Bistatic radar is an active microwave sensor operating with separated transmitting and receiving antennae. This paper deals with a spacebome mission of a bistatic synthetic aperture radar based on a small satellite flying in formation with ENVISAT along parallel orbits, and equipped with a receiving only microwave system, which catches the ASAR echoes. The proposed system does not require any modification of existing ENVISAT design and operation; in fact it is assumed that ENVISAT is non-cooperative. After an introduction to the original scientific activities and applications achievable using bistatic data as further input thanks to the capability of a multi-angle observation, the paper presents numerical results of a simulation aimed at characterising some radiometric and geometric aspects of bistatic imagery. In particular, the effects of a frequency offset, caused by differences in local oscillators, and of variable bistatic geometry, caused by spacecraft separation along the orbit, are put in evidence.

The classical space borne radar altimeters have been flown on most of the remote sensing missions, for the primary task of determining the distance from the observed surface to the radar with a very high degree of accuracy: typical altimeters utilize wide bandwidth pulses to obtain a high spatial resolution. In this kind of instrument, known as pulse-limited (PL) altimeters the spatial resolution is limited by the pulse width and the orbital height to values of some kilometers: the same concept is applicable to bistatic altimeter. A bistatic remote sensing system consists of a constellation of satellites flying at the same altitude with an operating geometry such that the incidence and scattering angles are equal. Usually opportunity sources could be used as GPS and Glonass. Moreover, when encountering rougher surfaces, with prominent features and non zero slopes, such as land surfaces, coastal regions, ice sheets, etc. the poor spatial resolution inherent in the pulse limited approach becomes inadequate to the observed surface and the altimeter performance degrades severely. In order to overcome these problems the spatial resolution can be improved at least in the along track direction, adapting well-known processing techniques developed for Synthetic Aperture Radar (SAR) (Doppler Beam Sharpened concept) by low-pass filtering the received echoes in the Doppler frequency domain. The pulse limited radar altimeter concept could be applied also to obtain penetration capability in radar sounder. Moreover the detection of a subsurface interface will be possible only if the following conditions are met: • the level of the subsurface reflection is higher than the noise floor • the surface/subsurface dynamic is included in the system dynamic range • the subsurface reflection is higher than the corresponding surface clutter reflection In many cases the limits of the penetration depth will be imposed by the surface clutter level (the last of the list above), which can be found through the evaluation of the depth at which the subsurface power is equal to the surface clutter power. This will be detailed in this paper, taking into account some typical surface and subsurface model in planetary application. Obviously in presence of rough surfaces the dynamic range is strongly reduced by the surface clutter. To increase the detection performance against surface clutter, the SAR-DBS concept could be also applied: as it will be demonstrated the Doppler azimuth processing significantly reduces the surface echoes coming from along track off nadir reflections. In this paper the SAR-DBS concept will be discussed and its performance and the optimum radar parameters will be evaluated.

We present a new algorithm for processing hybrid strip-map/spotlight SAR data which is based on a two-step focusing strategy. The first step performs an azimuth raw data filtering operation implemented via the (azimuth) convolution between the SAR data and a chirp signal with a properly chosen constant chirp rate. Following this step, the spectral folding of the azimuth raw signal spectrum is resolved and the space-variant characteristics of the system transfer function are preserved. Therefore, we can achieve the full focusing of the data by a conventional strip-map processing step implemented in frequency domain and requiring only a minor modification. The proposed algorithm is simple, efficient and has the unique feature of being suitable for processing strip-map, spotlight and hybrid strip-map/spotlight mode, the first two being particular cases of the more general last one. The presented experiments are carried out on simulated data relative to the COSMO/SkyMed sensors operating in the hybrid strip- map/spotlight mode; these results clarify the rationale of the proposed approach and confirm its validity.

