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We propose a new adaptive noise reduction method for interferometric synthetic aperture radar (InSAR) complex-amplitude images. In the proposed method, we detect residues (singular points) in the phase image as well as their neighbors at first. Normal areas that contain no residue are used for the estimation of correct pixel values at the marked residues according to 5th order non-causal complex-valued Markov random field (CMRF) model. The process is performed block-wise with the assumption of a locally stationary condition of statistics. Using a CMRF lattice complex-valued neural-network, the error energy defined as the squared norm of distance between signal and estimated values is minimized by LMS steepest descent algorithm. Eventually, the number of residues is decreased. An application is also presented. An InSAR image around Mt. Fuji is processed by the proposed technique and then phase-unwrapped by the branch-cut method. It is found that after the application of the proposed method, a better phase unwrapped image can be obtained successfully. Index Terms--Branch-cut method, complex-valued MRF, complex-valued neural network; interferometric synthetic aperture radar (InSAR), LMS algorithm, Markov random field, neural network, parameter estimation, phase unwrapping, phase noise filtering, residue.

The paper addresses the detection of changes in multi-temporal polarimetric radar images, focusing on small objects and narrow linear features. The images were acquired at C- and L-band by the airborne EMISAR system. It is found that the radar intensities are better suited for change detection than the correlation coefficient and the phase difference between the co-polarized channels. In the case of linear features, there is no obvious difference between C- and L-band, and slight variations of the flight tracks are acceptable at look angles larger than 35 degrees. Theoretical detection thresholds are evaluated from the statistical distribution of the intensity ratio due to speckle. For the linear features and for urban environments, the observed thresholds are larger than the theoretical predictions. This is interpreted as an effect of radar intensity variations on length scales smaller than the spatial image resolution. The signature of urban areas is very sensitive to deviations between the flight tracks, and the sensitivity is larger at C-band than at L-band. On the other hand, the intensity contrast between buildings and the urban background is smaller at L-band and larger at C-band. For change detection, thresholds may have to be chosen separately for each object class because the intensity ratios of different object classes vary differently as a function of time. Index Terms--Change detection, polarimetry, synthetic aperture radar (SAR).

A new method of interferometric synthetic aperture radar (InSAR) is described. The method uses the Monte Carlo Metropolis algorithm, based on the complex-valued Markov random field (CMRF) model.

We present a new method for automatic target/object classification by using the optimum polametric radar signatures of the targets/objects of interest. The state-of-the-art in radar target recognition is based mostly either on the use of single polarimetric pairs or on the four preset pairs of orthogonal polarimetric signatures. Due to these limitations polarimetric radar processing has been fruitful only in the area of noise suppression and target detection. The use of target separability criteria for the optimal selection of radar signal state of polarizations is addressed here. The polarization scattering matrix is used for the derivation of target signatures at arbitrary transmit and receive polarization states (arbitrary polarization inclination angles and ellipticity angles). Then an optimization criterion that minimizes the within class distance and maximizes the between class metrics is used for the derivation of optimum sets of polarimetric states. The results of the application of this approach on real synthetic aperture radar (SAR) data of military vehicles are obtained. The results show that noticeable improvements in target separability and consequently target classification can be achieved by the use of the optimum over non-optimum signatures.

A FARIMA (fractional integrated autoregressive-moving average) process for synthetic aperture radar images is described.

Research describing the restricted growth concept for separating objects is presented. Algorithms are developed for the analysis of irregular unstructured objects such as those found in natural scenes.

The authors discuss the difficulty in processing images hidden behind foliage, and propose an alpha-stable model to detect foliage-concealed images. Difficulties in constructing the target detection algorithm are analyzed.

A new technique is presented for compressing synthetic aperture radar images. The technique includes different quanization strategies within conventional transform-based methods for processing incoherent optical images.

A digital spotlighting technique was used to successfully implement spatial undersampling for synthetic aperture ultrasonic imaging. The method, which is demonstrated in the ultrasonic imaging field, allows the recovery of correct reconstructed images from the aliased data through chirp signals. It was also noted that the proposed digital spotlighting technique is able to suppress grating lobes that corrupt reconstructed images.

This paper describes a novel approach for three-dimensional (3-D) imaging in a heavy-clutter environment. The proposed approach consists of two techniques. The first calls for the use of a synthetic aperture radar (SAR) moving in a circular orbit, whereas the second involves using angular correlation function (ACF) measurement for achieving clutter suppression. To demonstrate the imaging and clutter suppression capabilities of this combined approach (circular-correlation SAR), microwave imaging experiments are conducted at X-band frequencies (7-13 GHz). It is found that in heavy-clutter environments, this combined SAR approach outperforms conventional SAR, resulting in clearer images with enhanced target-to-clutter ratio. Index Terms: Angular correlation function, circular synthetic aperture radar, clutter suppression, confocal imaging, correlation techniques, frequency correlation function, target detection, 3-D image reconstruction.

