The overarching goal of the InfoTerra/TerraSAR Initiative is to establish a self-sustaining operational/commercial business built on Europe’s know-how and experience in space-borne Synthetic Aperture Radar (SAR) technology, in SAR data processing as well as in SAR applications. InfoTerra stands for a new business concept based on supplying innovative geo-information products and services. TerraSAR is a space and ground system conceived to consist of an initial deployment and operation of 2 Radar satellites (one in X- and one in L-band) flying in a tandem configuration in the same orbit. The design of TerraSAR is driven by the market and is user-oriented. TerraSAR is key to capturing a significant proportion of the existing market and to opening new market opportunities, when it becomes operational. The InfoTerra/TerraSAR Initiative has evolved gradually. It started in 1997 as a joint venture between German (DSS) and British (MMS-UK) space industry, strongly supported by both space agencies, DLR and BNSC. In early 2001, DLR and BNSC submitted to ESA the Formal Programme Proposal for InfoTerra/TerraSAR to become an essential element of ESA’s Earth Watch Programme. In summer 2001, when it became evident that there was not yet sufficient support from the ESA Member States to allow immediate start entering into TerraSAR Phase C/D, it has been decided to implement first a TerraSAR consolidation phase. In early 2002, in order to avoid further delays, a contract was signed between DLR and Astrium GmbH on the development of one component of TerraSAR, the TerraSAR-X, in the frame of a national programme, governed by a Public Private Partnership Agreement. Even if now the different launch dates for TerraSAR-X and TerraSAR-L are narrowing down the window of common data acquisition, it is a reasonable starting point, but it should always be kept in mind that the utmost goal for the longterm is to achieve self sustainability by supplying geo-information products and services, mainly derived from combining high spatial resolution X-band data with full polarimetric L-band data and thus addressing a broad range of scientific, institutional/governmental and private applications.

The TerraSAR-X is a German national SAR- satellite system for scientific and commercial applications. It is the continuation of the scientifically and technologically successful radar missions X-SAR (1994) and SRTM (2000) and will bring the national technology developments DESA and TOPAS into operational use. The space segment of TerraSAR-X is an advanced high-resolution X-Band radar satellite. The system design is based on a sound market analysis performed by Infoterra. The TerraSAR-X features an advanced high-resolution X-Band Synthetic Aperture Radar based on the active phased array technology which allows the operation in Spotlight-, Stripmap- and ScanSAR Mode with various polarizations. It combines the ability to acquire high resolution images for detailed analysis as well as wide swath images for overview applications. In addition, experimental modes like the Dual Receive Antenna Mode allow for full-polarimetric imaging as well as along track interferometry, i.e. moving target identification. The Ground Segment is optimized for flexible response to (scientific and commercial) User requests and fast image product turn-around times. The TerraSAR-X mission will serve two main goals. The first goal is to provide the strongly supportive scientific community with multi-mode X-Band SAR data. The broad spectrum of scientific application areas include Hydrology, Geology, Climatology, Oceanography, Environmental Monitoring and Disaster Monitoring as well as Cartography (DEM Generation) and Interferometry. The second goal is the establishment of a commercial EO-market in Europe which is driven by Infoterra. The commercial goal is the development of a sustainable EO-business so that the e.g. follow-on systems can be completely financed by industry from the profit. Due to its commercial potential, the TerraSAR-X project will be implemented based on a public-private partnership with the Astrium GmbH. This paper will describe first the mission objectives as well as the project organisation and major milestones. Then an overview on the satellite as well as the SAR instrument is given followed by a description of the system design. Finally the principle layout of the TerraSAR-X Ground Segment and some remarks on the European context are presented.

The Infoterra/TerraSAR initiative started as an industrial concept to provide X- and L-band SAR data products from a pair of spacecraft in Sun-synchronous orbit. The mission was proposed by the BNSC and DLR for implementation as an element of the ESA’s Earth Watch programme. The X-band element evolved into a German national programme between DLR and Astrium GmbH, whereas the TerraSAR-L System has become the subject of a phase B consolidation study led by Astrium Ltd. in the UK and involves companies in several other ESA member states. The TerraSAR-L System comprises a single spacecraft carrying a large, fully polarimetric L-band SAR, and a ground segment architecture that will re-use existing facilities and technologies as much as possible. The service segment, sized to meet the mission’s requirements, complements this. Besides having a commercial role for the provision of geoinformation products, the TerraSAR-L System will serve the scientific user community, particularly with regard to Earth science use, and for contributions to the GMES initiative. Specification of the L-SAR instrument and ground segment performances has been guided by careful analysis of the product requirements so as to meet the user communities’ needs without over-design of the hardware. Extensive trade-off studies have proven that the L-band spacecraft should adopt the innovative 'Snapdragon’ configuration, minimizing technical risk and cost whilst offering comfortable design margins. The TerraSAR Consolidation has commenced and will involve a wide industrial community in the specification and definition for the TerraSAR-L System. Key instrument and Snapdragon risk reduction activities are well advanced under ESA contract, with completion expected in 2004.

