Using Watersheds segmentation on ISAR image ... - Semantic Scholar

Using Watersheds segmentation on ISAR image for automatic target recognition Abdelmalek Toumi , Brigitte Hoeltzener and Ali Khenchaf ENSIETA, E3I2-EA3876. 2, rue Franc¸ois Verny, 29806 cedex 9, Brest. France {toumiab, Brigitte.Hoeltzener, Ali.khenchaf}@ ensieta.fr

Abstract This paper deals with the processing adopted for shape extraction from the 2D-presentation (image) in radar automatic target recognition field. The goal is to provide helpful information to human operator for target recognition. However, extracting the target characteristics from a radar echoes is the rather difficult task. Hence, several kinds of radar signatures can be employed to acquire information about target [10, 11]. In this paper, we present one approach for retrieval system for target recognition based on ISAR-images in radar experimentation field. Then, we propose efficient features that deals with target shape which are extracted using Watersheds transformation. Of course, the target shape gives a better human interpretation.

1. Introduction In the modern war, how to recognize military target such as aircraft, naval ships, missile etc, is significant to attack the goal accurately. Thus, the radar signatures are exploited to perform and produce an efficient strategy for recognition. However several kinds of radar signatures can be employed to acquire information about the target characteristics. The main kinds deal with ISAR image of the target such that the information about the geometry of the target is revealed. This can be done in one radar reflection in range. Such a signature is called a radar range profile. Under certain circumstances, the use of the motion of the target information in the perpendicular direction on the line-of-sight can also be extracted from a two-dimensional image (ISAR image). It is the reason why lot of techniques have been investigated in this domain notably, several researches have been focused on ISAR techniques and automatic target recognition (ATR).In recent literature, several advances in ISAR image classification can be found in [10, 12, 7, 11]. This paper aims to ISAR image segmentation in order to extract the target shape as feature vectors. The feature

vectors are proposed in this framework as a mean of overcoming operator limitations and provide an helpful tool to decision making. Hence, we deal with 2D-ISAR images because they give more detailed information on the target (aircraft, naval ships, missile etc) geometry. The methodology used to design the complete processing chain from the acquisition step, to the recognition step (classification step), is issued from artificial intelligence approach. This in following the process of Knowledge Discovery from Data (KDD) which has been adapted to radar target recognition system [13]. In the section below, a simulation data and some parameters of radar configuration are presented. After, in order to give a better information to human operator, the shape is extracted by using Watersheds transformation. Hence, the hierarchical segmentation will be achieved as presented in section 3. The section 4 and section 5 are dedicated to present some details and results in this purpose.

2. Simulation data A radar data acquisition system is studied from an anechoic chamber of ENSIETA (Brest, France) in a specific environment of experimentation [13]. It facilitates achievement of real measurements and allows to have good control on the target-radar configuration. Thus, the human interpretation results can be made easier. To construct our simulation database of ISAR images, we used a scale reduced (1/48) aircraft models. Each target is illuminated in the acquisition level with a frequency stepped signal between 11.65GHz and 18GHz which is the bandwidth B. So, a sequence of N +1 pulses is emitted with linearly increasing frequencies fn = f0 +n∆f at time instance tn , where n runs from 0 to N . The frequency increment in our case is △f = 50M Hz (n = 128 frequency samples). The ISAR imagery produces a twodimensional distribution of the target scattering centers with both azimuth (cross-range) and range domain. Range resolution gives one imaging-resolution (range profile), and

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the resolution of differential Doppler caused by target rotation gives an orthogonal dimension (cross-range). The simulation step of our system used azimuthal domain from α0 = −5◦ to αn = 95◦ ([−5◦ ; 95◦ ]) with angular increment △α = 0.5◦ . We obtained ISAR-Image from the complex signature recorded over an angular sector of ∇α = 20◦ with an angular increment ∆α. We note that for classification purpose, we want that ISAR image contains as much information as possible. This requires a high resolution and therefore it is desirable to use a largest bandwidth given by the radar hardware. In this order, the bandwidth B is chosen. The appropriate technique to construct ISAR-images is the Inverse Fast Fourier Transform (IFFT) [8]. The figure 5(a) presents an example of ISAR-image for an aircraft target reconstructed by IFFT algorithm. In this paper, the whole database of ISAR images is built using IFFT algorithm.