Interferometric synthetic aperture radar (InSAR) ability to determine pixel elevation with a resolution derived from the interferometer phase resolution, is maintained as long as the interferometer phase change over the pixel does not exceed the phase resolution. This work investigates the surface coverage where this condition is met, determines the elevation resolution, and derives the ratio between elevation resolution and across-track resolution.

The purpose of the present article is to show that the JERS-1 synthetic aperture radar (SAR), primarily designed for global land observation, can be applied to deriving ocean wave spectra and determining the wave propagation direction. Split-look (multilook) processing is used to produce a sequence of individual sub-images of ocean waves. This processing technique has an inherent property that the successive subimages are formed at different discretely delayed times. Wave propagation direction can, therefore, be determined from a pair of wave images in different looks. The L-band JERS-1 SAR, despite its low signal-to-noise ratio, has an advantage over C-band spaceborne SAR's for having a longer integration time, suitable for the wave analysis using split-look processing. Several different methods are applied to the wave data collected near the coasts of Japan, including the methods of look sum (taking the standard Fourier spectrum of look-summed image), spectral sum, spectral phase shift, and cross-correlation function of sub-images. Index Terms - Cross-correlation function, cross spectrum, JERS-1 synthetic aperture radar (SAR), ocean waves, split-look processing.

The problem of obtaining quantitative data on spatial ocean wave spectra from the images of the ocean surface by either regular SAR or along-track interferometric SAR (INSAR) is studied. The dominant mechanism which allows imaging of ocean waves by SAR/INSAR is the so-called velocity bunching. This mechanism is essentially nonlinear. The theoretical analysis of SAR/INSAR imagery of the ocean surface due to velocity bunching is performed, and nonlinear solutions of the SAR/INSAR images of monochromatic waves and of the spectra of these images are obtained. Analytic expressions are presented here which allow to simulate accurately both SAR and INSAR images of waves with arbitrary lengths, heights and propagation directions. It is demonstrated that a monochromatic wave expands in the SAR/INSAR images into an infinite number of harmonics. In addition to the nonlinearity parameters of SAR which is related to the velocity bunching mechanism, it is shown that for complex INSAR, the degree of nonlinearity depends also on separation time between the two antennas. The results of the present study indicate that in addition to the prevailing practice to consider the phase component of the INSAR image, an analysis of the imaginary part of the complex INSAR map of the ocean surface may provide some supplementary information, beneficial, in particular, for rough sea. Index Terms - Ocean waves, remote sensing, SAR/along track interferometric SAR image, sea surface.

A three-dimensional (3-D) inverse synthetic aperture radar (ISAR) imaging experiment on a fir tree is described. Radar measurements on single trees under laboratory conditions can be performed in the scatterometric, with no aperture synthesis, and in the imaging (SAR) mode. Imaging an entire tree requires the use of a wideband radar and a two-dimensional (2-D) aperture which may be synthetic or real. A 3-D ISAR image can be obtained by processing coherently the backscattered fields as a function of the frequency and two rotation angles about axes which are mutually orthogonal. With such a system, and by conveniently processing the acquired data, the major scatterers and their positions within the tree volume can be identified with a high spatial resolution. The obtained 2-D and 3-D polarimetric ISAR images show that this new technique can be used to support the understanding of the interaction of electromagnetic waves with natural targets and provide the basis for the validation and verification of existing models. Index Terms - Radar measurements. radar polarimetry, synthetic aperture radar.

Studies were performed to investigate the application of the frequency-domain approach, polar-format spotlight-mode synthetic aperture radar (SAR) in lunar imaging and other planetary imaging problems at the Arecibo Observatory. The SAR imaging method was compared with the focused range-Doppler approach. Experimental results showed that SAR techniques yielded more refined images than those produced by the range-Doppler image processing technique.

A synthetic aperture radar (SAR) is considered, located on a geosynchronous receiver, and illuminated by the backscattered energy of satellite broadcast digital audio or television signals. The principal application of such a passive system could be differential interferometry, since even low spatial resolution coupled to zero baseline would be useful; however, other imaging applications could be envisaged and even some topographic capabilities if a baseline is created by ellipticizing the receiver's orbit. Spatial resolution, link budget, and possible focusing techniques are evaluated. Index Terms - Interferometry, microwave imaging, parasitic, satellite communication, synthetic aperture radar (SAR).