The TerraSAR X and L-band satellites have been specified from the earliest requirements to work as a pair, delivering quasisimultaneous data, designed to meet commercial and institutional needs for geo-information. Early work was concentrated on the generation of products to meet commercial needs, for applications such as commercial agriculture management, utilities and risk assessment. Since the advent of the GMES initiative, it has been possible to envisage a sustainable EO system providing for European institutional needs, as well as delivering commercial revenues. This paper will focus on the results of current work, particularly addressing the specification of joint X- and L-band derived geo-information products. It is very likely that the X-band satellite will become operational some years before its L-band partner, however. Therefore, the development of pre-cursor products and services, using supplementary EO data is important. C-band SAR imagery, from Envisat and Radarsat, along with L-band ALOS data and optical imagery is most likely to be used in the derivation of these products. It is also likely that, airborne data will have a role to play during this development period. The paper will also illustrate the high-level architecture of the TerraSAR Exploration Service Infrastructure, outlining the end-to-end TerraSAR delivery chain.

After the success of Europe’s SAR satellites, ERS-1/2 and Envisat ASAR, of the German programs X-SAR culminating in the Shuttle Radar Topographic Mission (SRTM) and because of the on-going implementation of the nationally funded TerraSAR-X program the German Aerospace Centre, DLR, initiated and funded a study on future needs for spaceborne SAR-systems (SAFARI-Study). In this paper the most important results w.r.t. market needs, possible new SAR systems and required technologies are outlined.

MINISAR is a compact airborne interferometric SAR potentially suitable for many applications but mainly finalized for the production of technical topographic maps and monitoring the evolution of landslides events and assessing their extension and risk area. The program is co-funded by the Italian Ministry for Education, Universities and Research (M.I.U.R.) The hardware consists in an airborne X-band radar, able to obtain a resolution less than a meter (because of a 280 MHz stepped chirp signal) and an altimetric accuracy less than 7 meters. Such an accuracy derives from an equivalent 1.5 meters baseline and the high gain antennas that let MINISAR to use a transmitted power of only 80 W. The system will be mounted on board of a small platform and it is thought to have future development for unmanned platform. Data will be processed using a chirp scaling algorithm in order to obtain the two Single Look Complex (SLC) images which can be then processed to obtain high accuracy Digital Elevation Model (DEM).

Pulse compression radar is used in a great number of radar applications. Excellent range resolution and high ECCM performance can be achieved by wide-band modulated long pulses, which spread out the transmitted energy in frequency and time. By using random noise as waveform, the range ambiguity can be suppressed as well. The same limit in doppler resolution is achieved as for a coherent doppler radar when the time compression of the reference is tuned to that of the target. Mostly, the random signal is transmitted directly from a noise generating HF-source. A sine wave, which is phase or frequency modulated by random noise, is an alternative giving similar performance but higher transmitted mean power when peak-limited transmitters are applied. A narrower modulation noise bandwidth can also be applied to generate the same output bandwidth. For phase modulation, the bandwidth amplifying factor is simply the rms value of the phase modulation, and for a frequency modulating waveform the output rms bandwidth equals the rms value of the frequency modulation. The results also show that the range sidelobes can be highly suppressed compared with the sidelobes of the modulating signal. The mean and variance of the correlation integral are derived in terms of the autocorrelation function of the modulation. Finally, random bi-phase modulation and the effects of low-bit ADC at the correlation processing are analyzed and described. The advantages of low range sidelobes and enhanced range resolution make frequency and phase modulation attractive for a great number of applications.

