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Synthetic aperture radar (SAR) is an advanced imaging sensor that can image side-looking terrain. Until now, SAR schemes and imaging theories have been researched for stationary scenes. Unlike a stationary scene, due to unknown motion parameters, a ground moving target (GMT) is shifted and defocused in a background image, which causes difficulties in their use for further applications. This paper proposes a multiple pulse repetition frequency (PRF) airborne SAR scheme for background-free GMT imaging. In the proposed scheme, pulses are transmitted by a nonuniform pulse repetition time, and echoes are divided into groups with different PRFs. For spectra with different PRFs, due to their diverse respective spectra extending periods, periodical extending GMT spectra will not appear at the same location except for the original spectra. A theorem is proposed to prove that the spectra can be extracted intact, undistorted, and unpolluted. In light of the divergence of the GMT spectra location from that of the clutter, filter banks are designed to locate, extract, and reconstruct the GMT spectra out of the mixed spectra that contain the GMT and clutter. For GMT SAR imaging, a moving target is regarded as a steady target under an equivalent geometry with a new equivalent squint angle and a new SAR platform velocity. The SAR image is obtained with the range-Doppler algorithm with equivalent geometry parameters that are estimated from the Doppler parameters. Experimental results obtained from using extensive numerical simulated data validate our proposed approach. [ABSTRACT FROM AUTHOR]

Motivated by classical coincidence imaging which has been realized in optical systems, an instantaneous microwave-radar imaging technique is proposed to obtain focused high-resolution images of targets without motion limitation. Such a radar coincidence imaging method resolves target scatterers based on measuring the independent waveforms of their echoes, which is quite different from conventional radar imaging techniques where target images are derived depending on time-delay and Doppler analysis. Due to the peculiar features of coincidence imaging, there are two potential advantages of the proposed imaging method over the conventional ones: 1) shortening the imaging time to even a pulse width without resolution deterioration so as to improve the performance of processing noncooperative targets and 2) simplifying the receiver complexity, resulting in a lower cost and platform flexibility in application. The basic principle of radar coincidence imaging is to employ the time-space independent detecting signals, which are produced by a multitransmitter configuration, to make scatterers located at different positions reflect independent waveforms from each other, and then to derive the target image based on the prior knowledge of this detecting signal spatial distribution. By constructing the mathematic model, the necessary conditions of the transmitting waveforms are analyzed for achieving radar coincidence imaging. A parameterized image-reconstruction algorithm is introduced to obtain high resolution for microwave radar systems. The effectiveness of this proposed imaging method is demonstrated via a set of simulations. Furthermore, the impacts of modeling error, noise, and waveform independence on the imaging performance are discussed in the experiments. [ABSTRACT FROM PUBLISHER]

In this paper, we present a quantitative comparison of L- and C-band airborne synthetic aperture radar imagery acquired at like- and cross-polarizations over deformed sea ice under winter conditions. The parameters characterizing the deformation state of the ice are determined at both radar bands and at different polarizations. The separation of deformed and level ice is based on a target detection technique. The threshold is set such that image pixels with intensities equal to or larger than the highest 2% of the level-ice intensity distribution are classified as deformed ice, independent of the radar configuration and ice conditions. Optical imagery of sufficient quality for comparison is available only in a very few cases. To characterize the deformation state, the areal fraction of deformation features and the average distance between these features are evaluated. The values obtained for both parameters are very sensitive to the radar frequency. Aeral fractions are larger, and average distances are smaller at L-band than at C-band because of the much higher intensity contrast between the deformed and level ice at L-band. The differences between polarizations at one radar band are smaller but not always negligible. In comparison to optical images, it was observed that deformed-ice areas can be distinguished from level ice over their whole length and extension at L-band, whereas at C-band, often, only prominent parts are visible. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of apodization on the statistical properties of synthetic aperture radar (SAR) images and its impact on the capability of extracting information from homogeneous regions of apodized SAR images. The statistical model for the pixel complex amplitude of the apodized image is derived in terms of both probability density function and statistical moments. Knowledge of the statistical properties is then used to develop appropriate schemes for parameter estimation and supervised classification of homogeneous regions with different radar cross sections in apodized SAR images. The performance analysis shows that the new techniques (properly derived for the apodized case) provide information extraction capabilities only slightly worse than those provided by the conventional techniques applied to the nonapodized case. This allows us to conclude that the use of nonlinear apodization yields sidelobe level reduction and main lobe resolution preservation that can be traded with the small losses above. A full characterization of the estimation and classification performance of these new techniques shows that non-linear apodization globally introduces a performance degradation comparable to a reduction of the number of looks of a factor of 1.455 for a homogeneous region. [ABSTRACT FROM AUTHOR]