The all-weather capability makes synthetic aperture radar (SAR) interesting with respect to glaciological studies in remote arctic areas. The aim of this project was to investigate how the European Remote Sensing Satellite (ERS) SAR backscatter from certain glaciological and geomorphological structures varied with time and find out at which time of the year a SAR acquisition will give the best result when observing certain features. Five ERS-1 SAR images, one from the winter and four from the summer, were acquired over the northwestern part of Svalbard, Norway. Ground measurements and observations were made at the same time as one of the summer SAR acquisitions. The ground data as well as meteorological recordings were used to analyze the SAR backscatter changes in the multitemporal data set. A zonation on the glaciers representing snow, firn/superimposed ice, and glacier ice was detected in the winter image only. The equilibrium line could also be derived from this SAR image. Information on drainage patterns, distribution of wet and dry snow areas, and the occurrence of crevasses was derived from the summer images. Changes in snow cover distribution was identified in the summer images. The form and position of ice-cored moraines, fossil beach ridges, and river channels were identified in all images. Early summer images showed the highest potential for identification of landforms. Small-scale landforms, such as patterned ground and tundra polygons, could not be identified. Index Terms - Equilibrium line, geomorphology, glacier, image analysis, moraines, multitemporal, scattering, synthetic aperture radar (SAR).

This paper presents a new three-dimensional (3-D) near-field inverse synthetic aperture radar (ISAR) imaging technique. A 3-D ISAR image can be obtained by processing coherently the backscattered fields as a function of the frequency and two rotation angles about axes which are mutually orthogonal. Most of the existing ISAR algorithms are based on the Fourier transform and as such can tolerate only small amounts of wavefront curvature. Wavefront curvature must be taken into account when imaging an object in the near-field. Near-field ISAR imaging of large objects using a direct Fourier inversion may result in images which are increasingly unfocused at points which are more distant from the center of rotation. An algorithm based on an azimuth convolution between a near-field focusing function and the frequency domain backscattered fields is discussed. This convolution is efficiently implemented by using fast Fourier transform (FFT) techniques. Furthermore, in order to further alleviate the computational load of the algorithm, the discrete Fourier transform (DFT) of the focusing function is evaluated by means of the stationary phase method. Experimental results show that this technique is precise and virtually impulse invariant.

A study was conducted to characterize an adaptive finite impulse response filtering technique for interferometric synthetic aperture radar (SAR) imaging. Compared with fast Fourier transform methods, the Amplitude and Phase EStimation algorithm supports spectral estimates featuring lower sidelobes and spectral peaks. Moreover, the technique could also determine spectral estimates and SAR images that are more accurate than the Capon technique.

The phase-gradient autofocus (PGA) technique is robust over a wide range of imagery and phase error functions, but the convergence usually requires four - six iterations. It is necessarily iterative in an attempt to converge on a dominant target against clutter interference, while sufficiently capturing the blur function. In this paper, we propose to speed the estimation convergence by selectively increasing the pool of quality synchronization sources and not be limited by the range pixels of the SAR map. This is highly probable since each range bin contains more than one prominent scatterer across the integration aperture. It is also highly probable that the least-brightest selected scatterer in a range gate may turn out to be of higher energy as compared to the maximum brightest scatterer of another gate. With appropriate target filtering to final select the quality scatterers out of the large pool and with higher order phase error measurement tool, the new algorithm achieves near-convergence focusing quality without iteration. We named this solution the quality PGA (QPGA) algorithm. Index Terms - Phase gradient approach, synthetic aperture radar (SAR) autofocus.

Speckle filtering in synthetic aperture radar (SAR) images is a key point to facilitate applicative tasks. A filter aimed at speckle reduction should energetically smooth homogeneous regions, while preserving point targets, edges, and linear features. A compromise, however, should be arranged on textured areas. In this work, a ratio Laplacian pyramid (RLP) is introduced to match the signal-dependent nature of speckle noise. Local statistics filtering is applied to the different spatial resolutions of the RLP of a speckled image. For natural scenes, each pyramid layer is characterized by an signal-to-noise ratio (SNR) increasing as resolution decreases. Thus, each filter may be adjusted to achieve adaptivity also across scales. In addition, the estimation of the local statistics driving the filter is more accurate thanks to the multiresolution framework. A complete procedure is setup, and a general formulation, in which the variance of speckle is theoretically derived at each resolution, is developed. Experiments carried out on remotely sensed optical images corrupted with synthetic speckle, as well as on true SAR images, show the potentiality of the pyramid-based approach compared with other established despeckle algorithms, in terms both of SNR improvements and of enhancement in visual quality. Index TErms - Laplacian pyramid, local statistics filtering, multiresolution analysis, speckle, synthetic aperture radar (SAR) images.