Some recently developed algorithms known as Non-Uniform FFT's (NUFFT), which enable the computation of efficient FFT's with unequally spaced data in the time or frequency domain, have been applied to SAR imaging in this study. The main objective has been to analyze the potential improvement of the computational efficiency and/or image accuracy of seismic migration SAR processing techniques, like the ω-k algorithm. Our approach consists in substituting both the Stolt interpolation and the final range inverse FFT by a single NUFFT. Numerical simulations illustrate the performance of the new method and the influence of the selection of NUFFT parameters in the precision and computation time of the SAR imaging algorithm. The new method is especially suited for near-field wide-band configurations, such as inverse SAR (ISAR) and ground-based systems, where a very precise imaging algorithm is required.

Standard SAR-processing methods are based upon the assumption of a scene at rest. If targets are moving, their positions in the SAR-image are translated in azimuth and defocusing may occur. In this paper, some methods for the detection and imaging of moving targets and the estimation of their real positions are discussed, such as monopulse and DPCA. Experimental results are shown on position correction using the monopulse ratio of two SAR-imagery derived from the Σ and Δ-channels.

The presented work aims to develop new methods for exploiting the potential of future SAR satellite systems. The current paper focuses on the detection of linear objects (e.g. roads, rivers, tree lines, etc.) in multi-frequency polarimetric SAR images. We obtained sets of polarimetric P-band and L-band and VV-polarised C- and X-band images. The images cover the same region but have a different spatial resolution. We also obtained transformation matrices that relate the slant-range coordinates to geocoded coordinates for each frequency band. The detection of linear features is performed on each of the slant-range images and the results are then geocoded and fused. In SAR images, for deciding whether a line passes through a given point, a relatively large neighbourhood has to be considered because of the speckle. Normally a set of rectangles is scanned over the image and at each point the statistics of the pixels inside the different rectangles are compared to decide whether a line is present. For single-channel data, a line detector is constructed from the Touzi edge detector. For polarimetric data, we use a multi-variate hypothesis test. Because of the difference in spatial resolution and information content of the 4 frequency bands, results are improved by fusing the individual results from the different bands. On the other, the synergy with speckle reduction is also examined. Without speckle reduction, large scanning rectangles need to be used for the line detection because of the presence of the speckle. If speckle reduction is applied prior to line detection, smaller rectangles can be used. The former approach allows to detected lines that show a lower contrast while the latter allows to find smaller details and achieves a higher spatial accuracy. The proposed method was applied to one of the sets of images and results are shown and evaluated.

This paper describes a nonparametric algorithm based on fuzzy-reasoning concepts and suitable for land use classification, either supervised or unsupervised, starting from pixel features derived from SAR observations. To this purpose, two novel features describing scene inhomogeneity are utilized. The former relies on the joint density of estimated local standard deviation to local mean. The latter is a multiresolution coefficient of variation calculated in the domain defined by the "a trous" wavelet transform. Pixel vectors constituted by features calculated from the backscattering coefficient(s) in one or more bands and/or polarizations are clustered. Possible “a priori” knowledge coming from ground truth data may be used to initialize the procedure, but is not required. At each iteration step, pixels in the scene are classified based on the minimum attained by a weighted Euclidean distance from the centroid representative of each cluster. Upgrade of centroids is iteratively obtained both from the previously obtained classification map, and by thresholding a membership function of pixel vectors to each cluster. Such a function has been derived based on entropy maximization of the resulting clusters configuration and has the favorable property of preserving minor clusters. Experimental results carried out on SIR-C polarimetric and X-SAR data of the city of Pavia and its surroundings demonstrate the usefulness of a nonparametric classification to discriminate land use in general, and urban and built-up areas with different degrees of building density, in particular, from SAR observations analogous to those which are routinely available from ERS-2 and EnviSat, and will be provided by the COSMO-SkyMed upcoming mission. Pixel-based classification attains over 70% accuracy without any postprocessing.

In this paper, a set of AIRSAR images has been exploited in conjunction with backscatter models of bare soils to develop an algorithm for soil moisture estimation. The algorithm is based on a Bayesian approach and combines prior information on surface parameters with observed data, in order to extract information regarding surface parameters. Bayesian methodology allows meaningful and rigorous incorporations of all information sources into the inverse problem solution. The key point is the evaluation of a joint posterior probability density function based on the contemporary knowledge of data sets consisting of soil parameters measurements and the corresponding remote sensed data. In this study, it is obtained by applying the maximum likelihood principle (MLP). The inversion procedure has been applied to C-band images at HH and VV polarisations, to L-band images at HH and VV polarisations and to a merger of C- and L-band images both at HH polarisation. From a comparison with ground truth measurements, the best performances are achieved with two frequencies. Furthermore, it can be noted that the drying phase changes considerably from one part to another of the same field.