Saturn and its largest satellite, Titan, are the principal objectives of the NASA-ESA-ASI Cassini-Huygens mission. Launched in 1997, the spacecraft will reach its destination in July 2004 and the mission will end in 2008, after 44 Titan flybys. A Ku-band radar, in particular, will investigate the nature of Titan surface, using four operative modes: imaging radar, scatterometer, altimeter and radiometer. During the Earth flyby, in August 1999, Cassini Radar acquired radiometric and scatterometric data over Pacific Ocean and South America for calibration purposes. These data have been compared with the co-located X-SAR, Seasat, other sensors' and reference database, retrieving information for Cassini Radar calibration. Furthermore, an e.m. simulation is applied in order to assess the possibility of radar discrimination among three different surface scenarios expected for Titan: oceans or lakes of ethane, water ice or ammonia-rich ice. The surfaces have been simulated using fractals, described as 3-D Fractional Brownian Motion processes, and their e.m. response has been calculated using the Kirchhoff approximation. The results indicate a good possibility of discrimination because of the higher sensitivity of backscattering coefficient to dielectric constant variations than to surface roughness. For very smooth surfaces, liquid methane in absence of wind, signals at the low limit of the radar detectivity are expected.

Speckle reduction in synthetic aperture radar (SAR) images is a key point to facilitate applicative tasks. A filter aimed at de-speckling should energetically smooth homogeneous regions, while preserving point targets, edges, and linear features. A trade-off, however, should be arranged on textured areas. Filtering capabilities depend on local image characteristics, and generally no filter outperforms the others in every situation. In this work, a set of adaptive filters is considered with attention to those oriented towards a multiresolution approach. Images are individually processed by each filter, and the output at each pixel position is obtained by choosing one out of the channels. The selection is based on thresholding a pixel feature based on a novel definition of inhomogeneity for SAR images and capable of describing both textured areas and ideal scatterers. Results on true SAR images show that also an empirical choice of thresholds is non-critical: the proposed scheme outperforms each filter individually, at least according to visual criteria. Aperture Radar (SAR), texture.

Recently, there is a growing interest in Scanning SAR systems (ScanSAR) for their wider swaths. In this framework, efficient algorithms for generating ScanSAR images from raw data are demanded for browsing purposes or for on-board image generation. The SPECtral ANalysis method (SPECAN) and its variations are very appropriate for efficient SAR processing in both range and azimuth, when no full resolution is demanded. In the case of azimuth ScanSAR, that condition is naturally met due to the burst mode that does not acquire the whole Doppler spectrum. However, several problems arise due to the spectral analysis operation and its practical implementation. On the other hand, matched filtering for range processing presents a different scenario related to SPECAN processing because the signal contains a fixed quadratic phase, but different amplitude envelope. This work studies the processing of raw SAR data by the SPECAN techniques in both range and azimuth dimensions. The main contributions of this study are the analysis of the signals and the identification of their practical limitations related to SPECAN processing. The results show that SPECAN is a very promising method for efficient two-dimensional ScanSAR image generation when a specially designed algorithm concerning the signal dimension is applied.

This paper deals with the analysis of SAR imagery of the Mediterranean Sea to estimate the directional wave spectrum and the wind vector. As case study an ERS-2 SAR acquired on 13 November 1997 (orbit 13417, frame 2889) which includes Lampedusa Island in the Sicily Channel was selected. Lampedusa was chosen as test site because of its privileged location in the centre of the Mediterranean and because it hosts a fully equipped meteorological station. Besides, the selected SAR image shows a striking feature from which the wind direction can be reliably estimated.Wave field and wind vector from SAR image were compared with predictions from the WAM wave model and the wind output of the ECMWF atmospheric model, respectively. The retrieval of directional two-dimensional wave spectrum from SAR image was carried out by means of the classical Hasselmann & Hasselmann inversion scheme and the SAR image cross-spectrum methodology, respectively. Assuming the wind direction is known independently, SAR data was then analysed to retrieve the wind speed by using the predictions from empirical backscatter models, such as CMOD4 and CMOD-IFREMER. Wind vector retrieval results were validated against in situ measurements provided by the Lampedusa airport anemometer.