The combination of frequency-modulated continuous-wave (FMCW) technology and synthetic aperture radar (SAR) techniques leads to lightweight cost-effective imaging sensors of high resolution. One limiting factor to the use of FMCW sensors is the well-known presence of nonlinearities in the transmitted signal. This results in contrast- and range-resolution degradation, particularly when the system is intended for high-resolution long-range applications, as it is the case for SAR. This paper presents a novel processing solution, which solves the nonlinearity problem for the whole range profile. Additionally, the conventional stop-and-go approximation used in pulse-radar algorithms is not valid in FMCW SAR applications under certain circumstances. Therefore, the motion within the sweep needs to be taken into account. Analytical development of the FMCW SAR signal model, starting from the deramped signal and without using the stop-and-go approximation, is presented in this paper. The model is then applied to stripmap, spotlight, and single-transmitter/multiple-receiver digital-beamforming SAR operational mode. The proposed algorithms are verified by processing real FMCW SAR data collected with the demonstrator system built at the Delft University of Technology. [ABSTRACT FROM AUTHOR]

A synthetic aperture radar (SAR) often is constrained to transmit only one polarization. Within this constraint, two aggressive measurement objectives are 1) full characterization and exploitation of the backscattered field, and 2) invariance to geometrical orientations of features in the scene. Full characterization implies coherent dual-polarization to support the four Stokes parameters. These are rotationally invariant with respect backscatterer orientation if and only if the transmission is circularly polarized. Given that the data products are the Stokes parameters, the receivers can use any orthogonal polarization basis. A SAR in hybrid-polarity architecture (CL-pol) transmits circular polarization and receives two orthogonal mutually coherent linear polarizations, which is one manifestation of compact polarimetry. The resulting radar is relatively simple to implement, and has unique self-calibration features and low susceptibility to noise and cross-channel errors. It is the architecture of choice for two lunar radars scheduled for launch in 2008. Data from a CL-pol SAR yield to decomposition strategies such as the m—δmethod introduced in this paper. [ABSTRACT FROM AUTHOR]

We describe in detail the theoretical and practical implementation aspects of a simulation for marine radars which can, in particular, be used in multistatic configurations. Since the simulator is intended to deliver pseudoraw signals, it can be used later as a tool to benchmark and improve postprocessing algorithms such as bistatic synthetic aperture radar focusing algorithms and ship wake detection algorithms. The work is divided into two parts. This paper reviews and recalls theoretical prerequisites necessary in implementing such a simulator. Included are the full derivation of the bistatic radar equation from the transmitter to the receiver, accounting also for the transmit-receive time, a description of the sea state phenomenology, a review of the theory of electromagnetic scattering from the sea surface, and the presentation and validation of the method used in the simulation. A companion paper discusses the practical implementation aspects of the simulator as well as an analysis of our results. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of supervised classification of remote sensing images in the presence of incomplete (nonexhaustive) training sets. The problem is analyzed according to two different perspectives: 1) description and recognition of a specific land-cover class by using single-class classifiers and 2) solution of multiclass problems with single-class classification techniques. In this framework, we analyze different one-class classifiers and introduce in the remote sensing community the support vector domain description method (SVDD). The SVDD is a kernel-based method that exhibits intrinsic regularization ability and robustness versus low numbers of high-dimensional samples. The SVDD technique is compared with other standard single-class methods both in problems focused on the recognition of a single specific land-cover class and in multiclass problems. For the latter, we properly define an easily scalable multiclass architecture capable to deal with incomplete training data. Experimental results, obtained on different kinds of data (synthetic, hyperspectral, and multisensor images), point out the effectiveness of the SVDD technique and provide important indications for driving the choice of the classification technique and architecture in the presence of incomplete training data. [ABSTRACT FROM AUTHOR]

