The study of interaction of waves from rough surface has been of interest to many people. It has obvious applications to remote sensing, both acoustic and electromagnetic. With the flood of SAR data becoming available from both airborne and spaceborne systems, this study is gaining in importance for unthrstariding the interaction of radio waves from ocean surfaces, bare land as well as agricultural land, foliage and urban images. The problem of scattering of electromagnetic wave from rough sea surfaces near grazing angle has been of interest to many people for some time.

In previous paper, a ray-racing model for urban SAR imaging and simulation was described in which a physical optics extension of the geometrical theory of diffraction (GTD) was used to include diffraction returns in calculating the specular backscatter from the scene. This theory is based on that the most important contributions towards the scattered field come from an area in the neighborhood of some critical points on the scattering surface. For a planar surface, three critical points may be regarded; specular, edge- diffraction and corner-diffraction points. A physical optics version of PoGTD was taken with the approximate diffraction coefficients derived using physical optics approximations to canonical problems. The results from the simulator are examined and validated by comparing them with theoretical and experimental results calculated and found in an annerchaic chamber using simple targets. The objects were chosen to cover different combination of the critical points, i.e. specular, edge- and vertex-diffraction which contribute towards the backscattered field. For both set of data the co-polarized data are presented and compared.

In this paper we present a new algorithm for segmenting SAR images. A common problem with segmentation algorithms for SAR imagery is the poor placement of the edges of regions and hence of the regions themselves. This usually arises because the algorithm considers only a limited number of placements for regions. The new algorithm circumvents this shortcoming, and produces an optimal segmentation into a prescribed number of regions. An objective function is derived from a statistical model of SAR imagery. This objective function is then minimized by the method of simulated annealing which is, assuming some weak constraints, guaranteed to give the global minimum. Starting with an initial segmentation, the algorithm proceeds by randomly changing the current state. The annealing then decides whether or not to accept the new configuration by calculating the difference between the likelihoods of the data fitting these segmentations. In practice there are many possible implementations of the algorithm. We describe an implementation which uses a free topological model and alters the segmentation on a pixel by pixel basis. This makes it possible to get results of high resolution, as shown in results obtained by applying the new algorithm to both airborne X-band and ERS1 imagery.

SAR clutter may be characterized by its single-point statistics. Many statistical models for SAR clutter have been proposed in the literature, including the Weibull, log- normal and K distributions, which fit the single point clutter statistics to varying degrees. However, a common feature of all these models is that it is difficult to incorporate correlations into the formulation in an analytically tractable manner. In practice, the only distribution whose multivariate form can be manipulated with any degree of ease is the Gaussian but this distribution does not fit SAR clutter intensities. However, on taking the log of the intensities, the clutter distribution can be approximated by a Gaussian, hence producing the log-normal model. Furthermore, closer approximations can be obtained by using Gaussian mixtures in which a weighted combination of different Gaussians is formed. Correlations may be introduced into this model via the Gaussian components of the mixture distribution. In addition, the weights used in the mixture form a discrete random process upon which correlations may be imposed. The weight process may thus be viewed as a Markov random field which modulates a correlated Gaussian field in the most general formulation. The proposed clutter model is thus an example of a hidden Markov model whose properties will be discussed in this paper.

The paper deals with the optimum detection of slowly moving targets with dual-channel SAR in the presence of internal clutter motion and mismatch from the DPCA condition. A double exponential model for the clutter covariance allows both interpretation of the optimum detector and closed form performance analysis to be derived. A shift invariant FIR space-time filter is obtained as the cancellation filter in the optimum detector. This is appealing for the processing of the long sequence of SAR echoes.

Previous studies on discriminating between SAR clutter textures have been largely based on optimizing the performance for the single-point statistics of texture alone. This paper makes a study of the effects of introducing spatial correlations on classification into a set of predefined textures. Maximum likelihood (ML) estimation is adopted throughout. Classification based on fitting the autocorrelation function is compared with that based on the amplitude spectrum of the data. In each case, care is taken to derive a good approximation to the observed data distributions so that the ML treatment is well founded.