Feature-based automatic target recognition (ATR) discriminates between target classes on the basis of the values taken by certain target features. The conventional approach is to select the best features for a particular task from a large set of features which have been pre-defined on the basis of physical intuition. A simple feature might be target area whilst a more sophisticated feature might be some measure of fractal dimension. ATR performance will be influenced by the choice of features and by the accuracy with which the statistical behaviour of these features has been characterised. This paper describes a technique which can be used to determine statistical feature behaviour despite limited examples of target realisations. It also addresses the problem of feature choice by introducing a method for assessing the information carried by different features. This leads to a potential technique for adaptive feature generation. These ideas are illustrated by application to synthetic aperture radar (SAR) images of vehicles.

Due to the enormous quantity of radar images acquired by satellites and through shuttle missions, there is an evident need for efficient automatic analysis tools. This article describes unsupervised classification of radar images in the framework of hidden Markov models and generalised mixture estimation. In particular, we show that hidden Markov chains, based on a Hilbert-Peano scan of the radar image, are a fast and efficient alternative to hidden Markov random fields for parameter estimation and unsupervised classification. We also describe how the distribution families and parameters of classes with homogeneous or textured radar reflectivity can be determined through generalised mixture estimation. Sample results obtained on real and simulated radar images are presented.

Automatic contour detection in SAR images is a difficult problem due to the presence of speckle. Several detectors exploiting the statistics of speckle in uniform regions have been already presented in literature. However, these were mainly applied to multi-look low-resolution imagery. This paper describes two new CFAR contour detectors for high-resolution single-look polarimetric SAR images. They are based on multi-variate statistical hypothesis tests. Failing of the test indicates the presence of an edge. A test for difference in means on log-intensity images and difference in variance on complex (SLC) images are used. Both tests take into account the interchannel covariance matrix which makes them a powerful tool for contour detection in multi-channel SAR images. Spatial correlation jeopardizes the CFAR character of the detectors. This problem is often neglected. In this paper its influence on the detectors is studied and eliminated. The localisation of detected edges is improved using a directional morphological filter. Different methods to fuse the results of the two detectors are explored and compared. Results obtained on a polarimetric L-band E-SAR image are presented. Most contours are well detected. Narrow lines on a uniform background remain undetected. Although the detector was developed to detect edges only between uniform areas, it also detects edges between textured and uniform areas.

In this paper, a new linear features (LF) extraction method is proposed to update old geographical maps using Synthetic Aperture Radar (SAR) data. This method is dedicated to the context of tropical rain forest where LF such as roads or railways often appear as long segments poorly contrasted and partially hidden in a homogeneous environment. Most classical SAR road extractors operate locally in the spatial domain using a point to point approach that does not take into account the “a priori information” provided by old maps. Moreover, often developed in a different context, they are not well adapted to the extraction of long segments which are most of the time difficult to follow, even for trained human operators. To overcome these problems, the proposed method is based on the Localized Radon Transform (LRT) which performs limited boundary geometrical integrals along straight lines. In the transformed domain, LF have a specific signature: they appear as strongly contrasted structures, easier to extract with a constant false alarm rate operator: the ratio of local mean. The “a priori information” is integrated by two parameters issued from the old map: the approximate direction angle and the minimum length of the LF. Experimental results show the robustness of this method with respect to the poor radiometric contrast and hidden parts. According to these results, this method can be proposed as a complementary tool to detect LF in SAR images.

There has been growing research in the area of coastline detection using SAR images over the past few years. In this paper we propose a novel coastline extraction method based on wavelet packets, multiscale segmentation and a Markov Random Field regularization . Numerous spatial domain classical algorithms currently failed in the discrimination of Water and Ground when the contrast within the pixels values is low. Suitable wavelet packets informations features provides a good tool for distinguishing between textures. Utilizing the inherent tree structured of wavelet packets, a multiscale texture segmentation based on the fuzzy C-means algorithm is performed at different scales. The aboved multiscale segmentation are fused using a Markov Random Field regularization in the features domain for the final extraction of the coastline. The experimental results show the performance of the method, we can visualy evaluated the improved quality of the coastline extracted compared to classical algorithm based on image domain. Somes results are presented with ERS SAR images.