Linear stepped frequency radar is used in wide-band radar applications, such as airborne synthetic aperture radar (SAR), turntable inverse SAR, and ground penetration radar. The frequency is stepped linearly with a constant frequency change, and range cells are formed by fast Fourier transform processing. The covered bandwidth defines the range resolution, and the length of the frequency step restricts the nonambiguous range interval. A random choice of the transmitted frequencies suppresses the range ambiguity, improves covert detection, and reduces the signal interference between adjacent sensors. As a result of the random modulation, however, a noise component is added to the range/Doppler sidelobes. In this paper, relationships of random step frequency radar are compared with frequency-modulated continuous wave noise radar and the statistical characteristics of the ambiguity function and the sidelobe noise floor are analyzed. Algorithms are investigated, which reduce the sidelobes and the noise-floor contribution from strong dominating reflectors in the scene. Theoretical predictions are compared with Monte Carlo simulations and experimental data. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of modeling high-resolution synthetic aperture radar clutter data from different vegetated areas. We analyzed moving and stationary target recognition (MSTAR) data sets focusing on histograms, moments, and covariance of clutter amplitude, texture, and speckle. The most celebrated statistical models are tested on real data of grass field or wood and trees to validate the goodness of fit of the compound Gaussian model in different scenarios. The results demonstrate that for grass fields, the compound Gaussian model provides a good data fitting. This is not the case for woods images where the speckle is not more Gaussian distributed. Covariance analysis and concluding remarks complete this paper. [ABSTRACT FROM AUTHOR]

Full-resolution 3-D ground-penetrating radar (GPR) imaging of the near surface should be simple and efficient. Geoscientists, archeologists, and engineers need a tool capable of generating interpretable subsurface views at centimeter-to-meter resolution of field sites ranging from smooth parking lots to rugged terrain. The authors have integrated novel rotary laser positioning technology with GPR into such a 3-D imaging system. The laser positioning enables acquisition of centimeter accurate x, y, and z coordinates from multiple small detectors attached to moving GPR antennas. Positions streaming with 20 updates/s from each detector are fused in real time with the GPR data. The authors developed software for automated data acquisition and real time 3-D GPR data quality control on slices at selected depths. Industry standard (SEGY) format data cubes and animations are generated within an hour after the last trace has been acquired. Such instant 3-D GPR can be used as an on-site imaging tool supporting field work, hypothesis testing, and optimized excavation and sample collection in the exploration of the static and dynamic nature of the shallow subsurface. [ABSTRACT FROM AUTHOR]

The fractional chirp scaling algorithm (FrCSA) is based on the use of the fractional Fourier transform (FrFT) within the chirp scaling algorithm (CSA). In this paper, a closed-form expression for the azimuth FrFT of the FrCSA is mathematically derived and analyzed from the high-resolution synthetic aperture radar imaging point of view. The azimuth-FrFT expression of the FrCSA is compared to that of the classical fast Fourier transform (FFT)-based CSA. As the FFT is a special case of the generalized FrFT, the derived expression is found to be in total agreement with that of the FFT-based CSA when the transformation order is equal to unity; that is the angle of rotation is equal to π/2. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to change detection in multitemporal synthetic aperture radar (SAR) images. The proposed approach exploits a wavelet-based multiscale decomposition of the log-ratio image (obtained by a comparison of the original multitemporal data) aimed at achieving different scales (levels) of representation of the change signal. Each scale is characterized by a different tradeoff between speckle reduction and preservation of geometrical details. For each pixel, a subset of reliable scales is identified on the basis of a local statistic measure applied to scale-dependent log-ratio images. The final change- detection result is obtained according to an adaptive scale-driven fusion algorithm. Experimental results obtained on multitemporal SAR images acquired by the ERS-1 satellite confirm the effective- ness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper presents a system to integrate digital elevation model (DEM) enhancement and Earth Observation (EO) image analysis for realistic three-dimensional (3-D) rendering applications. There is an increasing interest in interferometric synthetic aperture radar (InSAR) data, principally due to the availability of the nearly global Shuttle Radar Topography Mission coverage. To remove artifacts and noise from an InSAR DEM, a nonstationary Bayesian filtering is applied that preserves structural information. Land-cover or man-made structures are easily recognized in an optical image. The corresponding geometry encapsulated in the DEM differs from our implicit perception and generally leads to unrealistic 3-D rendering. To improve this, DEM regularization is achieved using only the visualization dataset (optical image and DEM). It consists of extracting relevant information from the optical image and integrate them in the filtered DEM. To gather image information, an object-based description of large optical EO images is obtained in two stages 1) an image is segmented to create a partition of regions and 2) a novel dynamical algorithm is proposed to extract the regions and encode them in a tree structure. Regions are modeled by objects primitives stored in a database. Spatial relationships between regions are reflected by the presented tree of regions. Using the object-based description generation, structures to be integrated into the DEM are interactively selected and classified among a set of user-thematic. Each thematic is associated with a corresponding elevation modeling and enables to estimate the region's 3-D structure. The proposed object line processing provides more realistic 3-D visualizations. [ABSTRACT FROM AUTHOR]