The objective of this paper is to investigate and compare two model-based methods for the soil moisture retrieval from SAR data. The overall accuracy of model-based methods in estimating geophysical parameters mostly depends both on the performances of the exploited direct model and on the intrinsic ambiguity of SAR data. The ambiguity is due to the complexity of the relationship between the geophysical parameters, such as soil moisture and soil roughness, and the backscattering values that makes 'ill posed' the inverse problem of the parameter retrieval. Moreover, the accuracy of soil moisture estimates depends also on the retrieval algorithm and on its robustness to the noise. In this study, the methods under investigation perform a probabilistic estimation of the parameters, finding solutions representative of an unknown distribution such as the mean or the most probable solutions. The model-based methods considered are a Neural Network algorithm, to explicit invert the direct model, and a Mixture Model algorithm, to approximate the parameter distribution function. The theoretical direct model adopted to tune the inversion algorithms is the Integral Equation Method (IEM) model. A comparison of the characteristics of the two algorithms is shown, and same evaluations of the accuracy in predicting soil moisture content from SAR data are performed. In particular, evaluations refers to ERS and ENVISAT ASAR data simulated by means of the IEM model.

This paper is focused on the analysis of SAR imagery of the Mediterranean Sea to estimate the directional wave spectrum and the wind vector. It is discussed the potential of using fetch-limited wave spectral parameterisation, which is currently used to represent wind generated waves, in the case of SAR imaging of swells. As the interest is also focused on the role of the SAR-estimated wind vector to get reliable estimates of the wave spectra, the spectral form used in this study is that due to Donelan et al.. ERS-1 SAR imagery, which is co-located with instrumented directional buoys belonging to the Italian Buoy Network (RON, Rete Ondametrica Nazionale), was exploited. The main purpose is to study SAR capability to discriminate between locally generated wind waves and old wave systems. Three case studies were selected and analysed. They are devoted to compare wave spectral properties typical of swell systems observed at buoy stations located off-shore La Spezia, Ponza island and Alghero with SAR inverted directional wave spectra.

Telaer is an advanced airborne remote sensing system for environmental monitoring and management. It is constituted by two aircrafts equipped with optical and microwave sensors. During the recent environmental crisis caused by the Prestige shipwreck, the operation of the Telaer system provided with an X-band SAR system mounted on a Learjet 35A, has been requested by the Spanish authorities. The Galicia campaign was aimed to perform daily missions on the Prestige sink area in order to monitor the status of the process of oil spilling out of the sunken ship hull. SAR data have been processed in real time on board for a first guess of oil slicks detection; next, on ground, the images have been geo-referenced and processed in near real time (within 4 hours from acquisition) for a more accurate identification, size estimation and localization of oil slicks. This information has been validated against external sources, based on visual recognition during helicopters and planes over-flights of the surveyed zone. During the Galicia campaign 16 missions were carried out with a total of more than 50 flight hours. The results obtained during such a campaign will be shown.

This paper presents two different approaches to detect and correct phase errors appearing in interferometric airborne SAR sensors due to the lack of precision in the navigation system. The first one is intended for interferometric pairs with high coherence, and the second one for low coherent ones. Both techniques are based on a multisquint processing approach, i.e., by processing the same image pairs with different squint angles we can combine the information of different interferograms to obtain the desired phase correction. Airborne single- and repeat-pass interferometric data from the Deutsches Zentrum fur Luft- und Raumfahrt (DLR) Experimental airborne SAR is used to validate the method.

This paper focuses on the geometric applications of the SAR interferometry, i.e. the generation of digital elevation models (interferometric SAR, InSAR) and the monitoring of deformations (differential interferometric SAR, DInSAR). The InSAR and DInSAR techniques have in common most of the processing steps of their procedures. In this paper we describe a general DInSAR procedure for deformation monitoring. Furthermore, we discuss the phase unwrapping, which represents a key processing step for both the InSAR and DInSAR techniques. The second part of the paper describe in depth the DInSAR results based on ERS images, which were obtained on the estimation of the co-seismic field associated with a series of earthquakes occurred in Central Italy in 1997.