The objective of this work is to develop a method to use radar scatterometer data and a hydrological model in order to retrieve soil behaviour at a level greater than C-band microwave penetration depth. For microwave measurements a C-band FM-CW scatterometer has been employed in two campaigns; the device is able to provide backscattering coefficients in the range of+10 dB and -40 dB for incidence angles between 10° and 60°. Subsequently, microwave scatterometer data have been analysed to estimate their sensitivity to the soil moisture patterns of topsoil comparing them with ground truth measurements. For the validation of these radar data, a coupled heat and moisture balance model has been run to predict the hydrological behaviour of the same topsoil starting from point ground truth measurements. In a second run, soil moisture values derived from scatterometer data should have been used for the initialisation of the model. First attempts have been carried out to propagate the surface physical parameters to unreachable soil layers, such as vertical soil moisture profiles.

We present a supervised three-stage mapping (labeling) scheme applied to SAR images of polar regions for detecting frazil/pancake ice and open water. The three-stage labeling procedure consists of: 1. a speckle noise filtering stage, based on a sequence of contour detection, segmentation and filtering steps, which removes SAR speckle noise (and texture information as well) without losing spatial details; 2. a second stage providing Bayesian, maximum-a- posteriori, hierarchical (coarse-to-fine), adaptive (data-driven) and contextual labeling of smooth images featuring little useful texture information, i.e., piecewise constant or slowly varying intensity images that may be corrupted by an additive white gaussian noise field independent of the scene; and 3. an output stage providing a many-to-one relationship between second stage output categories (types or clusters) and desired output classes. Modules 1. and 2., which demonstrated their validity in several applications in the existing literature, are briefly recalled in the current paper. The proposed labeling scheme features some interesting functional properties when applied to sea-ice SAR images: it is intuitive to use, i.e., it requires minor user interaction, is robust to changes in input conditions and guarantees satisfactory classification performances. Application results are presented and discussed for a pair of SAR images extracted from an an ERS-2 scene of the East Greenland Sea, Odden Ice Tongue region, acquired on March 8, 1997, at the time when a field experiment by the research vessel “Jan Mayen” was conducted in the same area.

Two techniques suitable for detecting non-periodic backscatter structures in the SAR images are presented: the Variable Interval Space Averaging (VISA) and the two-dimensional Continuous Wavelet Transform (CWT2) analyses. Both methods have been tested over SAR images taken under different geophysical situations. Despite these techniques require the definition of subjective parameters and the knowledge of the spatial scales of interest, the results indicate that they succeed in the identification of the non-periodic backscatter structures present in the SAR images, which may be referred to the imprint of the atmospheric boundary layer. This will allow the quantitative estimate of the size, number and orientation of the backscatter structures. On the contrary, when periodic structures as the wind rolls are present, only the CWT2 yields good results. An interesting development of these technique will consist of the possibility to distinguish atmospheric from oceanographic features.

Purpose of the present paper is to investigate the possibility to reconstruct strongly discontinuous terrain height profiles, starting from more than one wrapped interferometric phase signals obtained at different working frequencies. In absence of phase noise, the use of two frequencies that are in inational ratio allows to have a unique solution. The presence of the decorrelation noise, however, makes such methods not applicable in practical cases. We propose an unwrapping method based on a Maximum Likelihood estimation technique and using frequency diversity. Since it does not exploit the phase gradient, it assures the uniqueness of the solution, also in the case of piece-wise continuous elevation patterns with strong discontinuities. This result is derived from the consideration that the likelihood function obtained combining frequency diversity information exhibits a unique global maximum.

