Marc Jäger

NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising

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.

Very-High-Resolution Airborne Synthetic Aperture Radar Imaging: Signal Processing and Applications

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. Todays 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 todays 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.

Speckle Tracking and Interferometric Processing of TerraSAR-X TOPS Data for Mapping Nonstationary Scenarios

Terrain observation by progressive scan (TOPS) antenna beam steering is utilized for European Space Agencys (ESAs) Sentinel-1 synthetic aperture radar (SAR) sensor for the interferometric wide swath (IW) and extra wide swath (EW) modes. As a consequence of the azimuth steering, the resulting signal characteristics have to be accounted for in SAR interferometric (InSAR) processing. This paper assesses the performance of speckle tracking and spectral diversity (SD) [also referred to as split spectrum or multi-aperture interferometry (MAI)] when applied to TOPS data acquired over nonstationary scenarios, such as glaciers. The characteristics of the TOPS signal, especially the azimuth-variant Doppler centroid, are discussed with particular consideration of along-track surface motion between the interferometric acquisitions. The TOPS specific coregistration requirements are formulated, followed by an analysis of the theoretical estimation accuracy as a function of the estimation window size. A refined adaptive coregistration approach based on SD is suggested. Experimental TerraSAR-X TOPS data acquired over the Lambert glacier, Antarctica, are used to validate the proposed speckle tracking and SD methodologies.

Detection and classification of urban structures based on high-resolution SAR imagery

In this paper, several advanced methods for detecting and classifying urban structures in high-resolution SAR imagery are presented. The detection of man-made structures resp. the suppression of clutter, for example natural surfaces, can be accomplished by a coherent time-frequency analysis. These techniques can detect targets in the scene that behave like point-scatterers (such as edges, poles, etc.) by their high degree of correlation across sub-apertures of the scene. If polarimetric data are available, this approach can be extended by taking into account the frequently anisotropic polarimetric backscattering of man-made targets. As a third approach, a simple but powerful operator for detecting urban structures by analysing texture inhomogeneity in SAR images is developed. A second objective of this paper is the segmentation and classification of the detected urban structures. Based on the detected man-made structures, the characteristic structure size is investigated, using several approaches, to distinguishing between residential areas, industrial areas and dense city centres. The methods are evaluated using several real airborne SAR datasets.

System status and calibration of the F-SAR airborne SAR instrument

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.

Saliency and salient region detection in SAR polarimetry

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 irrespec- tive of its size. The results presented include the application of the operator to real data and demonstrations of the operators scale invariance. I. INTRODUCTION Feature extractors, for instance edge detectors, act as con- centrators of information that emphasise certain relevant prop- erties of an image and suppress noise and other types of irrele- vant information. This process is essential to algorithms which aim to provide high level, semantic interpretations of image content. This is partly due to the fact that such algorithms are easier to formulate if certain types of information are available, but also because images contain such a wealth of information that an exhaustive analysis is usually infeasible. A number of feature extraction operators have been proposed in the context of polarimetric SAR data. Ideally, these operators are designed to take the statistical properties of SAR images into account. Examples include polarimetric edge detectors (1) and texture descriptors (2). An important class of operators, point of interest and region of interest (ROI) detectors, has not yet been developed for SAR remote sensing data. ROI operators identify distinctive, prominent or highly informative patches in an image, and are employed to achieve sparse but succinct representations of complex image data. A wide range of problems in the automated analysis of image data can be formulated as search problems, where there is a need to identify certain objects or segments of a scene. In these types of problem, an operator which reduces an entire image to a comparatively small collection of patches, which are localised and have a known size, can greatly reduce the size of the search space to be considered.

Classifying polarimetric SAR data by combining expectation methods with spatial context

Unsupervised classification is an essential step in the automatic analysis of SAR remote sensing data. Classification results make SAR data easier to interpret and can serve as a starting point for automated analysis techniques that apply to homogeneous regions of the observed scene. Polarimetric SAR data are particularly interesting for unsupervised classification purposes, since they contain a great amount of information, allowing robust statistical clustering of the image content on the one hand and a direct physical interpretation of the result on the other. This paper proposes a new unsupervised classification approach for polarimetric SAR data. Assuming Wishart-distributed polarimetric covariance matrices, it combines spectral clustering based on the covariance matrices themselves with spatial clustering by statistical analysis of local neighbourhoods. Instead of working with binary assignments of samples to class centres, a soft decision rule is used in which each pixel is assigned to all class centres in the spectral and spatial domains. The local neighbourhood is taken into account by altering the probabilities of class membership by a neighbourhood function, obtained from normalized compatibility coefficients, describing cluster sizes and mutual tolerance. In this way, robust and homogenous classification results can be obtained even in the presence of strong speckle noise.

Multichannel SAR-GMTI in Maritime Scenarios With F-SAR and TerraSAR-X Sensors

This paper explores the ground moving target indication (GMTI) capabilities of the German Aerospace Centers state-of-the-art airborne (F-SAR) and spaceborne (TerraSAR-X) synthetic aperture radars (SARs) when operating over maritime scenarios. The performance of classical dual channel GMTI techniques, such as displaced phase center antenna (DPCA) and along-track interferometry (ATI), as well as the promising adaptive techniques, like extended DPCA (EDPCA) and imaging space-time adaptive processing (ISTAP) have been analyzed on the basis of experimental acquisitions with both sensors. The objective of the paper is to highlight the limitations and challenges to be considered when processing real, multichannel GMTI data from pioneering SAR sensors for maritime surveillance. Different calibration or channel balancing strategies, on the basis of the digital balancing (DB) method, are studied, considering their impact on SAR-GMTI performance. An adaptive SAR processor, accounting for target kinematics and based on a matched filter bank (MFB) approach, is integrated in the SAR-GMTI processing chain in order to retrieve refocused images of the moving vessels.

Multi-baseline polarimetrically optimised phases and scattering mechanisms for InSAR applications

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.

A polarimetric vegetation model to retrieve particle and orientation distribution characteristics

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 DLRs E-SAR system at L-band.