High-resolution wide swath (HRWS) synthetic aperture radar (SAR) systems are normally designed to have identical antenna patterns in each receive channel. Nevertheless, due to different factors, this condition might not be satisfied, resulting in a multichannel radar system with different antenna patterns. This letter studies the impact of these relative antenna differences on the performance of a state-of-the-art azimuth motion-adaptive image reconstruction for a real airborne SAR sensor with multiple azimuth channels on receive. The performance of the reconstruction algorithm is evaluated both when these relative differences are neglected and when they are accounted for in the multichannel reconstruction process. Additionally, the range dependence of the antenna pattern as a function of wavenumber and the use of range block processing to minimize the impact of this dependence are analyzed. The results confirm the relevance of including these additional steps in the reconstruction, extending the understanding of SAR systems using digital beamforming (DBF) in azimuth.

Synthetic Aperture Radar (SAR) represents nowadays a key technology in Earth Observation (EO), evolving its original capabilities into both large-scale monitoring of geophysical parameters and very high-resolution imaging with short revisit times. The increase in system performance and the wide range of application scenarios require significant efforts in the design of current and future SAR missions: one of the most critical on-board operations is the digitization of the received echoes, directly impacting the final image quality and, at the same time, limited by the available downlink capacity. State-of-the-art quantization methods, such as Block Adaptive Quantization (BAQ), offer a good trade-off between signal quality and overall complexity but lack adaptivity to the imaged scenario. This leads to different impacts of the quantization error on the final SAR image. As an evolution of BAQ, Performance-Optimized BAQ (PO-BAQ) is a recently proposed quantization method, which addresses this issue by employing variable quantization rates across the scene, targeting specific performance requirements in the final SAR image. In this paper, we present a feasibility study of variable bitrate allocation in a realistic SAR mission scenario: to ensure flexibility, we consider the bitrate allocation map (BRM) to be uploaded at commanding phase during each ground segment contact, individually tailoring the required performance quality for each acquisition. State-of-the-art uplink data rates are considered, and the complete performance evaluation after SAR processing is carried out using the experimental on-board processor developed within the SOPHOS Horizon 2020 project.

The use of airborne synthetic aperture radar (SAR) to demonstrate high-resolution wide swath (HRWS) operational modes in spaceborne SAR missions has supported the development of advanced digital beamforming (DBF) techniques. In doing so, one of the challenges to overcome is the temporal variation of the antenna phase centers in the airborne DBF SAR scenario, which significantly degrades the performance of the azimuth reconstruction. Multiple motion compensation (MoCo) solutions have been explored to correct these inconsistencies. However, the compensation of residual phase errors in the Doppler domain remains unresolved when the multichannel data has an aliased azimuth spectrum. This article proposes an algorithm that exploits the properties of the DBF azimuth reconstruction to correct these residual motion inconsistencies, although the channels are undersampled. The algorithm modifies the input range-compressed multichannel data by using an innovative MoCo technique to compensate the phase components coming from undesired 3-D time-variant baselines between the different apertures. Furthermore, a two-step azimuth reconstruction configuration is implemented to account for the polychromatic nature of SAR signals. To test the performance of the algorithm, point target simulations were carried out, in which the impact of a realistic across-track motion, inaccuracies in the digital elevation model (DEM), and variable velocity are analyzed. The results confirm the efficacy of the proposed technique in azimuth ambiguity suppression, where excellent ambiguity suppression is observed after applying the proposed MoCo technique. Finally, the outcome of the simulations is validated with real multichannel data acquired by the German Aerospace Center (DLR) airborne DBFSAR system.

