Two-beam-coupling correlator for synthetic aperture radar image recognition with power-law scattering centers preenhancement

Synthetic radar image recognition is an area of interest for military applications including automatic target recognition, air traffic control, and remote sensing. Here a dynamic range compression two-beam-coupling joint transform correlator for detecting synthetic aperture radar targets is utilized. The joint input image consists of a prepower-law, enhanced scattering center of the input image and a linearly synthesized power-law-enhanced scattering center template. Enhancing the scattering center of both the synthetic template and the input image furnishes the conditions for achieving dynamic range compression correlation in two-beam coupling. Dynamic range compression (a) enhances the signal-to-noise ratio, (b) enhances the high frequencies relative to low frequencies, and (c) converts the noise to high frequency components. This improves the correlation-peak intensity to the mean of the surrounding noise significantly. Dynamic range compression correlation has already been demonstrated to outperform many optimal correlation filters in detecting signals in severe noise environments. The performance is evaluated via established metrics such as peak-to-correlation energy, Horner efficiency, and correlation-peak intensity. The results showed significant improvement as the power increased.     

Estimating building dimensions from synthetic aperture radar image sequences

The problem of automatically extracting building dimensions from synthetic aperture radar (SAR) image sequences of urban scenes is considered. An algorithm based on the delineation of shadows using active contours constrained by a simple wire-frame building model has been developed and demonstrated using SAR imagery of a village on Salisbury plain. The core of the algorithm is a novel technique for target delineation involving multiple active contours evolving simultaneously. In particular, a technique referred to as multiple hypothesis delineation, in which contours can be in several states simultaneously, is developed and shown to lead to considerable improvement in convergence time and delineation accuracy when used to delineate multiple targets in close proximity. The technique is applied to the automatic estimation of building dimensions by delineation of shadows in a sequence of SAR images of an urban scene. The estimation of building dimensions is automatic; user interaction is limited identifying a building of interest and a region of background clutter close to the building. Results are presented for six different buildings, in each case two SAR images were used in the estimation process separated in illumination angle by either 28deg or 90deg. The estimates of building dimensions are compared with the actual building dimensions obtained from architectural drawings. The algorithm was found to perform robustly and provide reasonably accurate estimates of the building dimensions, typically within ~10% of the true values.     

Optical synthetic aperture radar

A method is proposed for increasing the resolution of an object and overcoming the diffraction limit of an optical system installed on top of a moving imaging system, such as an airborne platform or satellite. The resolution improvement is obtained via a two-step process. First, three low resolution differently defocused images are captured and the optical phase is retrieved using an improved iterative Gershberg–Saxton based algorithm. The phase retrieval allows numerical back propagation of the field to the aperture plane. Second, the imaging system is shifted and the first step is repeated. The obtained optical fields at the aperture plane are combined and a synthetically increased lens aperture is generated along the direction of movement, yielding higher imaging resolution. The method resembles a well-known approach from the microwave regime called the synthetic aperture radar in which the antenna size is synthetically increased along the platform propagation direction. The proposed method is demonstrated via Matlab simulation as well as through laboratory experiment.     

Automatic algorithm for inverse synthetic aperture radar images recognition and classification

The authors propose an algorithm for automatic aircraft categories that is models classification from inverse synthetic aperture radar (ISAR) images that use pulse reflection shape and Doppler shifts of parts of aircraft that are in any maneuver that introduces rotation to the target. The authors artificially generated five different categories of ISAR aircraft using computer simulations and tested these simulated ISAR aircraft images of the airplanes defined by size and shape that are flying in a prescribed holding pattern. The authors investigate in what parts of the holding pattern the ISAR reflections provide information that makes it possible to identify to which of the five categories an aircraft in the holding pattern belongs. The obtained results show that it is possible in most parts of the holding pattern to successfully classify the various aircraft targets.     

Azimuth-invariant bistatic multichannel synthetic aperture radar for moving target detection and location

Ground moving target indication (GMTI) is one of the most important applications of the bistatic synthetic aperture radar (SAR) system as well as the monostatic system. An algorithm for moving target detection and location is presented with an azimuth-invariant bistatic multichannel SAR, which consists of one transmitter (channel) and multireceivers (multichannel). The algorithm is based on the discussion of the particularities of the bistatic SAR configuration including coherence improvement and clutter characteristics. Then, the corresponding compensating methods including two-dimensional range-azimuth prefiltering and bistatic differential range correction are proposed to solve these particularities. It is shown that using the compensating methods, the stationary clutter can be suppressed and the moving parameters of ground targets can be estimated accurately. Finally, simulation results demonstrate the effectiveness of the proposed algorithm.     