Many of the despeckling filters currently available operate by smoothing over a fixed window, whose size must be decided by two competing factors. Over homogeneous regions large window sizes are needed to improve speckle reduction by averaging. However, a large window size reduces the fundamental resolution of the algorithm, as with multi- looking. For instance, when one of these filters attempt to reconstruct a small bright object it produces artifacts around the object over a distance equal to the filter dimension. This means that the background is badly defined in the neighborhood of bright targets and edges, which is just where one would like it accurate. In this paper, these problems are overcome by introducing a correlated neighborhood model into the MAP filter. This filter operates on a small window and so is able to preserve resolution. The correlation model allows us to describe both the scene heterogeneity and the effects of partial smoothing, which in turn, allows us to iterate the filter, hence, increasing the amount of smoothing that can be achieved with a small window. This gives a filter that is able to adapt to the underlying fluctuations of the scene, preserve detail of still achieve large amounts of smoothing. The final iterated filter is then compared with the current DRA simulated annealing algorithm.

The authors analyze the sub-optimal performance of simple texture measures for estimating the reciprocal order parameter of K-distributed radar clutter. A non-committal neural net has been applied to the parameter estimation task which has shown that improved error estimates are obtained when multiple moments are used to characterize the texture. Prompted by this result a new estimator is proposed which combines the mean normalized log intensity and the amplitude contrast moments of the imaged data. Its error performance is determined by the relative weighting in which the two moments are combined. With an appropriate choice of the weighting the modified estimator outperforms the normalized log estimator and gives close to maximum likelihood performance on the estimates over a wide range of the parameters values which are of interest.

Ship traffic surveillance plays an important role in providing safety of shipping, traffic management as well as treating a great deal of related environmental problems. One of the quite new but promising possibilities for this purpose lies in suing satellite-borne SAR imagery. A moving ship produces a set of waves often appearing in the image as bright or dark linear structures. These structures can provide information on both ship direction and speed. In the work presented here, the possibility of automatic detection of ship wakes was tested by applying the Radon transformation to the area surrounding the ship, followed by a verification of each detected wake by a set of criteria to discern it from other wake-like linear structures which are very often appearing in SAR imagery. Different methods for the improvement of the original image are applied as a preprocessing technique for the Radon transformation. The success of the algorithm implementation was found to depend greatly upon both wake and image appearances. The band-pass filtering together with a non-linear image amplification proved to be of use for the detection of practically invisible wakes.

Due to the speckle effect of coherent imaging the detection of liens in SAR scenes is considerably more difficult than in optical images. A new approach to detect lines in noisy images using a Markov random field model and Bayesian classification is proposed. The unobservable object classes of single pixels are assumed to fulfill the Markov condition, i.e. to depend on the object classes of neighboring pixels only. The influence of neighboring line pixels is formulated based on potentials derived from a random walk model. Locally, the image data is evaluated with a rotating template. As SAR intensity data is deteriorated by multiplicative noise, normalized intensity ratio is used as the response of the local line detector resulting in a constant false alarm rate. The new approach integrates SAR intensity and coherence from interferometric processing of a SAR scene pair. Besides maximum a posterior and iterated conditional modes estimation of the object parameters, an implementation of local highest confidence first estimation is used. It is initially applied to the sites which are most probably structures in object space, and is then allowed to progress to regions less promising for line detection depending on the results of previous iterations. In this way processing times are substantially reduced.

The optical flow models have been widely used in computer vision and recently also applied to motion estimation of sea ice with SAR satellite image sequences. However, the optical flow models, which are commonly based on the assumptions that the image brightness is stationary and velocity field or optical flow is constant within a small neighborhood, are sensitive to conditions usually encountered in real imagery. It is of interest and important to analyze the error sources and adaptability of optical flow models to real imagery. From similar assumptions, different optical flow methods can be derived. In particular, the optical flow model based on the second order partial derivatives of image brightness is emphasized because of its immunity to the aperture problem. The attention in the paper is paid to the analysis of this optical flow model. Based on error analysis, this paper discusses the adaptability of the optical flow model and the estimated accuracy of the velocity field in motion estimation of sea ice from ERS-1 SAR image sequences. Some experimental results of motion estimation from ERS-1 SAR images of sea ice in the Arctic area are presented in the paper, and some conclusions are finally drawn.