Synthetic Aperture Radar (SAR) enables the generation of realistic and high-resolution 2D or 3D representations of landscapes. Typically, radar instruments are deployed in specially equipped, low-flying aircraft that capture a significant amount of raw data, necessitating image reconstruction processing. However, the aircraft's limited onboard processing capabilities (power, size, weight, cooling, and communication bandwidth to ground stations) and the need to generate multiple SAR products, such as slant-range and geo-coded images during a single flight, require efficient onboard processing and transmission to the ground station. This paper outlines the processing architecture of the digital beamforming SAR (DBFSAR) employed by the German Aerospace Center (DLR) and the specific measures implemented to enable onboard processing. We elucidate the essential software optimizations and their integration into the SAR onboard routines, facilitating (near) real-time capability under certain conditions. Furthermore, we share the insights gained from our work and discuss their applicability to other processing scenarios with limited resource availability.

This paper presents our new SAR processing framework known as PRISM (Processor for Interferometric SAR Missions). This flexible approach allows the efficient and accurate processing of SAR data independent of the sensor and the acquisition mode. The two main PRISM components (the focusing and the interferometric chains) are described in this paper together with the philosophy of the software architecture. Experimental results are presented and discussed based on the impulse response function analysis of simulated data as well as the focusing and interferometric results using real TerraSAR-X data.

The GeoWAM project aims to achieve high-resolution and highly-accurate digital elevation models (DEM) to monitor the evolution of tidal flats over the German North Sea. To achieve this, the dual-frequency/dual-baseline (DFDB) DEM generation approach, recently developed by DLR, was further expanded to include L-band data. Based in the experience obtained in the 2019 GeoWAM campaign with X- and S-band only, the addition of L-band for the 2020 campaign helps to deal with the decorrelation found in repeat-pass data and potentially allows the creation of new merged products. For the computation of phase and coherence, a new local fringe frequency approach was included to improve the final generated DEMs.

The BIOMASS DEM Product Prototype Processor (BIO-DEMPP) is being developed in the frame of ESA's Earth Explorer BIOMASS mission. The prototype includes a complete interferometric SAR chain, from the stack co-registration until the mosaicking of the derived height products (Digital Elevation and Digital Terrain Models). This paper presents an overview of the BIODEMPP architectural design and its validation strategy, as well as first results obtained with simulated BIOMASS-like data.

This paper presents first results for an automatic interpretation of SAR images of sea ice acquired in the Davis Strait off the coast of Baffin Island in 2019. While the study provides multi-frequency and interferometric data collected by the DLR F-SAR airborne SAR sensor, we focus on the analysis of C-band as one of the most commonly used frequencies for sea ice monitoring. We apply an iterative version of polarimetric k-Means which allows to work on the local variance-covariance matrices directly. The obtained clusters show very distinct polarimetric as well as topological properties, which indicates that they are closely related to different sea ice types.

Recently synthetic aperture radar (SAR) has become implemented in multi-receiver systems to achieve, for example, along track interferometry (ATI) and/or digital beam forming (DBF) for wide observation swath. In this paper, we propose a novel radio frequency interference (RFI) detection method especially for multi-receiver SAR systems. We assume that RFI signals have greater similarity than the summation of radar echoes among multiple receivers. Based on this idea, we performed an interferometric analysis of the SAR raw data in both time and frequency domain and removed signals which associated with an exceptionally high correlation. We report experimental results with fully-polarimetric, single-pass interferometric dataset acquired by DLR's F-SAR sensor.

Speckle is inherent to all coherent imaging systems and affects SAR imagery in the form of strong intensity variations in pixels with similar backscattering coefficient, difficulting the interpretation of SAR data. In the context of the Beltrami filter, a polarimetric distance is utilized as part of a region growing algorithm to find and then average similar covariance matrices within a central window using an iterative scheme. The Beltrami filter has shown good results using a computationally expensive geodesic distance that takes into account the Hermitian positive definite nature of the polarimetric covariance matrices. The flexible nature of the Beltrami distance allows for the use of any polarimetric distance, allowing the study on the utilization of less computationally complex distances and their impact on speckle reduction. In this paper, an analysis on the effect of some of the commonly utilized polarimetric synthetic aperture radar (PolSAR) distances measures within the Beltrami despeckling filter will be presented.