Inverse synthetic aperture radar imaging of ship target with complex motion

High-resolution inverse synthetic aperture radar (ISAR) imaging and recognition of ship target is very important for many applications. Although the principle of ISAR imaging of ship target on the sea is the same as that of flying target in the sky, the former usually has more complex motion (fluctuation with the oceanic waves) than the latter, which makes the motion compensation very difficult. However, the change in phase chirp rate caused by the complex motion of ships will deteriorate the azimuth focusing quality. In this paper, we first model the complex motion of ship target with cubic phase terms (parameterised on chirp rate and its change rate), then a new ISAR imaging method, referred to as TC-DechirpClean, is proposed, which estimates the chirp rate and the change rate of chirp rate of all scatters in the time? chirp distribution plane. Both numerical and experimental results are provided to demonstrate the performance of the proposed method.     

Single-element diffractive optical system for real-time processing of synthetic aperture radar data

I present an optical system for the polar formatting of data in a spotlight-mode synthetic aperture radar. This system is implemented with only one diffractive optical element (DOE). Previously such a DOE could not be produced because the phase of the required transmission function of the DOE does not obey the continuity condition, which is a prerequisite for the conventional implementation of such optical transforms. Here I show how a DOE can be produced to perform the complete polar-formatting transform by incorporating branch-point phase singularities in the transmission function of the DOE. The computation of the transmission function is shown, and numerically computed diffraction patterns obtained from this DOE are also shown.     

Direct-Fourier reconstruction in tomography and synthetic aperture radar

We investigate the use of direct-Fourier (DF) image reconstruction in computed tomography and synthetic aperture radar (SAR). One of our aims is to determine why the convolution-backprojection (CBP) method is favored over DF methods in tomography, while DF methods are virtually always used in SAR. We show that the CBP algorithm is equivalent to DF reconstruction using a Jacobian-weighted two-dimensional periodic sinc-kernel interpolator. This interpolation is not optimal in any sense, which suggests that DF algorithms using optimal interpolators may surpass CBP in image quality. We consider use of two types of DF interpolation: a windowed sinc kernel, and the least-squares optimal Yen interpolator. Simulations show that reconstructions using the Yen interpolator do not possess the expected visual quality, because of regularization needed to preserve numerical stability. Next, we show that with a concentric-squares sampling scheme, DF interpolation can be performed accurately and efficiently, producing imagery that is superior to that obtainable by other algorithms. In the case of SAR, we show that the DF method performs very well with interpolators of low complexity. We also study DF reconstruction in SAR for trapezoidal grids. We conclude that the success of the DF method in SAR imaging is due to the nearly Cartesian shape of the sampling grid. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 1–13, 1998     

Target-adaptive polarimetric synthetic aperture radar target discrimination using maximum average correlation height filters

We report the development of a technique for adaptive selection of polarization ellipse tilt and ellipticity angles such that the target separation from clutter is maximized. From the radar scattering matrix [S] and its complex components, in phase and quadrature phase, the elements of the Mueller matrix are obtained. Then, by means of polarization synthesis, the radar cross section of the radar scatters are obtained at different transmitting and receiving polarization states. By designing a maximum average correlation height filter, we derive a target versus clutter distance measure as a function of four transmit and receive polarization state angles. The results of applying this method on real synthetic aperture radar imagery indicate a set of four transmit and receive angles that lead to maximum target versus clutter discrimination. These optimum angles are different for different targets. Hence, by adaptive control of the state of polarization of polarimetric radar, one can noticeably improve the discrimination of targets from clutter.     

Inverse synthetic aperture radar imaging of satellites

We present a scheme for radar imaging of satellites by the inverse synthetic aperture radar technique. We include some general principles in image formation when dealing with microwave-scattering experiments, such as radar observation of a satellite, so that the article is not confined to the radar community. We propose an original motion compensation algorithm for image autofocusing: This one-stage algorithm is faster than usual two-stage algorithms and turns to be quite accurate, which can be measured by quality criteria and visual inspection of reconstructed images. We show its application to imaging of the Russian space station Mir with two different radar systems. © 1998 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 9, 24–28, 1998     

远场合成孔径计算光学成像技术：文献综述与最新进展

传统光学成像实质上是目标场景的光强信号在空间维度上的直接均匀采样记录与再现的过程。因此,其成像分辨率与信息量不可避免地受到光学衍射极限、成像系统空间带宽积等若干物理条件制约。如何突破这些物理制约,获得更高分辨率、更宽广的图像信息,一直是该领域的永恒课题。计算光学成像通过前端光学调控与后端信号处理相结合,为突破成像系统的衍射极限限制,实现超分辨成像提供了新思路。本文综述了基于计算光学合成孔径成像实现成像分辨率的提升以及空间带宽积拓展的相关研究工作,主要包括基于相干主动合成孔径成像与非相干被动合成孔径成像的基础理论及关键技术。本文进一步揭示了当前“非相干、无源被动、超衍射极限”成像的迫切需求及其现阶段存在的瓶颈问题,并展望了今后的研究方向以及解决这些问题可能的技术途径。

     

Interpolated spatially variant apodization in synthetic aperture radar imagery

The original formulation of spatially variant apodization for complex synthetic aperture radar imagery concentrated on integer-oversampled data. Noninteger-oversampled data presented previously [IEEE Trans. Aerosp. Electron. Syst. 31, 267 (1995)] suggested use of different weightings in the algorithm. An alternative noninteger-oversampled approach that employs the same apodization concept but uses local spatial interpolation is presented. With this approach the combined image formation, apodization, and detection of 1.3x-versus-2.0x oversampled data can be performed in half the time without loss of image quality.     