3. Shape extractor from ISAR image To achieve shape extraction, several processing steps are required. In the first processing step, we perform segmentation of the image ISAR. In this segmentation process, the target response is extracted from the background which includes the noise and clutters response. For this purpose, since the data is obtained from anechoic chamber, Then, we assume that the signal to noise ratio (SNR) and signal to clutter ratio (SCR) are relatively high. We can note that the noise of anechoic chamber is measured and substracted from the signal in the acquisition step. Thus, we use a simple method of mean threshold. The preprocessing image is noted I(xi , yj ), i = 1, 2, . . . , M, j = 1, . . . , N where M , N are the number of pixels in the down-range and crossrange dimension. In the next processing, we accomplish the shape extraction by using a watersheds method. The proposed method can be applied to kind of ISAR images of aircraft and ship. An image I is seen as a topographic relief by considering grayscale as altitude information. So, topographic surfaces are handled through digital elevation models (DEM’s). The watersheds transformation was introduced as morphological tool by H.Digabel and C. Lantuejoul [5] research which was based on binary images. Later, the completed work of C. Lantuejoul and S. Beucher has extended the ”inversion” of the original algorithm on the more general framework in grayscale images [3, 1]. In [1], S.Beucher described morphological tools used in segmentation, he introduced a concept of marker and the homotopy modification to resolve over-segmentation problems. Thus, the watersheds transformation of hierarchical segmentation is introduced. For this purpose, different levels of segmentation starting from a graph representation of the images based on the mosaic image transform are defined. We have used this concept to extract the target shape.

In the next section, we explain the necessary watersheds principles.

3.1. Watersheds transformation In watersheds segmentation, any object present on a image is generally linked to the minima Mi of the morphologic gradient and their contours to the watersheds of the gradient. Let us give an intuitive definition of this operation by considering image as topographic surface which presents minima. Then, suppose that the minima are pierced and the topographic surface is immersed in water. The water will progressively flood the surface. In the end of flooding, we build dams at any point where waters coming from two different minima. Then, several divided lines appear, called watersheds, which separate different connected components, which are called catchment basins noted CB(M ). Each one of this catchment basins is associated to a single minimum. The figure 1 illustrates watersheds notions. Let us consider an ISAR image I that takes discrete value W a te rS h e d s (W S )

C a tc h m e n t b a s in s (C B )

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M in im a W a te rS h e d s (W S )

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Figure 1. Watersheds principle. (gray) in a given range [0, K], where K is an arbitrary positive integer. Therefore, each image can be represented by a function I(p) : Z 2 → Z and can be seen as a topographic surface S, where I(p) represents the grayscale value of the image at a point p. The points p of the space Z 2 is the vertices of a square grid G in four or eight connectivity, or hexagonal grid in six connectivity. The set M of all minima of I is made of various connected components Mi (I) [14]. Boundaries that separate catchments bassin constitute a watersheds W S(CB) of the image I. We must noted that, different techniques allow to determine the watersheds and catchments bassin of grayscale image. For some relief configuration, the results of these techniques may uneasy differ [2]. The common technique used in segmentation framework to build the watersheds is a flooding procedure. We have used this procedure to constitute the watersheds with 8−square grid.

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3.2. Watersheds segmentation A morphological segmentation by watersheds usually proceeds through two steps. The first one is called marking step, roughly marking markers of the objects to be extracted. This marking is the fundamental step of segmentation process. The second step extracts the contour of objects with applying the watersheds transformation on the gradient image [2] (see figure 4). For an ISAR image, the scatterers are principally represented by significant contrast values on ISAR image as shown in figure 2. To detect the shape tar-

Figure 3. Topographic surface of 3 scatterers. s c a tte re r re p re s e n ta tio n o n im a g e IS A R

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Figure 4. Watersheds of the gradient image.

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Figure 2. Image ISAR of three scatterers. get from ISAR image, the segmentation task is achieved by using the detection of all scatterers which are localized on image. The scatterers object appear as domes with a peak. Each scatterer has a unique peak (see figure 3). Our objective is to extract the contour of each scatterer object presented on ISAR image. The first simply solution consists to use thresholding. However, using the threshold principle is not efficient and can not be used [2]. We must use a variation of the function that is the gradient. This principle is exploited in different techniques, but to extract the shape target from ISAR image is more complex than to use a simple edge gradient detectors (edge operators). Thus, using edge detectors on ISAR image without preprocessing, can not produce a closed target shape (i.e. contour). They generally give a lot of points dispersed on the image. We have illustrated some examples in [12]. In topographic representation illustrated by figure 3, each scatterer on the original image shown in figure 2 becomes a regional minimum(see figure 4). In practice, the computation of watersheds of the gradient image does not constitute a good segmentation method either. The simple computation of watersheds on the gradient produce an over-segmentation caused by to small variations, manly due to noise (see figure 3) or to some object to not be extracted. In this case, a correct contour of the objects presented on image are lost of irrelevant one. This over-segmentation can be reduced by filtering the initial image or its gradient. But a better segmentation will be started by the marking step that marks patterns to be seg-