Linear and nonlinear adaptive networks are used for target discrimination in synthetic aperture radar imagery. Problems associated with the two-parameter constant false alarm rate detector led to the use of the quadratic gamma discriminator (QGD) as a classifier. This classifier is further improved in terms of linear processing as a multilayer perceptron (MLP) topology called the nonlinear QGD (NL-QGD).

The polarimetric whitening filter (PWF) proposed by Novak et al. is extended to the multilook case to form a multilook PWF (MPWF) for intensity speckle reduction in multilook polarimetric synthetic aperture radar (SAR) images. The MPWF optimally processes the multilook covariance matrix to generate a minimum-speckle intensity image. The amount of speckle reduction achievable by using the MPWF is quantified. We also show that the MPWF is equivalent to a maximum likelihood (ML) filter. Experimental results with the NASA/Jet Propulsion Laboratory (JPL) airborne L-band, four-look polarimetric SAR data demonstrate the effectiveness of the MPWF. Index Terms - Maximum likelihood (ML) estimation, multilook processing, polarimetric synthetic aperture radar (SAR), speckle reduction, texture.

A novel across-track interferometric synthetic aperture radar (SAR) raw signal simulator is presented. It is based on an electromagnetic backscattering model of the scene and an accurate description of the SAR system impulse response function. A set of meaningful examples are also presented. They show that the proposed simulator is structurally consistent and correctly simulates the decorrelation effect, both in the mean and in the distribution sense.

An approach has been developed that involves the fit of a combination of three simple scattering mechanisms to polarimetric SAR observations. The mechanisms are canopy scatter from a cloud of randomly oriented dipoles, even- or double-bounce scatter from a pair of orthogonal surfaces with different dielectric constants and Bragg scatter from a moderately rough surface. This composite scattering model is used to describe the polarimetric backscatter from naturally occurring scatterers. The model is shown to describe the behavior of polarimetric backscatter from tropical rain forests quite well by applying it to data from NASA/Jet Propulsion Laboratory's (JPL's) airborne polarimetric synthetic aperture radar (AIRSAR) system. The model fit allows clear discrimination between flooded and nonflooded forest and between forested and deforested areas, for example. The model is also shown to be usable as a predictive tool to estimate the effects of forest inundation and disturbance on the fully polarimetric radar signature. An advantage of this model fit approach is that the scattering contributions from the three basic scattering mechanisms can be estimated for clusters of pixels in polarimetric SAR images. Furthermore, it is shown that the contributions of the three scattering mechanisms to the HH, HV, and VV backscatter can be calculated from the model fit. Finally, this model fit approach is justified as a simplification of more complicated scattering models, which require many inputs to solve the forward scattering problem.

This paper explains the underestimation of phase slope and the consequent distortion of the phase surface, observed in two-dimensional (2-D) phase unwrapping by linear estimators. like least squares methods applied to synthetic aperture radar (SAR) interferometry. These methods minimize the difference between the gradient of the unwrapped phase and the wrapped differences of the measured wrapped phase. Using the probability distributions of phase noise and phase differences for a given coherence, the probability of a phase gradient error giving rise to a nonconservative vector field is derived. It is shown that this phase gradient error has nonzero mean in the presence of phase slopes. Linear phase estimators cannot distinguish the mean phase gradient error from a true phase slope; hence, the unwrapped phase shows a slope bias. This bias is quantified as a function of coherence and the number of independent samples that are averaged. The theoretical results are confirmed by simulations.

Spatial analysis of synthetic aperture radar (SAR) image data holds much promise for characterizing and discriminating environmental scene elements. The autocorrelation function (ACF) has been identified as a potentially useful spatial metric because it admits an analysis with conventional linear system theory. Recent models of spatial scattering suggest that ACF-based texture analysis of SAR image data is capable of discriminating between a variety of area extensive targets. The incorporation of texture in an image classification or segmentation system requires some understanding of the uncertainties in the texture estimates. In this paper, we introduce a particular ACF model and examine the errors associated with estimating its parameters from image measurements. We also conduct an analysis of two important classes of errors: imaging system errors and estimation errors. We found that as the proportion of raw signal used to create the image increases the effects of system errors rapidly degrade ACF performance. This has implications for operationally produced image products that do not use an autofocusing procedure. We also found that the agreement between theoretical and observed estimation errors is quite good, so that the scale of these errors may be accurately estimated during a spatial analysis of the image data. We found some residual bias that may be attributed to both the use of the ACF itself and to the way the ACF model was constructed.