In this work, multitemporal synthetic aperture radar (SAR) data in conjunction with an electromagnetic (EM) model and a vegetation growth model were used to monitor and explain burn-regrowth events of junco vegetation in a wetland environment. The data used were from Radarsat-1, ENVISAT Advanced Synthetic Aperture Radar (ASAR), and European Remote Sensing 2 (ERS-2) temporal series. The EM model is based on radiative transfer theory and describes junco vegetation as a set of vertical dielectric cylinders on a flat flooded surface. It was used, together with the vegetation growth model, to predict the temporal evolution of the radar response during a burn-regrowth event. This simulation was compared with the ERS-2 vertical (VV) data. It was observed a "bell-shaped" temporal trend that was confirmed by the simulated data with a mean error of 2.5 dB. Additionally, in view of current and future ENVISAT ASAR Alternating Polarization Mode Precision data, the horizontal (HH) SAR temporal response was also simulated giving as a result strong differences between simulated HH and VV temporal trends. These differences are in good agreement with the ones observed between Radarsat-1 HH and ERS-2 VV SAR data acquired at close dates and also with the same differences observed between HH and VV ENVISAT ASAR data. Electromagnetic modeling results provide a sound theoretical interpretation of these observations. [ABSTRACT FROM AUTHOR]

Developments in the field of image understanding in remote sensing over the past four decades are reviewed, with an emphasis, initially, on the contributions of David Landgrebe and his colleagues at the Laboratory for Applications of Remote Sensing, Purdue University. The differences in approach required for multispectral, hyperspectral and radar image data are emphasised, culminating with a commentary on methods commonly adopted for multisource image analysis. The treatment concludes by examining the requirements of an operational multisource thematic mapping process, in which it is suggested that the most practical approach is to analyze each data type separately, by techniques optimized to that data's characteristics, and then to fuse at the label level. [ABSTRACT FROM AUTHOR]

A novel system for the classification of multitemporal synthetic aperture radar (SAR) images is presented. It has been developed by integrating an analysis of the multitemporal SAR signal physics with a pattern recognition approach. The system is made up of a feature-extraction module and a neural-network classifier, as well as a set of standard preprocessing procedures. The feature-extraction module derives a set of features from a series of multitemporal SAR images. These features are based on the concepts of long-term coherence and backscattering temporal variability and have been defined according to an analysis of the multitemporal SAR signal behavior in the presence of different land-cover classes. The neural-network classifier (which is based on a radial basis function neural architecture) properly exploits the multitemporal features for producing accurate land-cover maps. Thanks to the effectiveness of the extracted features, the number of measures that can be provided as input to the classifier is significantly smaller than the number of available multitemporal images. This reduces the complexity of the neural architecture (and consequently increases the generalization capabilities of the classifier) and relaxes the requirements relating to the number of training patterns to be used for classifier learning. Experimental results (obtained on a multitemporal series of European Remote Sensing 1 satellite SAR images) confirm the effectiveness of the proposed system, which exhibits both high classification accuracy and good stability versus parameter settings. These results also point out that properly integrating a pattern recognition procedure (based on machine learning) with an accurate feature extraction phase (based on the SAR sensor physics understanding) represents an effective approach to SAR data analysis. [ABSTRACT FROM AUTHOR]

A hyperspectral imaging sensor can reveal and uncover targets with very narrow diagnostic wavelengths. However, it comes at a price that it can also extract many unknown signal sources such as background and natural signatures as well as unwanted man-made objects, which cannot be identified visually or a priori. These unknown signal sources can be referred to as interferers, which generally play a more dominant role than noise in hyperspectral image analysis. Separating such interferers from signals and annihilating them subsequently prior to detection may be a more realistic approach. In many applications, the signals of interest can be further divided into desired signals for which we want to extract and undesired signals for which we want to eliminate to enhance signal detectability. This paper presents a signal-decomposed and interference-annihilated (SDIA) approach in applications of hyperspectral target detection. It treats interferers and undesired signals as separate signal sources that can be eliminated prior to target detection. In doing so, a signal-decomposed interference/noise (SDIN) model is suggested in this paper. With the proposed SDIN model, the orthogonal subspace projection-based model and the signal/background/noise model can be included as its special cases. As shown in the experiments, the SDIN model-based SDIA approach generally can improve the performance of the commonly used generalized-likelihood ratio test and constrained energy minimization approach on target detection and classification. [ABSTRACT FROM AUTHOR]