In this paper we evaluate a combined phase unwrapping method that improves the results in difficult areas (containing noise and discontinuities) taking advantage of the Region Growing (RG) and Weighted Least Mean Square (WLMS) algorithms. The performance of the combined method is based on the following steps. Starting from the wrapped phase, a binary mask is calculated using a first RG solution, in order to generate a mask which is able to consider low-quality pixels. Afterwards, the WLMS solution is calculated using the wrapped phase and the RG mask. To obtain this WLMS solution it is not necessary to compute many iterations, since the objective of this step is just to ease the subsequent RG process. Therefore, the calculation time is smaller than with a WLMS solution separately, because it needs a larger number of iterations to converge. As commented, at this stage the RG algorithm is applied in order to unwrap the error of the WLMS solution, which is expected to have less noise than the input interferogram. Therefore, the RG is able to handle its values in a relatively easy manner. Finally, the first WLMS solution and the unwrapped error are added to obtain the desired unwrapped phase.

Automated phase unwrapping of a highly incoherent differential interferogram of the Arthur’s Pass earthquake of 1994 has illustrated many challenges to the use of interferometric SAR in the New Zealand environment. Though severe decorrelation between interferometric pairs is common in New Zealand, processing of images for differential interferometry is motivated by the geologically active landscape. The weighted full multigrid (WFMG) phase unwrapping method of Pritt has been implemented for its robust, global operation. This paper examines the opposing requirements to block out the distorting effect of incoherence, and yet to retain valuable connections between coherent regions. Using both synthesised examples and the Arthur’s Pass differential interferogram to illustrate, we propose a modification to the WFMG algorithm, which improves its performance for highly incoherent interferograms.

Phase unwrapping (PU), i.e. the retrieval of absolute phases from wrapped, noisy phase measurements, is a tough problem which arises in various realms of signal processing. Generally, the adopted PU strategy consists in integrating the estimated phase gradients or instantaneous frequencies (IFs). The standard IF estimation technique used by most PU algorithms is the simple wrapping-of-the-wrapped-phase-gradient rule. However, this rule results in a strongly biased estimations as the noise level grows. The idea on which this paper relies consists in locally approximating the interferogram by a sum of complex exponentials. The algorithm described in this paper makes use of the Matrix Pencil (MP) method for estimating the parameters of these exponentials, i.e. their amplitude and frequency. The proposed algorithm is applied to the unwrapping of noisy synthetic interferograms. The results are compared with those obtained unwrapping the same interferograms by means of the classical wrapping-of-the-wrapped-phase-gradient rule.

This paper describes the use of the wavelet transform for interferometric noise filtering in synthetic aperture radar (SAR). The interferometric phase noise is characterised by an additive noise model. This noise is also non-station ary, therefore some filtering schemes use a windowing process to take into account this behaviour, but the performance of the noise removal process will be highly related with the dimensions of the window. A new approach to solve this problem is presented. This new algorithm is based on the wavelet transform, allowing to process the phase without using the windowing process. The interferometric phase is not processed directly, but in the complex plane using a complex number containing the same phase information. This approach allows to maintain the phase jumps that are very important in the unwrapping process. A new noise model is proposed both in the original and in the wavelet domain. Using this noise model, the noise removal algorithm is presented. Results, using both synthetic and real phase images, are shown. Parameters that characterize the phase signal, as the number of residues and statistical parameters are also presented. The results show that there is a clear improvement in the phase signal.

One of the main limitations on the use of SAR interferometry to automatically generate Digital Elevation Models (DEM) is related with both the accurate calibration of the system parameters and its stability from flight to flight. The unstable movements of an airborne SAR platform can be corrected during the processing step, as long as those movements are recorded in an accurate manner. Errors and time drifts in the system parameters or on the plane position and attitude measurements lead to geolocation errors in the final DEM. A calibration method based on the so-called “sensitivity equations”, which relate the target location error with the error on the estimation of the system parameters, is described. The equations have been derived by differentiating the interferometric geolocation equations in closed form for the most general case in a squinted geometry. The interferometer parameters considered are the baseline length and elevation, common time delay, interferometric constant phase, plane position and attitude angle offsets, etc. The equations can be used to both calibrate a single DEM from different ground control points (GCP) spread along the swath and the frill interferometer from well-known located comer reflectors (CR).