Cross spectra of individual-look synthetic aperture radar (SAR) images of the ocean surface are used to retrieve ocean wave spectra. A quasilinear transform is derived that relates ocean wave spectra to SAR image cross spectra. Furthermore, Monte Carlo simulations are also carried out for those cases where quasilinear imaging does not apply. It is shown that, as the time separation between the individual-look SAR images increases (within a limit determined by the Doppler bandwidth of the original single-look complex SAR image), the spectral energy density of the imaginary part of the SAR image cross spectra increases, while the spectral energy density of the real part decreases. The integration time has a small effect on the SAR image cross spectra as long as the integration time is large compared to the scene coherence time. In order to retrieve ocean wave spectra from SAR data by using cross-spectral analysis techniques, we suggest calculating two SAR image cross spectra: one with a short time separation and one with a large one between the individual-look SAR images. The real part of the SAR image cross spectra calculated from individual-look SAR images with the short time separation is used for retrieving ocean wave spectra, which have a 180 [degrees] ambiguity in wave propagation direction. The imaginary part of the SAR image cross spectra calculated from individual-look SAR images with the long time separation is used for removing this 180 [degrees] ambiguity. Index Terms - Cross spectral analysis, ocean waves, SAR image.

The Technical University of Denmark (TUD) Synthetic Aperture Radiometer (SARad) is a two-channel demonstration model that can simulate a two-dimensional (2-D) thinned array radiometer having an unfilled aperture populated with several small antenna elements. Aperture synthesis obtained by interferometric measurements using the antenna elements in pairs, followed by an image reconstruction based on an inverse Fourier transform, results in an imaging instrument without the need of mechanical scan. The thinned aperture and the non-scanning feature make the technique attractive for spaceborne radiometer systems, especially at low frequencies. The TUD SARad demonstration model consists of a two-channel [K.sub.u]-band correlation radiometer with two horn antennas and an antenna mounting structure enabling the horns to be mounted in relevant positions within a certain aperture. A total aperture synthesis is obtained by sequentially placing the two antenna elements in all required pairs of positions and measuring the corresponding samples of the visibility function. The system has been used to demonstrate 2-D synthetic aperture imaging of complex targets in outdoor ground experiments. A special feature of the system is that it uses a focused antenna system, thus enabling a short distance to the target, yet still utilizing image reconstruction algorithms identical to those used in a normal far-field situation. The aperture synthesis theory is discussed, with special emphasis on focused systems; the radiometer system is described; and images suitable for demonstration of resolution and other imaging properties are presented and discussed. Index Terms - Aperture synthesis, radiometer.

Edge detection is a fundamental issue in image analysis. Due to the presence of speckle, which can be modeled as a strong, multiplicative noise, edge detection in synthetic aperture radar (SAR) images is extremely difficult, and edge detectors developed for optical images are inefficient. Several robust operators have been developed for the detection of isolated step edges in speckled images. We propose a new step-edge detector for SAR images, which is optimal in the minimum mean square error (MSSE) sense under a stochastic multiedge model. It computes a normalized ratio of exponentially weighted averages (ROEWA) on opposite sides of the central pixel. This is done in the horizontal and vertical direction, and the magnitude of the two components yields an edge strength map. Thresholding of the edge strength map by a modified version of the watershed algorithm and region merging to eliminate false edges complete an efficient segmentation scheme. Experimental results obtained from simulated SAR images as well as ERS-1 data are presented. Index Terms - Edge detection, multiedge model, region merging, segmentation, speckle, synthetic aperture radar (SAR), watershed algorithm.

A study was conducted to investigate the effectiveness of the use of advanced two-dimensional spectral estimation algorithms for synthetic aperture radar (SAR) imaging. Results show that SAR imaging using these advanced methods generates enhanced resonance and reduced speckle and eliminates sidelobe artifacts as compared to using conventional Fourier transform SAR imaging techniques.

A novel approach for inverse synthetic aperture radar (ISAR) imaging is presented for both target translational motion and rotational motion nonuniformity compensation. The basic idea is to perform Doppler tracking to individual scatterers via an adaptive joint time-frequency (AJTF) projection technique. After maximizing the projection of the phase function to a set of basis functions in time-frequency plane, the Doppler frequency drift of the strongest scatterer in the range bin is automatically tracked out and the multiple prominent point processing (PPP) scheme is implemented to eliminate both the translational motion error and rotational motion nonuniformity. Further the azimuth spacing can be estimated, which permits polar reformatting of the original collected data.