mented before performing the watersheds transformation of the gradient. The goal uses available knowledge to separate the shape of the target, noise and background (darkness) of image, etc. Different morphological techniques are proposed in literature [2, 14, 6]. The main idea is proposed to detect markers of all the domes of the image I, then to extract accurate contour and lastly, to remove all undesirable domes by using contour information. This principle to extract markers is used in morphological transformation based on grayscale geodesic reconstruction. This approach is composed of two independent steps, the first step finds the markers for the objects to be extracted. The second one consists in modifying the gradient g in order to produce a new gradient g∗, that called regularized gradient, to provide a new selected markers and computing the watersheds. This image modification is called homotopy modification. In ISAR-image, this approach can be used to extract the shape target if the markers extraction is robust. However, extracting robust markers is almost impossible when specially the image is changed with different target motions. This approach is exploited by another morphologic transformation. It is used to perform image segmentation and consists to hierarchical segmentation based on simplified image called mosaic image. We have used this one to extract the shape of target. It is introduced in order to merge the fragments region which caused the over-boundaries. Thus, the neighborhood relations are introduced by the definition of the graph [2]. A simplified image or mosaic image can be defined as: Definition 1 (Mosaic image) We note Mi , i = 1, ..k, the

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minima of the gradient g(I) of a grayscale image I, and CB(Mi ) their associated catchment bassins. The simplified image that we note I (1) given by : ∀j ∈ {1, ..., k}, ∀p ∈ CB(Mj ), I (1) = inf{I(p), p ∈ Mj } (1) The mosaic image I (1) is of the first order and provides the first decomposition of the initial image on the homogenous regions then we can iterate this processing. This iterative process in image segmentation is too called hierarchical segmentation. From the definition above, firstly, the watersheds are calculated from the gradient image. Secondly, every catchment bassin is labeled with gray value in the initial image I corresponding to the minima of the gradient. In the result, mosaic image may be used to define value graph G(1) . The algorithm used to determine the graph is based on different homogenous regions [4]. We can iterate this procedure and apply to G(1) by using a gradient and watersheds on graphs [2]. We obtained the mosaic image of second level denoted I (2) where each region presents the CB of the previous graph G(1) . The process is then iterated until a level gives us the fulfilled criterion or the desired merging. In the ISAR image, we have used this process until level 3 and 4 for some cases. The figure 5(b) illustrates the first level of segmentation. The over-segmentation is important than on level 3 and 4 presented on figure 5(c) and figure 5(d) respectively. The same processing can be ap-

Figure 6. Watersheds on ship ISAR image. figure 7. In some cases, we obtained several closed shapes in the same image. Therefore, we took only the principal closed shape to be used in the retrieval system.

Figure 7. Extraction of the shape target. Now, the second task in ATR system is to recognize and identify the unknown target. The fundamental problem is to determine the similarity between the known shape stored in database and unknown shape extracted from ISAR-image of an unknown target. This similarity measure must be independent of their position in the image. It results from this, that the shape descriptors must be accurate, compact and invariant with a certain number of geometrical transformations (translation, rotation, scaling,...).

4. Shape descriptors (a) ISAR image of Mig29.

(b) Watersheds of gradient.

In our case, for shape representations we used the contour-based methods that need extraction of boundary information. Therefore, for generic purposes, this kind of shape representation is necessary to accomplish the classification task. The goal of classification is to assign a new target to a class from a given set of classes based on attributes’ values of these targets. (c) Watersheds at level 3.

(d) Watersheds at level 4.

Figure 5. Watersheds at different levels. plied on the ship ISAR-image as presented on figure 6. In the result, to remove all the contour inside, we simply used the opposite of distance function in the binary result image. After this, we obtain the target shape as illustrated by the

4.1. Fourier Descriptors Fourier descriptors are the famous shape descriptors used in very large application. they are obtained simply by applying Fourier transform on shape boundaries extracted by watersheds method described in the section above. To obtain Fourier descriptors, we must use an appropriate shape signature. Many shape signatures can be used as

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2

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ri = ([xi − xc ] + [yi − yc ] )

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This shape signature is related to the position of shape, therefore, it is invariant to shape translation. Before applying the Fourier transform, we have normalized all the shapes in database to a same number of points in order to accomplish the recognition task. In this step, the centroid center vector is normalized to N points (80 points in our case). Since that, the Fourier descriptors, denoted f d, are computed by applying discrete Fourier transform. Hence, we have chosen Fourier transform for their implementation simplicity and efficiency.