This contribution will present the investigations carried out with stacked time-series of TOPS acquisitions to retrieve azimuth displacements. The exploitation of the burst overlapping areas allows to retrieve highly accurate ground displacement estimates in that direction, mitigating the limited performance of classical correlation techniques, due to the low resolution of the mode. The focus of this contribution is the application to time-series of Sentinel-1 data, where slow azimuthal motion is expected. Results with Sentinel-1 data on the proposed methodology are provided for the region of Balochistan, in Pakistan.

Soil moisture under grassland is assessed at field scale using multiple sensing techniques: in situ soil moisture network measurements (SoilNet), rover-based cosmic ray neutron sensing (CRNS rover) and airborne polarimetric SAR acquisitions (PolSAR) at L-band. The three interdisciplinary techniques acquire on different spatial scales from meters to hectometers. In this study, the methods are blended at the field scale to estimate soil moisture under grassland in a synergistic as well as a stand-alone approach. Data from the TERENO Fendt test site near Weilheim (Germany) were recorded concurrently within the ScaleX campaign on 10thof July, 2015. The multisensor assessment reveals that PolSAR estimates benefit fundamentally from the in situ techniques to effectively remove the vegetation scattering component leading to very accurate permittivity estimates (RMSE <; 1 [-]). The PolSAR analyses verified the full applicability of the low-parameterized vegetation scattering model to sufficiently represent grassland cover. Moreover, the comparison of all moisture products indicates the constraint of PolSAR to assess only the surface moisture at L-band, while the other two techniques are able to assess also soil moisture of deeper layers, reaching down to the root zone.

Generating accurate repeat-pass interferometric airborne synthetic aperture radar (SAR) products demands the precise compensation of the 3-D motion of the aircraft. This requires to estimate and correct small residual motion errors (RMEs) within the accuracy limits of the sensor navigation subsystem, e.g., using residual motion compensation (MoCo) algorithms. Because of their data-driven nature, the performance of these algorithms severely degrades for heavily decorrelated interferograms. This letter proposes a generic RME estimation and compensation strategy optimally estimating RME in a stack of SAR acquisitions, where some interferometric pairs are strongly affected by decorrelation. The algorithm works even if the whole scene decorrelates, as long as the coherence magnitude is reasonably high over short temporal and spatial baselines. The approach entails correcting the navigation data of each slave image of a one-to-many interferometric network with a cumulative correction. The summation is over the results of a precursory application of a general data-driven residual MoCo algorithm (e.g., multisquint) to interferometric pairs for which the impact of interferometric decorrelation is marginal (i.e., small temporal and/or spatial baselines). Compared to other RME correction strategies, the main appeal of the proposed approach lies in the simplicity of its implementation. The overall methodology is tested on a zero-baseline time series acquired at L-band by the German Aerospace Center’s airborne system F-SAR.

Synthetic aperture radar (SAR) remote sensing allows high-resolution imaging independent of weather conditions and sunlight illumination and is therefore very attractive for the systematic observation of dynamic processes on the earth's surface. Conventional SAR systems are, however, limited in that a wide swath can only be imaged at the expense of a degraded azimuth resolution. This limitation can be overcome by using systems with multiple receive subapertures displaced in along-track, but a very long antenna is required to map a wide swath. If a relatively short antenna with a single aperture in along-track is available, it is still possible to map a wide area: multiple subswaths can simultaneously be imaged using digital beamforming in elevation, but “blind ranges” are present between adjacent swaths, as the radar cannot receive while it is transmitting. Staggered SAR overcomes the problem of blind ranges by continuously varying the pulse repetition interval (PRI). A proper selection of the PRIs, together with moderate oversampling in azimuth, allows an accurate interpolation of the nonuniformly sampled raw data into a uniform grid, so that resampled data can then be focused with a conventional SAR processor. This approach thereby allows high-resolution imaging of a wide continuous swath without the need for a long antenna with multiple subapertures. In this paper, the performance of staggered SAR is thoroughly discussed and novel methods for the evaluation of the range and azimuth ambiguity-to-signal ratios in staggered SAR are proposed. An L-band design example based on a reflector antenna with multiple feeds shows that outstanding ambiguity performance is obtained, provided that data are moderately oversampled in azimuth. As an additional benefit, the energy of range and azimuth ambiguities is spread over large areas: ambiguities therefore appear in the image as a noise-like disturbance rather than localized artifacts. The impact of staggered SAR operation on image quality is furthermore assessed with experiments using real data. As the first step, highly oversampled F-SAR airborne data have been used to generate equivalent staggered SAR data sets and to evaluate the performance for different oversampling factors and interpolation methods. Then, the radar instrument of the German satellite TerraSAR-X has been commanded to acquire data over the lake Constance in staggered SAR mode. Measurements on these data show very good agreement with predictions from simulations. Staggered SAR is currently being considered as the baseline acquisition mode for Tandem-L, a proposal for a polarimetric and interferometric spaceborne SAR mission to monitor dynamic processes on the earth's surface with unprecedented accuracy and resolution.