Two new Bayesian Maximum A Posteriori (MAP) vector speckle filters are developed for multi-channel detected SAR images. These filters incorporate statistical descriptions of the scene and of the speckle in multi-channel SAR images. These models account for the scene and system effects which result in the presence of a certain amount of correlation between the different channels. In order to account for the effects due to the spatial correlation of both the speckle and the scene in SAR images, estimators originating from the local autocorrelation functions are incorporated to these filters, to refine the evaluation of the non-stationary first order local statistics, as well as to improve the restoration of the scene textural properties and to preserve the useful spatial resolution in the speckle filtered image. Results obtained, first on 3-look spaceborne ERS PRI multi-temporal images, then on a couple of ERS PRI and RADARSAT standard beam SAR images illustrate the performance of these estimators for different SAR combinations. These results show that these filters present convincing performances for speckle reduction, as well as for texture preservation and for small and/or thin scene objects detection. Finally, since the new established Bayesian speckle filters present the structure of control systems, promising perspectives are presented for the development of application oriented processing chains for multi-channel SAR images, where the speckle filtering operation will be the first processing step.

It is an accepted fact that combining data from different sensors can yield more information than the individual sensors will give when used on their own. This is especially true for synthetic aperture radar (SAR) data when combined with optical data. However, if performed manually, the registration of these two data sets can be time consuming and inaccurate. Due to the properties of SAR images, it is generally very difficult to select ground control points to use in the registration process. This paper covers the work being undertaken to develop an automatic system for registering SAR data to optical data using feature matching. In order to complete the registration, corresponding features have to be extracted from both images and matched with each other to generate a number of 'match points'. The parameters of the transformation can then be calculated using these match points. The successfully merged image will then be used to develop accurate change detection algorithms. Perhaps one of the most difficult aspects of this procedure is the extraction of features from SAR images, which are heavily degraded by speckle. Here we present the results of testing various speckle reduction filters and segmentation procedures in order to examine their ability to aid feature extraction. We have also developed a number of new algorithms which reduce speckle and segment SAR images.

The research described here addresses the problem of detecting lines of pylons in a SAR image. Specifically, an approach for solving this problem which attempts to overcome the difficulties arising from the speckle is described, and examples of the results derived from applying it are presented.

This paper deals with adaptive vector filtering of speckling in SAR images. The proposed method is based on the ordering of the vector data. The vector order is obtained by an Euclidean distance calculated from the center of a set of vectors. The local variation coefficient has been introduced for filter adaptivity. This coefficient is a reflection of the generalization of local scalar variation towards a case of a vectorial variation. For this purpose we use the fuzzy cluster analysis domain.In each large window, we decompose data into two groups using the fuzzy center mobile algorithm. The Euclidian distance between the two group centers provides good information about local variation in the window. In a homogeneous area this distance is minimal whereas it is considerable in the heterogeneous area. The global distribution of this distance coming from all windows of the image determine the threshold value. According to the local variation value, the vector will be treated in two different ways. If the local variation is less than the threshold value then we use a classic mean filtering. Otherwise, we replace the vector of interest by another ordered vector. Its choice depends on the local variation value.

This paper presents a method for unsupervised segmentation of polarimetric SAR data into classes of homogeneous microwave backscatter characteristics. Clustering of polarimetric backscatter are obtained either by the CMF-NSO or be SEM algorithm. These algorithms carry out the classification without a priori assumptions on the number of classes in the data set. Assessment of cluster validity is based on performance measures using hypervolume V or CS function criteria. The later measures the overall average compactness and separation of a fuzzy-partition. The CMF-NSO algorithm performs well in situations of large variability of cluster shapes and densities. Given the clusters of polarimetric backscatter, the entire image is segmented using a MAP estimation. Implementation of the MAP technique is accomplished by an ICM algorithm. Results, using fully polarimetric SAR forest data, obtained by the CMF-NSO following by the ICM algorithm with a K-distribution model are quite satisfactory.

SAR data is a remote sensor which produces a high amount of data. Since the transmission of the corresponding raw signals requires extremely high data rates, on-board data compression is usually necessary. The approach presented in this paper use the statistic properties of the received signal in order to improve the compression performance.

The HK-filter previously developed by the authors is a statistical filter devoted to the speckle removal and structural features enhancement on SAR images. As this filter is based on a pattern analysis applied to the shape observed in a given neighborhood and according to a gray- level range, it can be used to extract structural features. We purpose here to use spatial arrangement given by the occurrence of the constitutive elements of shapes, to extract and to differentiate the different structural features encountered on SAR images.

