pattern nication System and Network Department, INSA Rennes. He is a co-author recognition, and image processing to extract information of land and ocean of more than 40 conference papers, 11 journal papers, two book chapters, and with radar remote sensing data. one patent. He has been supervising eight Ph.D. students and currently six Ph.D. students. Dr. Méric was the EuRAD 2019 and the Automotive Forum 2019 TPC Co- Chair. He has set with the educational team and teaching department in an engineering school at Oujda, Morrocco.

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