A retrieval system from inverse synthetic aperture radar images: Application to radar target recognition
Introduction
Automatic Target Recognition (ATR) systems are an important part of modern military strategy. Correct recognition of military targets such as aircraft, naval ships, and missiles is indispensable to be able to attack the target accurately. Nowadays, radar and radar signatures are widely used for recognition purposes. However, several kinds of radar signature can be applied to acquire information about the target characteristics. For example, one can use modulation in the radar echoes produced by the rotating parts of a target such as propellers, rotors, or the compressor blades of a jet aircraft. Another option would be the extraction of geometrical parameters or characteristics of a target that can be obtained from a radar image. These characteristics can be obtained in one, two or n radar reflections in range. This single radar reflection is called a radar range profile. Under certain circumstances, the information about the target’s motion perpendicular to the line-of-sight can be extracted from a bi-dimensional image. That is why several techniques have been investigated in this context; several pieces of research have particularly focused on ISAR techniques and Automatic Target Recognition (ATR). These investigations typically use algorithms such as the Inverse Fast Fourier Transform (IFFT) and super-resolution radar imaging (for example MUSIC-2D and ESPRIT-2D algorithms). Several advances concerning ISAR image classification can be found in [16], [20], [23], [25], [29].
In the field of feature extraction, the main problem is to obtain feature vectors that have the invariance property relative to the scale and rotation of the ISAR image. For example in ship recognition using ISAR images, some feature vectors use a heuristic approach to estimate motion and geometric characteristics such as location of breaks in the superstructure profile or the number of major uprights [20], [2]. Such feature vectors are thus hardly applicable to aircraft recognition. In order to resolve this difficulty and to change the target scale, a number of theories have been proposed [2], [8]. A semi-automatic/automatic recognition scheme is proposed in this paper as an alternative in order to overcome operator limitations resulting from target recognition difficulties and to provide a useful tool to aid decision making.
Several feature vectors are desirable for interpretation and analysis in order to recognize different targets and therefore help a human operator to perform target recognition tasks correctly. The main strength of target recognition is that it uses target shape extracted from 2D-representations (ISAR images). We draw upon 2D ISAR images specifically because they furnish particularly detailed information about the geometry of the target (aircraft, naval ship, missile, etc.). The methodology used to design the complete processing chain from the acquisition step to the recognition (classification) step is based on the artificial intelligence approach. This process is known as Knowledge Discovery from Data (KDD) [14] which we have adapted to the radar target recognition system [29].
In this study, we put forward two main propositions. The first proposition is to provide the human operator with better information about target shape. Section 5 is devoted to the presentation of details and results concerning this proposition.
The second proposition presents other features that can be exploited to achieve satisfactory target recognition. In this proposal, processing time is reduced by using two approaches. The first approach is based on the processing of partial polar signatures in several steps as illustrated in Section 8. The second method is the use of non-supervised classification in order to optimize the training database and thus reduce matching time as presented in Section 9.
Section snippets
Automatic target recognition steps
Generally, four steps are involved in Automatic Target Recognition (ATR). They are data acquisition, data pre-processing, data representation and data classification for decision making. However, when the KDD methodology is applied to semi-automatic target recognition, another terminology is used for these steps: data acquisition, data preparation, Data Mining (DM), and the evaluation and decision making step. In the first step, data (signals) are collected from an anechoic chamber which is
Simulation data
A radar data acquisition system was studied using results obtained during tests conducted in ENSIETA’s anechoic chamber. This chamber facilitates the taking of real measurements and allows good control of the target-radar configuration. Thus, the human operator’s interpretation and control are made easier. The experimental setup is shown in Fig. 3.
To construct our simulation ISAR image database, we used eleven reduced-scale (1/48) aircraft models: F-104, F-117, Tornado, Harrier, A-10, F-14,
Shape extractor
The aircraft shape is one of the best features because it can give the human operator initial information on the unknown target. For this purpose, we chose to extract the shape from the ISAR image using the watershed method. Therefore, we used several processing steps.
In the first processing step, we segmented the ISAR image. In this segmentation process, the target response is extracted from the background which can include noise and clutter. Furthermore, in the anechoic chamber, we assumed
Shape descriptors
For shape representation, 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 the attribute values of these targets. The shape descriptors can be used in this way. In this section we describe Fourier descriptors and our classification scheme. Other
Results and discussion
In the simulation results, each target was represented by 162 ISAR images of 256 × 256 grayscale pixels. The initial database was divided into test and training databases. From each ISAR image, we computed a shape template that contained the compressed Fourier descriptors. In the results below, the size of each template was 20 elements (normalized boundaries = 80 points, Fourier descriptors df = 40 and PCA(df) = 20). It is important to take into account the size of training database. Some techniques
Polar descriptors
In this step, our aim was to construct the Polar Image (PI) using the Polar Mapping (PM) principle [16]. In the same principle, Log-Polar Mapping (LPM) can be used in this framework, but PM has a finer grid than LPM [16] because of its sampling interval in r-direction and the same sampling interval irrespective of the radius (see Fig. 2). The PM is shown in Fig. 21.
If we applied polar mapping to the initial image I(xi, yj) as defined in Section 5, we obtained an image called polar image Ip(rm, αn
Retrieval system for recognition
The classification scheme is based on a hierarchical architecture using three classifiers C1, C2 and C3. Each classifier is based on one part of the signature except C3 which uses V3 and V4 vectors as illustrated in Fig. 24.
After computing the normalized feature vectors Ir, Iα, and dfα from the polar image Ir,α, we constructed the training and test databases. In this step (Step2) it was possible to use some non-supervised classification methods. The authors in [16] uniformly sampled each class
Experiment results
To reduce the computation time, we used η < λ < 100%. Of course, the determination of λ and η values is a critical issue to guarantee a robust performance and to reduce time matching. However there is no systematic way of choosing them. Hence, we chose small values as desirable values. The idea was to reduce the size of the training database by using the Ward method in order to reduce the computation times and to improve classification accuracy.
In all the simulation results given below, we used the
Support vector machines
The Support Vector Machine (SVM) classification is based firstly on the training database to produce a model which is able to predict a label of the unknown images stored in the test database. This step is commonly called SVM learning. Given a labeled training vectors set {xi, yi}i=1,…,l with xi ∈ Rn the template of Ir and dfα vectors and yi ∈ {1, −1}l, support vector machines [9] require the solution of the following (primal) optimization problem:s.t.
Conclusion
The methods proposed in this paper have shown their adequacy for semi-automatic and/or automatic target recognition. In order to provide helpful information to a human operator, the target shape is extracted by watershed segmentation and the preliminary results are given. In this framework, some further processing and descriptors from shape information are really necessary to achieve feature vectors in order to obtain a satisfactory rate of correct recognition.
The retrieval system based on
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