The study of the foreshore is of considerable interest to the environmental science community, and the advent of regular SAR imagery from the ERS, Radarsat and JERS satellites has offered the opportunity to monitor continuously this dynamic region. Knowledge of foreshore topography is of use in improving flood prediction models, measuring sediment flux, and assessing the effectiveness of sea defences. Conventional surveying is the expensive, time consuming and often hazardous current technique often employed to obtain these data. The alternative technique presented here involves a semi-automatic process to delineate the shoreline on a number of ERS SAR images, and a model to calculate the sea heights at the time the images were taken. By combining these data a set of quasi-contours is generated, and from these it is possible to devise a digital elevation model of the foreshore. Each of these quasi-contours is associated with the time its image was taken, and it is therefore necessary to interpolate in time and space. Such interpolation allows the creation of time- dependent DEMs which can be used to study the long-term evolution of the foreshore. This paper demonstrates the technique by following its application to the length of UK coastline of most interest to the NERC Land-Ocean Interaction Study Community Programme, the 100km from the Humber estuary to the Wash. The INDUS project funded under the British National Space Centre Earth Observation LINK scheme is further refining the accuracy of the technique and extending its application to cover more of the coastal regions of the UK, as well as establishing the requirements of users.

This paper presents an original method base don the simultaneous analysis of phase, amplitude and coherence images for the detection of landscape features which do not provide topographic phase signal; for these surfaces, either phases or phase differences are not valid, respectively because of temporal changes and non-backscattering surfaces, or because of foreshortening and lay-over effects on mountain fore-slopes. A contextual classification based on Markov Random Fields is used to detect such perturbations through different classes corresponding to different feature shapes and characteristics. Classes describing thick structures are identified on the coherence and amplitude images available along with interferograms and on a measure of confidence derived from spectral analysis of the phase signal. The classical Potts model is well adapted to the regularization of such compact shape structures. Thin structures are enhanced by directional contrast operators dedicated to speckle SAR images. Associated directions allow a specific regularization scheme which preserves thin structures. The classification results are eventually used as a mask to enable automatic phase unwrapping performed either by path-following methods or by weighted least squares methods. Feature detection and unwrapping results are presented for various landscapes on interferometric data obtained from ERS-1 satellite over Ukraine and Switzerland regions.

An innovative IFSAR simulator is presented. It is based on an electromagnetic backscattering model of the scene and an accurate description of the IFSAR system impulse response function. A set of meaningful examples are also presented.

We present a new time-domain algorithm for weighted least squares phase unwrapping based on the finite element method. The proposed technique is specially designed for interferometric SAR applications; a noise-robust extension of the algorithm is also investigated. A number of experiments carried out both on simulated patterns and on real ERS-1/ERS-2 data validate the presented technique.

SAR interferometry is a promising tool to generate DEM out of SAR data taken from space. In the state of the arts technology of SAR interferometry, phase unwrapping looks only the problem to be resolved.. In phase noise free environment, phase unwrapping straightforward process to integrate local difference of phase. But this is quite rare case in actual interferometry due to the various phase noise source to distort fringes. In the integration of local phase difference, the effect of the noise is transferred to the neighboring area to jeopardize the estimated DEM. In this paper, I propose an interactive phase unwrapping method which works even under the poor fringe due to noise.

Coordinates in the 3D space of elements in a SAR image can be obtained by the combination of along-track, slant-range and interferometric fringe measurements. In order to evaluate the elevation of a pixel with respect to a slant- range reference plane, its absolute interferometric phase is required and this is typically derived unwrapping a 2D interferometric fringe pattern. Phase inconsistencies in SAR interferograms due to noise and topography determine unwrapping errors which appear as discontinuities in the computed absolute phase field. Phase aliasing arising from rapid phase variations from topography generates 2D unwrapping inconsistencies characterized by phase patterns statistically different from those induced by noise. In this paper, the spatial configurations of the phase field around residues is utilized in the phase unwrapping procedure. The feasibility of a neural network approach for classifying residual complex geometric phase patterns requiring different corrective measures is also presented. In addition, a method based on pseudo-differential interferometry to resolve residual inconsistencies as noise- or topography-generated is explored.