SAR image matching is a difficult task in relief reconstruction by radargrammetry. Two major class of methods exist : Area based methods and feature based methods. In one hand, feature based methods can give robust, but sparse disparity maps. The difficulty lies in feature extraction. On the other hand, area based methods give dense disparity maps but classical correlation measures are not efficient because of speckle noise. In this paper we deal with various correlation measures evaluation. We propose and compare different ways to estimate the correlation, or the similarity between a couple of SAR image in radargrammetric conditions. Five correlation coefficients will be studied : - the classical Zero Normalized Correlation Coefficient (ZNCC), - a ZNCC applied on a edge image of the scene, - a Binary Correlation Coefficient : we define a binary image of both images and measure the binary overlap. - a correlation coefficient taking into account the Intensity Image statistics, - a correlation coefficient taking into account the Reflectivity Correlation of the underlying scene. We also introduce two similarity measures : - the Cluster Reward Algorithm - and the Mutual Information These kind of operators are based on an entropy measure, and a 2D-histogram analysis to estimate the similarity between the couple of image. They are well adapted to compare images with different radiometry, but similar geometry. In our work, we evaluate the performances of these coefficients with SAR images. We also characterize their behavior on different kind of scenes (textured, high relief area, cities...).

Topographic applications of satellite images, such as image map production and digital elevation model (DEM) generation, are particularly important in remote and unmapped areas. These applications require a precise image geo-referencing but ground control point (GCP) acquisition is normally difficult in those areas. Ancillary data supplied with images can provide exterior orientation parameters with some accuracy. In the case of SAR images the knowledge of a precise orbit and time and range references can provide the required image geo-referencing. This paper presents an analysis of the geo-location of a set of Radarsat and ERS-2 images. Errors of a few pixels were found, with a systematic character, allowing for an improvement of image orientation by slight adjustments in range and time references. A minimum of one GCP is required for that. Some examples of ortho image generation and DEM extraction are presented.

This paper presents a numerical tool able to generate realistic SAR images from accurate vessel models for a given orbital sensor. Its capability to extract high resolution radar signatures converts this SAR simulator in a useful tool for vessel classification studies and, furthermore, to define a future constellation of SAR sensors bound for carry on an automatic vessel monitoring system. This SAR simulator has low computational requirements as it is based on high frequency electromagnetic calculations making feasible to run it in a simple PC. In this paper, the main scheme of the simulator and its capability to consider the vessel velocity and the ocean waves, which can produce an important distorting effect in the final SAR images, will be presented as well as some validation results and particular aspects of vessel modeling. Finally, some examples of radar signatures of precise fishing vessel models are exposed.

The most recent and advanced synthetic aperture sensors are able to work in different operating modes and are currently being installed in an increasing variety of platforms. In order to be ready to process data generated not only by these new sensors but by the incoming ones, it is important to identify the common processing blocks. For instance, different chirp scaling algorithm implementations have been analyzed to derive an approach of the same algorithm being able to process raw data in StripMap, ScanSAR and SpotLight operating modes. Next to the Radar imaging techniques, the processing software has been developed to be able to dynamically adapt its performance to the memory and CPU resources. Maximum portability has also been one of the major tasks and the same code runs under IBM and SUN UNIX, Linux and Windows 32 bits platforms. Finally, Extended Markup Language (XML) standard has been adopted for parameter, setup and report files to improve the user experience. The processing kernel and the specific modules for each operating mode and platform have been validated using raw data from ERS-1, RADARSAT and DLR while to validate SpotLight mode, simulated data has been used for both air- and space borne platforms.

InfoTerra is an innovative market-derived EO-services concept with end-to-end products and service chains addressing end user information requirements in existing and new markets, with the advantage of a dedicated SAR satellite system TerraSAR (L+X-band) featuring high spatial and thematic resolution. The services will be provided from integrated value chains in a network with complementary partners, benefiting from most up-to-date and reliable image acquisition. The service development has been initiated in 1998 running in parallel to the TerraSAR space and ground system implementation. Infoterra, a new geo-information services company founded in 2001, implements the business concept. TerraSAR-X will be the first system element being available in 2006. The X-band SAR capability enables various applications, e.g. change detection to rationalize updating of cartographic databases; forest inventories, and de-/afforestation monitoring; crop stand density monitoring to support optimized fungicide application; land use monitoring in support of environmental control. A key challenge addressed in the exploitation development is the largely automated and quality controlled large area processing and feature extraction. The high resolution, multi-polarization, and multi-mode TerraSAR-X data source will considerably improve the short-term event observation from space. Reception of data via dedicated ground stations of customers or partners will also be offered.