Leads are relatively linear features in the sea ice cover, which are composed of open water or new, thin ice. Because of their composition, leads impact the ocean/air heat exchange. Automated analysis of leads from sea ice imagery may provide a means of gathering important information about the sea ice cover and its climatic influence. This paper describes: 1) a method for extracting and analyzing leads from ERS-1 synthetic aperture radar (SAR) images classified by ice type and 2) the results of using this method on images of the Beaufort Sea. The methodology consists of identifying potential lead features in the image and measuring their characteristics both before and after using a thinning or skeletonization technique on the features. The measurements obtained using this method include lead area, average width, number of leads in an area, amount of branching, and linearity of the lead. These measurements were analyzed with respect to the time of year and the latitude of the images. Results indicate that the measurements produced by the methodology are consistent with lead measurement distributions that others have found. The results of the study suggest that the methodology is appropriate to study lead characteristics on a large scale. Index Terms - Beaufort Sea, image analysis, image processing, leads, lead statistics, SAR, sea ice.

In this paper, we have studied the significance of non-Gaussian signal statistics in some synthetic aperture radar (SAR) images of the ocean surface. The study consisted of calculating the amplitude histogram of the returned echoes from the images and comparing these with the Rayleigh- and [K.sub.v]-distributions, corresponding to the Gaussian and non-Gaussian statistics, respectively. The images used were some C-band SAR data from the Canadian airborne SAR collected during the NORCSEX'88 campaign and some ERS-1 data collected during the NORCSEX'91 campaign. The analysis of the NORCSEX'88 data included studies of the dependency of the signal statistics on incidence angle and meteorological and imaging conditions. It was found, specifically at small incidence angles, that there was a significant deviation from Gaussian statistics. It was also found that when the wind was blowing against the waves, the deviation from Gaussian statistics was more pronounced than when the wind was blowing in the same direction as the waves were propagating. The study also showed a correlation between the signal statistics and the width of the SAR image spectra. At low incidence angles, this agrees with the interpretation that non-Gaussian statistics may be related to strong widebanded scattering events. However, since non-Gaussian statistics also were observed at incidence angles as high as 50 [degrees], it is evident that the modulation of the scattering cross section by the long waves is also an important factor. In addition, the analysis of the ERS-1 data showed that to account for the width of the SAR image spectra, an azimuth smearing term, due to short scene coherence time, had to be included. This was in the present work done by modeling the short-coherence-time-smearing as a Gaussian low-pass filter. By this procedure, we were able to obtain realistic estimates for the average scene coherence time of the SAR scenes. Index Terms - Azimuth smearing, K-distribution, microwave radar backscatter, non-Gaussian statistics, scene coherence time, signal statistics, synthetic aperture radar (SAR) imaging.

The ocean surface wind field is observed from space operationally using scatterometry. The European Space Agency's (ESA's) ERS-1 satellite scatterometer routinely produces a wind product that is assimilated into forecast models. Scatterometry cannot give accurate wind estimates close to land, however, because the field of view of a spaceborne scatterometer is on the order of 50 km. Side lobe contamination, due to the large contrast in backscatter between land and water, compounds the problem. Synthetic aperture radar (SAR) can provide wind speed and direction estimates on a finer scale, so that high-resolution wind fields can be constructed near shore. An algorithm has been developed that uses the spectral expression of wind in SAR imagery to estimate wind direction and calibrated backscatter to estimate wind strength. Three versions, based on C-band scatterometer algorithms, are evaluated here for accuracy in potential operational use. Algorithm estimates are compared with wind measurements from buoys in the Gulf of Alaska, Bering Strait, and off the Pacific Northwest coast by using a data set of 61 near-coincident buoy and ERS-1 SAR observations. Representative figures for the accuracy of the algorithm are [+ or -]2 m/s for wind speed and [+ or -]37 [degrees] for wind direction at a 25-km spatial resolution. Index Terms - Image analysis, radar applications, satellite applications, synthetic aperture radar (SAR), wind.

We propose a two-step algorithm for almost unsupervised detection of linear structures, in particular, main axes in road networks, as seen in synthetic aperture radar (SAR) images. The first step is local and is used to extract linear features from the speckle radar image, which are treated as road-segment candidates. We present two local line detectors as well as a method for fusing information from these detectors. In the second global step, we identify the real roads among the segment candidates by defining a Markov random field (MRF) on a set of segments, which introduces contextual knowledge about the shape of road objects. The influence of the parameters on the road detection is studied and results are presented for various real radar images. Index Terms - Markov random fields (MRF's), road detection, SAR images, statistical properties.