Temporal and spatial decorrelation effects are very important in repeat pass interferometric imaging of boreal forest. Temporal decorrelation refers to changes in the strength and distribution of scattering, due to the effects of wind, rain, and moisture changes of both canopy and ground. Spatial decorrelation is a result of baseline decorrelation, volume scattering, and uncompensated topography. These effects complicate interferometric imaging and the use of interferometry for topographic mapping of forest covered regions. The differences between heights measured from interferograms over forest and a DEM (the effective tree height) are related to the coherence. Another problem is the decrease in accuracy of coherence estimates (e.g. for land use classification) when the coherence is low. Influences of various factors on coherence in interferometry using images from the ERS-1 3 day repeat cycle of 1994 are studied. A wide range of time intervals between images and of baselines is used. The coherence of various surfaces is found to vary considerably between interferograms. As the time interval increases, the coherence generally decreases. The causes of decorrelation and their relation to meteorological effects and volume scattering are investigated. Analysis of coherence over various surfaces in Northern Sweden during the winter of 1994 is performed, with particular emphasis on forest, clear cut and agricultural areas.

An approach to 2D phase unwrapping for SAR interferometry is presented, based on separate steps of coarse phase and fine phase estimation. The coarse phase is constructed from instantaneous frequency estimates obtained using adaptive multiresolution, in which estimation is done of difference frequencies between resolution levels, and the frequency differences are summed over resolution levels such that a conservative phase gradient field is maintained. This allows a smoothed coarse unwrapped phase, which achieves the full terrain height, to be obtained with an unweighted least squares phase construction. The coarse phase is used to remove the bulk of the phase variation of the interferogram, allowing more accurate multilooking, and the resulting fine phase in unwrapped with weighted least squares. The unwrapping approach is verified on simulated interferograms.

This paper reviews the concept of noise in 2D phase unwrapping of SAR interferograms. It is shown that phase gradient estimates derived as wrapped phase differences of adjacent samples are biased, leading to an underestimation of phase slopes. Hence, linear estimators like least squares methods operating on such gradient estimates tend to globally distort the reconstructed terrain. The slope bias is quantified as a function of coherence and number of looks both theoretically and via simulations. The particular type of noise under discussion also may lead to impulse-like errors in the phase unwrapped by a linear method. In order to avoid these errors the support of reconstruction must be restricted in the same way as with so-called branch-cut methods.

Conventional SAR interferometers derive the surface height from an estimate of the phase difference between the SAR processed echoes received by two displaced phase centers. This paper introduces a maximum likelihood algorithm to process the SAR data from the three phase centers, which can be obtained from two antennas by switching the transmitter between the two. The accuracy of the new technique is derived and compared to the conventional one. It is shown that his new approach to SAR interferometry provides better accuracy, together with adaptivity to the look angle and possibly surface slope. Moreover it maintains the wide unambiguous height range corresponding to the shortest baseline.

ENVISAT-1 microwave radiometer (MWR) is an instrument designed and developed for the European Space Agency by the European Industry. The instrument will be part of the ENVISAT-1 satellite scientific payload. Alenia Spazio is engaged in the phase C-D as instrument Prime Contractor, responsible for design and development, leading an industrial consortium of European and American companies. The current design takes also benefits from Alenia Spazio activities as MIMR radiometer Prime Contractor in the frame of METOP ESA program. The MWR design concept derives from the experimental radiometers embarked on ERS-1 satellite.It is a two channels passive Dicke microwave radiometer, operating at 23.8 and 36.5 GHz. By receiving and analyzing the earth's generated and reflected radiation at these two frequencies, this instrument is able to measure the amount of water content in the atmosphere within a 20 Km diameter field of view immediately beneath the satellite's track. A two points calibration scheme is adopted with hot and old calibration reference points, so that periodically the measurements of earth scene radiation are interrupted to allow the measurement of an on-board calibration load and of the deep cold space. The MWR output products are of prime importance for wind/wave products of radar altimeter instrument part of the ENVISAT-1 payload, providing correction of atmospheric propagation data, but also for direct evaluation of brightness temperature to characterize polar ice, land surface properties and for sea surface temperature accurate measurements. Within this paper, after an overview of the instrument design concept, the MWR radiometric performance prediction is presented, with emphasis on the design and technology applied to radiometric receivers.