Adaptive-neighborhood speckle removal in multitemporal synthetic aperture radar images

We present a new method for multitemporal synthetic aperture radar image filtering using three-dimensional (3D) adaptive neighborhoods. The method takes both spatial and temporal information into account to derive the speckle-free value of a pixel. For each pixel individually, a 3D adaptive neighborhood is determined that contains only pixels belonging to the same distribution as the current pixel. Then statistics computed inside the established neighborhood are used to derive the filter output. It is shown that the method provides good results by drastically reducing speckle over homogeneous areas while retaining edges and thin structures. The performances of the proposed method are compared in terms of subjective and objective measures with those given by several classical speckle-filtering methods.     

Modelling and enhancement of interrupted synthetic aperture radar imagery

A synthetic aperture radar system can be made compatible with other interleaved modes if the impact of the interruptions in operation necessary to accommodate these can be ameliorated. The ways in which this might be done are discussed. Effects of regular and random interruptions are characterised by simulation and analysis. Two methods of image enhancement, based on interpolation and apodisation, are then discussed; the latter appears to hold more promise of practical usefulness.     

Feasibility study of synthetic aperture infrared laser radar techniques for imaging of static and moving objects

Techniques for two types of 10-mum band synthetic aperture infrared laser radar using a hypothetical reference point target (RPT) are presented. One is for imaging static objects with a single two-dimensional scanning aperture. Through the simple manipulation of a reference wave phase, a desired image can be obtained merely by the two-dimensional Fourier transformation of the correlator output between the intermediate frequency signals of the reference and object waves. The other, with a one-dimensional aperture array, is for moving objects that pass across the array direction without attitude change. We performed imaging by using a two-dimensional RPT correlation method. We demonstrate the capability of these methods for imaging and evaluate the necessary conditions for signal-to-noise ratio and random phase errors in signal reception through numerical simulations in terms of feasibility.     

Theory and simulation of ionospheric effects on synthetic aperture radar

Space-based synthetic aperture radar (SAR) necessarily involves imaging through the ionosphere. At low frequencies (VHF, UHF and L-band) the ionosphere will degrade the SAR image. Previous work has shown that the amount of image degradation strongly depends on the integrated strength of ionospheric turbulence, C _k L. The focusing, sidelobes and integrated sidelobe ratio all depend on C _k L in a manner that can be directly predicted by a simple analytic theory, which is reviewed and extended to cover any synthetic aperture length. Simulations of the ionosphere, using a thin phase screen parabolic equation approach, are performed under different ionospheric conditions for a number of possible SAR systems and the results compared with the analytic theory. It is concluded that, provided that the scattering is weak, the theory represents a good predictor of SAR performance, even at UHF. The known statistics of C _k L can therefore be used to predict the performance of any trans-ionospheric SAR without performing a simulation.     

Integration of synthetic aperture radar image segmentation method using Markov random field on region adjacency graph

A novel approach to obtain precise segmentation of synthetic aperture radar (SAR) images using Markov random field model on region adjacency graph (MRF-RAG) is presented. First, to form a RAG, the watershed algorithm is employed to obtain an initially over-segmented image. Then, a novel MRF is defined over the RAG instead of pixels so that the erroneous segmentation caused by speckle in SAR images can be avoided and the number of configurations for the combinatorial optimisation can be reduced. Finally, a modification method based on Gibbs sampler is proposed to correct edge errors, brought by the over-segmented algorithm, in the segmentations obtained by MRF-RAG. The experimental results both on simulated and real SAR images show that the proposed method can reduce the computational complexity greatly as well as increase the segmentation precision.     

Raw signal processing of stripmap bistatic synthetic aperture radar

Under the translational invariant (TI) configuration of stripmap imaging bistatic synthetic aperture radar (BISAR), the frequency domain expression of signal model is developed. Two forms, with an approximation of extracting the range dependence and an accurate iterative solution, as well as their respective focusing methods, that is the approximated solution method and the iterative solution method are presented here. The range-Doppler method is extended to the TI configuration of BISAR. Imaging simulation of the point targets and comparison with the bistatic backprojection method shows the feasibility of these methods as well as their merits and demerits.