The effect of ionospheric Faraday rotation on polarimetric synthetic aperture radar (SAR) imagery is evaluated theoretically. Faraday rotation is readily included in the polarimetric SAR system equation describing the received signal if the rotation angle is assumed constant over the synthetic aperture length. Deviations from a constant rotation angle over this length impact the validity of the derived system equation, but an explicit evaluation shows such effects to be negligible for most expected ionospheric conditions. The modified system equation provides a basis for calibration techniques that can be used to either remove or measure Faraday rotation effects in SAR imagery.

A method is presented for detecting moving targets which are hidden in foliage. Finding moving targets is difficult because the image of the target is weak compared to the surrounding foliage which cannot be filtered out. The method involves two Synthetic Aperture Radar Systems (SAR) being fixed onto a single aircraft and they have a common range and altitude. The monopulse SAR images enable difference and interferometric images to be built up.

Usual coherence estimation in SAR interferometry is a time consuming task since an accurate estimation of the local frequency of the interferometric fringes is required. This paper presents a fast algorithm for generating coherence maps, mainly intended for data browsing. The proposed estimator is based on the speckle similarity of coherent SAR data, and is thus independent of fringe frequency. The following advantages, with respect to the usual estimates, are achieved: a) The estimator is more than 100 times faster, achieved at the cost of a reduced statistical confidence. b) The estimator is not affected by possible local frequency estimation errors. c) The estimator can be directly applied to single look detected images. The theoretical derivation of the statistical properties of the frequency independent estimator is carried out in the stationary case. The nonstationary case is then analyzed on real ERS SAR images.

A new joint time-frequency inverse synthetic aperture radar (ISAR) algorithm that combines ISAR processing with the joint time-frequency signal representation is presented as a means of extracting the nonpoint-scattering features from the standard ISAR image. The adaptive Gaussian representation, applied to the range axis of the ISAR image, is used as the time-frequency processing engine. This technique uses Gaussian basis functions to adaptively parameterize the data and, as a consequence, the point-scattering mechanisms and resonance phenomena can be readily separated based on the width of the Gaussian bases. The adaptive joint time-frequency ISAR algorithm is tested using data generated by the moment-method simulation of simple structures and the chamber measurement data from a scaled model airplane. The results show that nonpoint-scattering mechanisms can be completely removed from the original ISAR image, leading to a cleaned image containing only physically meaningful scattering centers. The nonpoint-scattering mechanisms, when displayed in the frequency-aspect plane, can be used to identify target resonances and cutoff phenomena. Index Terms - Electromagnetic scattering inverse problems.

We present in this communication a new linear edge detector that is very well suited to process noisy images. Performances of the new operator have been evaluated using Canny's criteria together with experimental comparison results on a noisy SAR image.

This paper focuses on the spatially varying backscattering signature of an area of refrozen brash ice observed by a ship based scatterometer. The measurements were carried out as part of the Baltic Experiment for ERS-1 in 1994. The scatterometer was operated at 5.4 GHz at different incidence angles and polarizations. By analysing the scatterometer data over azimuth scans, it was found that the backscattering variabilities are not only due to fading but also contain a textural component. Surface height profiles were measured using a laser. The observed ice surface roughness was nonstationary over the measurement area. The ice surface can be approximated by adjacent patches of stationary roughness with patch dimensions of about 4.5 m. From the roughness spectra of different stationary patches, two roughness classes can be distinguished. The implications of estimating the roughness parameters from relatively short profile lengths is discussed and the effect on theoretical predictions of the backscattering coefficient is investigated. The texture variance is evaluated theoretically on the basis of the simulated backscattering coefficients of the two observed roughness classes and is found to compare with the scatterometer data. Index Terms - Scatterometry, SAR, surface roughness, texture.

The nature of the surface in a fiat arid zone located in the western Sahara desert was studied and its roughness parameters defined. The main geological units were compared with backscattering data from ERS-1 images. Results showed a close correlation between the backscattering coefficient, [[Alpha].sub.0], and the maximum height, [h.sub.max], of the rocks making up the arid landscape. The simplified relationship [[Alpha].sub.0] = 4.63. log [h.sub.max] 15.29 (with [h.sub.max] in cm and [[Alpha].sub.0] in dB) was established from field measurements, giving an [r.sup.2] of about 88%. This empirical relationship is confirmed by an other close relation between [[Alpha].sub.0] and the statistical parameter s, the height standard deviation. The advantage of [h.sub.max] is that it can be extracted for any site, whatever its roughness may be. The spatial distribution of the rock facets and the geometric characteristics of the incidence angle of the radar signal seem to explain the results. Index Terms - Modeling, radar, roughness.