ENVISAT-1 microwave radiometer (MWR) is an instrument designed and developed for the European Space Agency (ESA) by European Industry as part of ENVISAT-1 scientific payloads developed by ESA. It is a two channels passive Dicke microwave radiometer, operating at 23.8 and 36.5 Ghz. By receiving and analyzing the Earth's generated and reflected radiation at these two frequencies, this instrument is able to measure the amount of water content in the atmosphere within a 20 Km diameter field of view immediately beneath the satellite's track. The MWR output products are of prime importance for wind/wave products of radar altimeter providing correction of atmospheric propagation data, for direct evaluation of brightness temperature to characterize polar ice and land surface properties and for sea surface temperature accurate measurements. In order to achieve the required accuracy and sensitivity performance, a two points calibration concept is adopted with hot and cold calibration reference points: periodically the measurements of earth scene radiation are interrupted to allow the measurement of an on-board calibration load and of the deep cold space. Given this on- board calibration architecture, a specific pre-flight calibration and characterization activity is necessary in order to develop a ground calibration and characterization philosophy has been developed by Alenia Spazio. In this paper, an overview of radiometric performance prediction is reported, with extensive description of the foreseen calibration and characterization activities. Moreover, a general description of in-flight calibration is provided.

The SIR-C/X-SAR SRL-3 topographic mapping mission was conceived by JPL to provide a global topographic mapping of the planet Earth and to acquire multi-frequency/multi- polarization SAR data over selected sites. The major benefits expected by this eleven-day shuttle mission, now officially called Shuttle Radar Topographic Mapper (SRTM), are to support many branches of the earth sciences providing an accurate knowledge of the surface elevation. To generate required data, SRTM will use a single-pass fixed baseline interferometer SAR system composed by the Shuttle Imaging Radar C and by the X-band SAR successfully flown twice in 1994, enhanced by two additional C-band and X-band receive- only antennae/receivers mounted on the tip of a 60 meters long retractable mast. The aspects related to the mission objectives and to design concepts of the interferometric channel of the X-SAR radar are dealt with in this paper. In particular the system configuration, the secondary azimuth beam steerable antenna, the front-end electronics and the on-orbit antennae misalignment's compensation are introduced. Finally the achievable performances are presented.

The Central Electronics Sub-Assembly (CESA) forms part of the advanced synthetic aperture radar (ASAR) to be included in the payload for ESA's ENVISAT-1 mission. The purpose of ASAR is to obtain measurements of the radar backscatter from the Earth's surface, allowing its imaging day and night and under all weather conditions. ASAR is composed by two separate subassemblies: an antenna-subassembly (ASA), which is a large deployable planar phase array containing distributed transmit/receive modules; the CESA, which provides all centralized control facilities enabling the ground control to select the desired instrument operation mode with the desired parameters set and keeping the ground control informed about ASAR detailed health status. From an RF signal point of view, CESA generates the transmit radar waveform with the selected characteristics providing it as C-band drive pulse to the ASA; CESA also downconverts the ASA received echo signal, digitizes, pre-processes and formats it prior to transmission to the platform DMS. Purpose of this paper is to provide an overview of the main CESA functions nd an outline of the main performances aspects. Architectural choices and constraints are also discussed.

The knowledge of the state of the atmosphere is limited and atmospheric spectroscopy from space is for many disciplines an indispensable tool for understanding the physics and chemistry of the atmosphere. In order to support the studies in the millimetric and sub-millimetric spectral range, the ESA has established a scientific group which has examined this problem and has come up with the conclusion that two missions can be defined. A stratospheric mission, fulfilled by an instrument comprising sub-millimetric channels and the MASTER mission. MASTER is a limb sounder which performs passive monitoring of the atmosphere at MM channel bands providing high performance radiometric/spectrometric measurements for determination of atmospheric profiles for H₂O, CO, O₃, HNO₃, SO₂, and N₂O. Recently, the scientific group has established that a strong priority should be put on exchange mechanisms between the troposphere and the stratosphere for a future atmospheric chemistry mission. For this reason, an enhanced version of MASTER has been proposed. This enhanced version of MASTER should be able to include the ClO measurement capability with an additional band. This work has been conducted under funding from the Earth Observation Preparatory Programme of the European Space Agency. Purpose of this paper is to provide an overview of the engineering activities performed in order to preliminary assess the MASTER instrument feasibility.

This paper briefly overviews the status of the research in the development of new altimeter systems presently carried out in Alenia Spazio S.p.A. in the frame of internal research activities and ESA feasibility study contracts. In particular, the concepts of synthetic aperture and interferometric altimetry or global ice/land topography are reviewed in this paper. These system designs are extremely promising, since they can overcome the limitations of the classic nadir-looking pulse limited systems. Conventional system in fact, in spite of the extremely high range accuracy achievable over oceans, are unable to provide topographic details over ice or land due to their coarse horizontal resolution, several hundreds od meters against the 100-300 m required in ice/land topography applications.