The paper presents a comparison of automatic temporal change detection techniques on the basis of SAR data acquired during the 2015 DALO-ARCTIC campaign in Greenland. The data, acquired by DLR's F-SAR sensor, includes fully polarimetric imagery at X-, C-, S- and L-band with temporal baselines ranging from very short (less than one hour) to long (more than three weeks). Results for both coherent (interferometric) and incoherent (polarimetric) change detection are presented, compared and discussed, taking into account the known environmental conditions during and between data acquisitions. They suggest that natural environmental changes in Arctic regions, such as snow fall or thaw, have a significant impact on the polarimetric/interferometric SAR signal that can, in some cases, dominate the change detection result, especially for long temporal baselines.

Speckle noise is an inherent problem in coherent imaging systems such as synthetic aperture radar. It creates strong intensity fluctuations and hampers the analysis of images and the estimation of local radiometric, polarimetric, or interferometric properties. Synthetic aperture radar (SAR) processing chains thus often include a multilooking (i.e., averaging) filter for speckle reduction, at the expense of a strong resolution loss. Preservation of point-like and fine structures and textures requires to adapt locally the estimation. Nonlocal (NL)-means successfully adapt smoothing by deriving data-driven weights from the similarity between small image patches. The generalization of nonlocal approaches offers a flexible framework for resolution-preserving speckle reduction. We describe a general method, i.e., NL-SAR, that builds extended nonlocal neighborhoods for denoising amplitude, polarimetric, and/or interferometric SAR images. These neighborhoods are defined on the basis of pixel similarity as evaluated by multichannel comparison of patches. Several nonlocal estimations are performed, and the best one is locally selected to form a single restored image with good preservation of radar structures and discontinuities. The proposed method is fully automatic and handles single and multilook images, with or without interferometric or polarimetric channels. Efficient speckle reduction with very good resolution preservation is demonstrated both on numerical experiments using simulated data, airborne, and spaceborne radar images. The source code of a parallel implementation of NL-SAR is released with this paper.

The paper proposes a novel approach to object classification and segmentation in multi-channel (e.g. polarimetric) SAR data. The classifier is intended for particularly difficult problems, where objects of interest exhibit a high degree of radiometric, polarimetric and geometric heterogeneity, both within individual object instances and across the object category as a whole. Classification is based on a non-parametric characterization of scene contents that avoids model assumptions liable to fail in this scenario. The classifier structure is based on a combination of techniques developed for related problems in computer vision: the cascade architecture helps breaking down the problem into manageable stages while random forests provide a powerful framework for learning and combining discriminative classification rules. In addition, scale space techniques explicitly introduce non-local, contextual and geometric information into the classification process. Preliminary results illustrate the potential of the proposed approach with respect to the task of building segmentation in dual-polarized TerraSAR-X data.