This paper presents a detailed study of geocoding algorithms for interferometric SAR data from airborne sensors. They allow to relate a phase value in slant-range coordinates to a position in a cartesian reference system and, hence, a height value on a topographic map. Two basic approaches, which follow forward or backward strategies, are proposed in this paper. Another aspect is related to the kind of the acquisition mode: single- or repeat-pass. In general, a wide variety of techniques and combinations (single or repeat pass, direct or indirect transformations, one or two steps, zero-Doppler or squinted geometry) exist. This paper reviews the state-of-the-art of these combinations, as well as their evaluation, comparison and discussion by means of real data from the DLR’s E-SAR system. The discussion includes the problem of polarimetric data geocoding. In that case, the map-projected height information derived from a X-band single-pass system is used to geocode non-interferometric channels (for example polarimetric L-band data).

The objective of the present study is to evaluate the performances of a series of SAR ERS images for a land cover classification of a Mediterranean landscape, focusing on the discrimination of vegetation types. We tested the contribution of multitemporal data and contextual methods of classification with and without filtering for land cover discrimination. An index of temporal change was developed to characterise the stability of land covers, this index is based on the mean normalised difference between consecutive dates. This study shows the importance of time series of ERS sensor and of the vectorial MMSE filter based on segmentation, for land cover classification. Fifteen land cover classes, where eight of them concern to different vegetation types, have been classified obtaining a 80.1 % of mean producer’s accuracy for 1998 series, and 70.6 % for 1994. These results are comparable with those from two-date SPOT images (85.3 % of mean producer’s accuracy).

Three measurements are required to reconstruct the topographic information by means of Synthetic Aperture Radar Interferometry (InSAR): range, azimuth, and elevation. The first is obtained by timing the return of the radar pulse, the second by observing its Doppler frequency shift, and the third by measuring the phase difference between signals recovered at the spatially displaced antennas. In this paper a new general scheme for the geolocation of InSAR information is presented. It avoids the use of an Earth model and exploits the full information of a SAR interferometer: orbit data, range and Doppler frequency shift of each SAR image, and interferometric phase. Two geolocation algorithms are obtained within this scheme. The former studies the geolocation as the intersection of five surfaces defined by measurements of range, Doppler frequency shift and interferometric phase. In particular, the five surfaces are: the range spheres centered at the SAR antennas which is also where the Doppler frequency shift cones are located. The interferometric phase adds another surface - a phase hyperboloid - whose axis of symmetry is the interferometer baseline. These five surfaces intersect at two locations in space. One of them is the geolocation of image pixel. The last geolocation algorithm is based on the solution of a set of four equation: the range spheres and the Doppler frequency shift cones of the two SAR images. An exact closed-form solution is obtained. This solution does not rely on approximations and avoids the use of iterative algorithms. This results in a reduction of the computational load. Moreover, the proposed algorithms give a scheme for computing the geolocation accuracy.

This paper describes a Synthetic Aperture Radar (SAR) phase processing system and an automated method for estimating baseline length and its inclination angle from only pair of Single Look Complex (SLC) images to be interfered. The information about the baseline parameters are indispensable to produce accurate Digital Elevation Model (DEM) without geometrical distortion and also accurate displacement from differential Interferometric SAR (InSAR) processing. Only the nominal value of the baseline length is currently provided. The fixed value, however, does not represent nonparallel orbits from which raw interferogram is obtained. The ambiguity causes the geometrical distortion in DEM and the apparent displacement in the result of differential InSAR processing. In order to produce the accurate DEM, we have to know the baseline parameters as the function of the azimuth line. The proposed method derives coefficients of both functions for baseline length and for inclination angle by fitting the local disparities to a geometrical model. The proposed method was successfully applied to an actual InSAR data. The baseline estimated by the proposed method reduced apparent displacement to 1 /10 of that yielded by the nominal baseline. A piecewise co-registration method is also described for obtaining highly accurate interferogram.