4.2. Retrieval experimentation To achieve a classification step, several techniques can be used to perform recognition task. The basic idea consists to implement the nearest-neighbor classifier to retrieve from unknown target (test database), the nearest known target in training database. This retrieval system is based on the template of Fourier descriptors. We note in this case that we can compress each template of Fourier descriptors by exploiting PCA (Principle Components Analysis). Then, template designs the compressed Fourier descriptors. Hence, we can compute the simple distance from unknown template and known templates stored in training database. Therefore, some distance functions can be used. We have used Pearson distance which performs a better results than Euclidean or Mahalanobis distance. It is given by : p (3) d(p, q) = 1 − C(p, q)2 Where C(p, q) is normalized correlation coefficient between the two Fourier descriptors (templates) given by : f T (p, q)h(pu , qu ) C(p, q) = p , p = 1, . . . P, q = 1, . . . , Q f T (p, q)h(pu , qu ) (4) Where f (p, q) is the vector of compressed Fourier descriptors of the pth target class and q th aspect angle in the training database. h(pu , qu ) denotes the compressed vector of Fourier descriptors of the unknown target class pu at unknown aspect qu . P is the number of unknown classes and Q is the number of images for each target class used in training database. Therefore, we can expect that two templates will have small d(p, q) values in (3)(or high C(p, q)). Then,

after computing the d(p, q) values and sorting them in ascending order, we select indexes with the training database whose d(p, q) values are within the first K templates. Finally, the desired class of unknown target is identified as p∗ if (5) d(p∗ , q ∗ ) ≤ dK (p, q) Where dK (p, q) designs the identity of the majority of K nearest-neighbor templates.

5 Results and discussion In simulation results, each target is represented by 162 ISAR images of 256 × 256 grayscale pixels. The initial database is divided for test and training database. From each ISAR image, we compute a shape template that contains the compressed Fourier descriptors. In the results bellow, the size of each template is 20 elements (normalized boundaries = 80 points, Fourier descriptors df = 40 and P CA(df ) = 20). In figure 8, we present the classification accuracies with varying the number of target in database. We start a classification experiments for 2 targets to 11 targets respectively F104, F117, Tornado, Harrier, A10, F14, F15, F16, Mig29, F18, and F4. C o r r e c t r e c o g n itio n r a te

shown in [15] and using the centroid distance outperforms others shape signatures in the shape based recognition. The centroid distance is defined as the distance of the boundaries points (xi , yi ), i = 1 . . . L, from the centroid point (xc , yc ) of the shape that can be computed as average of x and y coordinates respectively. Finally, we obtain a centroid distance Vector R = {r1 , ...rL } where :

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Figure 8. Classification results. The performance of algorithm is usually measured by the ratio of the number of the correct classification and that of the whole data set. For 11 targets, the best result is 41, 64% which obtained for all 162 × 11 = 1782 images. Hence, in training database we have 81 × 11 = 891 images and the remaining (81 × 11 = 891) images are used for testing. We can note that the classification accuracy decreases if the number of target increases (size of database increases). We must note that the classification performance based on the shape descriptors depends on the size of shape database. In this study, we have ignored the notion of superstructure of target and their locations. Although the shape representation gives an insufficient results on large database but it gives an important information of the unknown target before recognition. For intuitive interpretation, the shape representation give a good means for decision making to a human operator. In this way, we have proposed a Man Machine Interface (MMI) that assumes the processing from

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data acquisition to data retrieval. The figure 9 illustrate one example of a proposed MMI. So, the shape extracted from

approach is not only appropriate to aircraft target, but also for other kinds of targets.

References

Figure 9. An example of MMI. the ISAR-Image can be composed of several components superstructures and changes motions with angular motion of target. Hence, all information contained on the ISAR image must be integrated in the recognition scheme. However, Feature vectors based on target shape is insufficient if a large database is used. Several results which based on shape descriptors are given with small number of target [9, 10]. We have examined in this paper the efficient classification with a large database as given by the results above (see figure 8). Hence, we have exploited an other description of target based on polar image [13, 11] to build a polar signature. A retrieval scheme used in background the polar signature to achieve a recognition task. We have obtained the complete recognition close to (100%).

6. Conclusion The methods proposed in this paper have shown their adequacy for automatic target recognition. In order to propose an helpful informations to human operator, target shape is extracted by watersheds segmentation. Preliminary results are given. In this framework, some further processing and descriptors from a shape information are really necessary to achieve feature vectors in order to get a satisfactory rate of correct recognition. In this framework, our work deals with experimental data used to validate the classification scheme and the efficiency of the feature vectors. Then, in future works, the database will be completed with another targets from ships and other descriptors must be added. Other techniques of superresolution in ISAR images will be also experimented. The system retrieval based on polar signature and recognition scheme gives a satisfactory result. Our recognition

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