The objective of the NASA Soil Moisture Active & Passive (SMAP) mission is to provide global measurements of soil moisture and its freeze/thaw state. The SMAP measurement approach is to integrate L-band radar and radiometer as a single observation system combining the respective strengths of active and passive remote sensing for enhanced soil moisture mapping. Airborne instruments will be a key part of the SMAP validation program. Here, we present an airborne campaign in the Rur catchment, Germany, in which the passive L-band system Polarimetric L-band Multi-beam Radiometer (PLMR2) and the active L-band system DLR F-SAR were flown on six dates in 2013. The flights covered the full heterogeneity of the area under investigation, i.e. all types of land cover and experimental monitoring sites. The obtained data sets are used as a test-bed for the analysis of existing and development of new active-passive fusion techniques.

The F-SAR airborne SAR instrument represents the successor of the E-SAR system of the German Aerospace Center (DLR), which has been extensively used in the last three decades. Its development was triggered by the current demand for data being simultaneously acquired at different wavelengths and polarisations, as well as by the demand for very high resolution in the order of decimetres. F-SAR is a modular development utilising the most modern hardware and commercial off the shelf components. F-SAR is designed to operate fully polarimetrically at X-, C-, S-, L- and P-bands and provides single- pass polarimetric interferometric capabilities in X- and S-bands. Additionally, up to four bands can be acquired simultaneously, partly even in polarimetric acquisition modes. This paper aims to present and evaluate the advanced multi-frequency polarimetric capabilities of the F- SAR system, as well as first results of single-pass polarimetric interferometry at X- and S-band.

This paper proposes a method for extracting the tropospheric time delay in a single-pass using SAR images acquired in squinted geometries. The required accuracies and error sources are analysed and a satellite configuration providing single-pass measurements of the tropospheric delay is proposed. Moreover, a joint topography-tropospheric delay estimation using very-high resolution SAR images is also suggested for cases where an inaccurate DEM is available. A performance of the approach with current TerraSAR-X staring spotlight like parameters is also computed.

During the last decade, synthetic aperture radar (SAR) became an indispensable source of information in Earth observation. This has been possible mainly due to the current trend toward higher spatial resolution and novel imaging modes. A major driver for this development has been and still is the airborne SAR technology, which is usually ahead of the capabilities of spaceborne sensors by several years. Today's airborne sensors are capable of delivering high-quality SAR data with decimeter resolution and allow the development of novel approaches in data analysis and information extraction from SAR. In this paper, a review about the abilities and needs of today's very high-resolution airborne SAR sensors is given, based on and summarizing the longtime experience of the German Aerospace Center (DLR) with airborne SAR technology and its applications. A description of the specific requirements of high-resolution airborne data processing is presented, followed by an extensive overview of emerging applications of high-resolution SAR. In many cases, information extraction from high-resolution airborne SAR imagery has achieved a mature level, turning SAR technology more and more into an operational tool. Such abilities, which are today mostly limited to airborne SAR, might become typical in the next generation of spaceborne SAR missions.

The F-SAR airborne SAR instrument represents the successor of the E-SAR system of the German Aerospace Center (DLR), which had previously been extensively used over the last three decades. Its development was triggered by the current demand for simultaneous acquisitions at different wavelengths and polarisations as well as by the demand for very high resolution in the order of decimetres. F-SAR is a modular development utilising state of the art hardware. As for E-SAR, DLRs Dornier DO228-212 aircraft is the first choice as platform for the new system. With the recently completed X-band single-pass interferometer and the P-band subsystem, F-SAR is now ready for fully replacing the E-SAR. This paper presents the two new subsystems and analyses their performance based on fully polarimetric imagery acquired during recent system test and calibration campaigns.