SAR images has already been successfully exploited for the detection of changes in a scene. Being the backscatter intensity affected by the presence of wind fields, water and flood identification could be unreliable, unless weather information are integrated. On the contrary, interferometric coherence, usually low in presence of water, is not sensitive to weather conditions. As a consequence, the additional information provided by the absence of coherence over water should allow a more accurate identification of flooded areas. A qualitative and quantitative evaluation of the improvement that could be obtained by exploiting interferometric data has been performed. The data set is composed by interferometric pairs acquired by the ERS-l/ERS-2 satellites before and during the Yangtze River flooding occurred in China in summer 1998. Starting from these data, several features have been computed and associated to the channels of an RGB image, in order to obtain an intuitive interpretation of the data content and an easy identification of the flooded areas. The results show that, in order to correctly highlight the flooded areas the best combination of features includes the coherence difference between the acquisitions before and during the flooding, the backscatter intensity in the reference period and the coherence computed during the flooding.

The capabilities of Interferometric SAR (IFSAR) to provide Digital Elevation Model is well known and have been highlighted with the recent Shuttle Radar Topographic Mission. Nevertheless, layovers, shadows, multiple bounces and surfaces discontinuity strongly reduce the accuracy of the extracted shapes in urban areas with high-resolution systems. Due to the number and the complexity of uncontrolled interactions at work, we choose to use simulations to study the layover produced by buildings. An interferogram simulator developped at CNES, IRIT and CS, which is called 2SIR, can achieve those. This tool has been designed in order to simulate realistic interferograms (and its pair of radar images) in such complex situations as urban areas. C. Prati et al. have shown that a slope induces a spectral shift of the ground response. Although it generates a loss of coherence, it can be used to partially separate layover and non layover areas as they done with ERS images. In high- resolution, the spectral shift is insufficient to apply such a filtering. However, this technique can be achieved in urban area with certain restrictions to extract the shape of buildings.

Modern very high resolution interferometric SAR sensors deliver slant range magnitude and ground range interferometric height and coherence measurements at pixel sizes of 30cm to 10cm from a single flight path. Detection and reconstruction of buildings get feasible from this kind of data. Single data sources are corrupted by blur, speckle noise and other view dependent effects as e.g., layover and shadows. Especially in case of buildings, these phenomenological features provide also valuable information about the underlying building structure. In this paper we use information from the interferometric height and coherency channel to detect buildings. Shadow information from slant range magnitude images is then used to delimit the exact boundaries of the buildings further and rectangles are fit to the selected points. The resulting building models are input into a simulator to produce slant range magnitude images and interferometric height information. The layover and shadow regions from these simulated images are compared with the corresponding regions in the original data to detect occlusions of adjacent buildings and to further refine the building structure. The results are compared to ground truth data available from optical imagery. Accuracies achieved in the measurement of building dimensions are in the range of 3 pixels.

Synthetic aperture radar (SAR) differential interferometry allows, in principle, to measure very small movements of the terrain. Main limitations of this technique include decorrelation noise and atmospheric artifacts that can affect SAR differential interferograms. In this paper we show that the problem of decorrelation noise can be efficiently faced by using a new phase unwrapping approach that allows to process sparse data, and that the impact of atmospheric artifacts can be minimized by performing a temporal analysis of the deformations observed in successive SAR differential interferograms. Also, in this study we show that it is possible to perform a temporal analysis of slow terrain movements by using a rather limited number of ERS SAR data set and low precision topographic information. As an application of the proposed technique, the subsidence phenomena occurring in Bologna (Italy) and in the surrounding area from 1992 to 1999 are analyzed.

In this paper we consider the detection of a moving point target using two SAR antennas mounted on the same aircraft and spatially separated in the along track (cross range) direction. We consider two cases: conventionally quantized raw data and signum coded (SC or one bit coded) raw data. The performance of the presented along track interferometric system is evaluated by comparing the probability of false alarm and the probability of detection obtained in the two cases. Numerical results show that is possible to achieve comparable performances for both coding techniques.