This paper gives the current status of a medium resolution SAR definition prephase A study. The objective of such a novel SAR instrument is the global monitoring of land surfaces and polar region. The study was conducted in order to analyze all the possible concepts that could be compliant with this mission definition. Several trade-offs were performed in order to select an instrument concept compatible with large coverage, medium spatial resolution, good absolute radiometric accuracy, low mass and low power consumption.

As a result of a pre-phase a study, founded by ESA, this paper presents the definition of a spaceborne Rain Radar, candidate instrument for earth explorer precipitation mission. Based upon the description of user requirements for such a dedicated mission, a mission analysis defines the most suitable space segment. At system level, a parametric analysis compares pros and cons of instrument concepts associated with rain rate retrieval algorithms in order to select the most performing one. Several trade-off analysis at subsystem level leads then to the definition of the proposed design. In particular, as pulse compression is implemented in order to increase the radar sensitivity, the selected method to achieve a pulse response with a side-lobe level below--60 dB is presented. Antenna is another critical rain radar subsystem and several designs are com pared: direct radiating array, single or dual reflector illuminated by single or dual feed arrays. At least, feasibility of centralized amplification using TWTA is compared with criticality of Tx/Rx modules for distributed amplification. Mass and power budgets of the designed instrument are summarized as well as standard deviations and bias of simulated rain rate retrieval profiles. The feasibility of a compliant rain radar instrument is therefore demonstrated.

This paper reports a system study of a constellation of spaceborne advanced SARs for high temporal, geometric and radiometric resolution mapping of the Earth surface. The study is aimed at the identification of overall performance and feasibility of an integrated remote sensing system to be deployed within the next fifteen years for nearly real-time high accuracy mapping, with special emphasis on natural disasters management. The SAR conceptual design includes the frequency identification and the quantitative evaluation of the main system parameters. Thanks to the capability of range beam steering, it is demonstrated that fast global access of whatsoever area on the Earth surface can be obtained by means of two sets of identical satellites, adequately located along sun-synchronous orbits. Finally, the spacecraft pointing knowledge requirements are pointed out, taking global fast access and target localization into account.

The use of interferometric SAR (IFSAR) to measure elevation is one of the most powerful and promising capabilities of radar. A properly equipped spaceborne IFSAR system can produce a highly accurate global digital elevation map, including cloud-covered areas, in significantly less time and at significantly lower cost than with other systems. For accurate topography, the interferometric measurements must be performed simultaneously in physically sperate receive system, since measurements made at different times with the same system suffer significant decorrelation. The US/German/Italian spaceborne imaging radar C/X-band SAR (SIR-C/X-SAR), successfully flown twice in 1994 aboard the Space Shuttle Endeavor, offers a unique opportunity for global multifrequency elevation mapping by the year 2000. With appropriate augmentation, SIR-C/X-SAR is capable of producing an accurate elevation map covering 80 percent of the Earth's land surface in a single 10-day Shuttle flight. The existing US SIR-C SCANSAR mode provides a 225-km swath at C-band, which makes this coverage possible. Addition of a C-band receive antenna, extended from the Shuttle bay on a mast and operating in concert with the existing SIR-C antenna, produces an interferometric pair. Accuracy is enhanced by utilizing the SIR-C dual polarizations simultaneously to form separate SCANSAR beams. Due to the practical limitation of approximately 60 meters for the mast length, the longer SIR-C L-band wavelength does not produce useful elevation measurement accuracy. IFSAR measurements can also be obtained by the German/Italian X-SAR, simultaneously with SIR-C, by utilizing an added outboard antenna at X-band to produce a swath coverage of about 50 km. Accuracy can be enhanced at both frequencies by processing both ascending and descending data takes. It is estimated that the 90 percent linear absolute elevation error achievable is less that 16 meters for elevation postings of 30 meters. This will be the first use of spaceborne IFSAR to acquire accurate topographic data on a global scale.

This paper initially summarizes the scientific requirements for a space borne cloud radar and different mission scenarios are categorized. The major system design constraints and their possible impact are then described in greater detail. A number of different concepts are then briefly described. The possible performance of an along track cloud radar is then estimated, this relatively simple concept being fairly typical of the type of concept that is likely to be first employed in orbit.