The F-SAR airborne SAR instrument represents the successor of the E-SAR system of the German Aerospace Center (DLR), which has been extensively used in the last three decades. Its development was triggered by the current demand for data being simultaneously acquired at different wavelengths and polarisations as well as by the demand for very high resolution in the order of decimetres. F-SAR is a modular development utilising the most modern hardware and commercial off the shelf components. As for E-SAR DLRs Dornier DO228-212 aircraft is the first choice as platform for the new system. Although the F-SAR system is still under development, it is already taking over some of the operational duties of the old E-SAR system. This paper will analyse the performance of the current system, based on the multi-frequency and fully polarimetric imagery acquired during several campaigns in the last two years. Since F-SAR is using a fixed antenna mount without gimbal, precise radiometric calibration is particularly challenging, especially in the shorter wavelengths. Therefore, special emphasis is placed on the system calibration and the associated quality control including the achieved spatial resolution and radiometric accuracy in the different bands.

Scatterers in synthetic aperture radar (SAR) images exhibit high dependence on scatterer-sensor orientations. This phenomenon is prevalent in urban areas. This paper applies hidden Markov model (HMM) to characterize the dependence and model the variations with respect to orientation. Buildings in high resolution SAR images of urban areas are studied. Buildings regions are divided into several discrete classes according to their orientation angles. We model the variations of scatterers characteristics throughout the subapertures using HMM. Subapertures are generated using wavelet packet decomposition. The experimental results show that HMM is efficient in building detection and orientation angle identification. HMMs trained using different feature sets are investigated. The evolution of scatterer states in subapertures are obtained from the HMM inference.

A simple vegetation model for polarimetric covariance and coherency matrix elements is presented. The model aims to represent vegetation characteristics which are observable by radar polarimetry, including the average particle scattering anisotropy, the main orientation of the volume, the degree of orientation randomness in the volume, and the terrain slopes. The goal of this approach is to quantify these parameters and to enable their estimation in a remote sensing parameter inversion framework. The retrieval of parameters related to effective particle shapes in the polarization plane and the orientation distribution characteristics is evaluated on real SAR data acquired by DLR's E-SAR system at L-band.

This paper presents a preliminary comparison of mean shift segmentation, efficient graph-based segmentation and normalized cuts for segmenting meter-resolution SAR data in urban areas. The small patches generated by these bottom-up segmentation algorithms provide spatial support for object detection. We evaluate their performances on ground truth data by varying their parameters. In order to obtain better spatial support, we apply multiple segmentations to a single segmentation of each algorithm. The multiple segmentations are representative samplings of the entire segmentation space. The experimental results demonstrate that the three algorithms are promising for SAR image segmentation, and that the multiple segmentations improves the abilities of providing spatial support.

Modern space-borne SAR sensors, like ALOS-PALSAR, TerraSAR-X and Radarsat-2 all provide at least a "partial polarimetric mode", acquiring only 2 of the 4 elements of the Sinclair matrix, like for example HH and HV or VV and VH. In addition, it has been demonstrated that with certain so-called "compact PolSAR" single-transmit dual-receive techniques one can obtain an estimation of the fully polarimetric information. Such systems are attractive in terms of reduction of pulse repetition frequency, data rate, and complexity and are currently very popular. However, they do not acquire complete information pertaining to the full polarisation state of the target and, as a consequence also coherence optimisation suffers from the reduced configuration space. In this paper, the potential of the different partial polarimetric setups for coherence optimisation is evaluated both theoretically and experimentally and compared to the capabilities of a fully polarimetric system. It will be analysed to which extent partial polarimetric system can improve the derivation of interferometric information from partly decorrelated surfaces, in particular of vegetated or even forested areas. Special attention is paid to the constrained coherence optimisation of multi-baseline setups, important for modern DInSAR techniques like PS analysis and continuous DInSAR monitoring in general. All experimental analyses will be performed using fully polarimetric multi-baseline data sets. For proper comparison, partial polarimetric information is derived from these by matrix transformations according to the respective transmit / receive configuration.