In this paper we outline a new scheme for parameterizing polarimetric scattering problems, which has application in the quantitative analysis of polarimetric SAR data. The method relies on an eigenvalue analysis of the coherency matrix and employs a three-level Bernoulli statistical model to generate estimates of the average target scattering matrix parameters from the data. The scattering entropy is a key parameter is determining the randomness in this model and is seen as a fundamental parameter in assessing the importance of polarimetry in remote sensing problems. We show application of the method to some important classical random media scattering problems and apply it to POLSAR data from the NASA/JPL AIRSAR data base. Index Terms - Image classification, radar polarimetry, unsupervised classifier.

Multiscale stochastic models can be used for segmentation of natural clutters of grass and forest terrains and for enhancement of anomalous pixel regions for man-made object detection in synthetic aperture radar (SAR) imagery. The models take advantage of the coherent nature of SAR sensors and the differences in interscale variability and predictability of different images. The models have a scale-autoregressive nature, and are applicable to image pixel classification and grass-forest boundary detection. The algorithmic calculations involved are simple and efficient.

A radiometric and textural classification method for the single-channel synthetic aperture radar (SAR) image is proposed, which explicitly takes into account the probability density function (pdf) of the underlying cross section for K-distributed images. This method makes extensive use of adaptive preprocessing methods (e.g., Gamma-Gamma MAP speckle filtering), in order to ensure good classification accuracy as well as fair preservation of the spatial resolution of the final result. Error rates can be estimated during the training step, allowing one to select only relevant reflectivity classes and to save computation time in trials. The classification method is based on a Maximum Likelihood (ML) segmentation of the filtered image. Finally, a texture criterion is introduced to improve the accuracy of the classification result.

Extraction of the Shoreline in SAR images is a difficult task to perform using simple image processing operations such as grey-value thresholding, due to the presence of speckle and because the signal returned from the sea surface may be similar to that from the land. A semiautomatic method for detecting the shoreline accurately and efficiently in ERS-1 SAR images is presented. This is aimed primarily at a particular application, namely the construction of a digital elevation model of an intertidal zone using SAR images and hydrodynamic model output, but could be carried over to other applications. A coarse-fine resolution processing approach is employed, in which sea regions are first detected as regions of low edge density in a low resolution image, then image areas near the shoreline are subjected to more elaborate processing at high resolution using an active contour model. Over 90% of the shoreline detected by the automatic delineation process appear visually correct.

The brightness in a SAR image is affected by topographic height variations due to 1) the projection between ground and image coordinates, and 2) variations in backscattering coefficient with the local scattering geometry. This paper derives a new equation for 1), i.e., the radiometric slope correction, based on a calibration equation which is invariant under a coordinate transformation. An algorithm is described to obtain the slope correction from a SAR interferogram, which also enables retrieval of the full scattering geometry. Since the SAR image and interferogram are derived from the same data set, there is no need to match the image with the calibration data. There is also no need for phase unwrapping since the algorithm only uses the fringe frequencies. A maximum-likelihood estimator for the fringe frequency is analyzed and the algorithm is illustrated by processing ERS-1 SAR data. The example demonstrates that the spatial resolution and calibration error are adequate for most applications.

We discuss an efficient phase preserving technique for ScanSAR focusing, used to obtain images suitable for ScanSAR interferometry. Given two complex focused ScanSAR images of the same area, an interferogram can be generated as for conventional repeat pass SAR interferometry. However, due to the nonstationary azimuth spectrum of ScanSAR images, the coherence of the interferometric pair and the interferogram resolution are affected, both by the possible scan misregistration between two passes and by the terrain slopes along the azimuth. The resulting decorrelation can be significantly reduced by means of an azimuth varying filter, provided that some conditions on the scan misregistration are met. Finally, the SAR-ScanSAR interferometry is proposed: here the decorrelation can always be removed with no resolution loss by means of the technique presented in the paper.

Estimates of ocean wave spectra obtained from conventional synthetic aperture radar (SAR) images suffer from the azimuth falloff effect, which limits the range of azimuth wavenumbers that can be observed by such systems. This effect was earlier predicted to be modified by the use of a second receive antenna which is displaced from the first in the along-track direction. In this paper, we present experimental evidence which is consistent with these theoretical predictions and suggests that the azimuth falloff limitations inherent in conventional SAR systems can be overcome by the use of two or more appropriately spaced antennas.