A grown importance in long-term operational glacier monitoring has emerged, mainly due to the connection of glacier recession to climate changes. Up to now, mainly two types of methods have been used for the estimation of glacier flow velocities: Image matching and differential interferometry (DInSAR). Although the principal potential of DInSAR for glacier velocity estimation has been shown in several case studies, its successful application is often limited by phase noise, described by the coherence. Additionally, the glacier velocity is often too large to be analysed by means of DInSAR since this method can be too sensitive to correctly track the large displacements occurring during a typical data acquisition interval of one month. SAR amplitude images are not limited by phase stability problems like in DInSAR and can reliably be acquired on a regular basis. In this work, a novel algorithm for computing the velocity field and motion parameters from a sequence of SAR amplitude images are presented. The algorithm is based on the vector relaxation combined with standardized cross- covariance matrix information and cross-correlation techniques. The cross-correlation is used to indicate the candidate motion vectors for each pixel. After this step, by a relaxation operation local smoothness constraints are introduced into the estimated flow pattern, leading to a more homogeneous velocity estimation. In order to handle fast motion and reduce the mismatches, the mentioned algorithms are applied in different scales and linked using anisotropic diffusion equation in case of multiscale cross-correlation. This significantly improves the reliability of the motion detection in the presence of noise, inherent in case of SAR data.

The paper presents a new framework for the classification of polarimetric SAR data. The underlying model introduces cyclic conditional dependencies among the class labels assigned to neighboring observations as a mechanism to regulate the spatial homogeneity of classification results. Classification is posed as an inference problem, and is solved by coherently integrating expectation maximization and graph cut optimization. Results based on real SAR data are presented.

This paper presents a distributed approach for time- domain focusing, which significantly enhances the overall efficiency by distributing the computational load across a (potentially large) number of networked computers. The system described includes the so-called master, responsible for pre-processing, the distribution of fragments of raw-data data and the collection of processed image fragments. Fragments of raw-data are passed to so-called slaves, any number of which can be connected to the master, which are responsible for the focusing itself. Master and slave actively communicate over the network to organise the entire process in a scalable manner. In this way, time-domain processing can be accelerated by a factor that is virtually linear in the number of participating slaves. This paper summarises the current status of software development, realised in a platform-independent way using the IDL and Java languages. Additionally, some preliminary evaluations of performance, scalability and the required network infrastructure are given. Some examples of SAR data, acquired by the airborne sensor E-SAR of DLR, and processed with the system described are shown.

An interesting, but rarely used technique in polarimetric SAR interferometry is the enhancement of interferometric coherence by projection into an optimal polarimetric state. In particular, newly developed methods for polarimetric optimisation of multi-baseline coherences provide the possibility of simultaneous constrained coherence optimisation for more than one baseline. This technique can significantly improve the usefulness of long-term interferometric pairs and time-series, and appears, therefore, of interest to various fields of application. The aim of this paper is to discuss the correct derivation of multi-baseline differential interferograms with polarimetrically optimised coherence and to outline several possible areas of application, particularly in the field of differential interferometry and permanent scatterers.

This paper describes an unsupervised classifier for polarimetric interferometric synthetic aperture radar (PolInSAR) data. Expectation maximization is used to estimate class parameters that maximize the likelihood of observations in an input data set for a given number of classes. Polarimetric information, in the form of coherency matrices, and interferometric information, in the form of complex coherences, are taken into account. Differences in interferometric phase across different polarization states are explicitly modeled to make the classifier sensitive to the vertical structure of the scene under observation, and the distribution over such phase differences is introduced. The classifier is self-initializing, in that it does not rely on decompositions or thresholds. Classification results obtained for real polarimetric interferometric data are presented and discussed.

Abstract — Effective feature extraction is the basis of every approach to automated image analysis. An important class of extraction operators, point and region of interest detectors, has not yet been developed for SAR Polarimetry. This paper describes a region of interest operator designed to identify distinctive regions in a scale invariant fashion. The work presented includes a novel definition of image entropy, in the information theoretical sense, for polarimetric SAR image content, as well as a rigorous statistical analysis of the operators scale selection mechanism. This analysis establishes the ability to identify a region irrespective of its size. The results presented include the application of the operator to real data and demonstrations of the operators scale invariance. I.