A Non-Parametric Texture Descriptor for Polarimetric SAR Data with Applications to Supervised Classification

The paper describes a novel representation of polarimetric SAR (PolSAR) data that is inherently non-parametric and therefore particularly suited for characterising data in which the commonly adopted hypothesis of Gaussian backscatter is not appropriate. The descriptor is also non-local and can capture image structure in terms of the arrangement of edge-, ridge- and point-like features, to yield a salient characerisation of semi-periodic spatial patterns. The basic approach is based closely on [1] and has been adapted for application to PolSAR data. As an example application, the descriptor is evaluated in the context of supervised classification. The performance is compared with conventional statistical approaches on both simulated and real PolSAR dat

A Versatile Processing Chain for Experimental TanDEM-X Product Evaluation

TanDEM-X is a high-resolution interferometric mission with the main goal of providing a global digital elevation model (DEM) of the Earth surface by means of single-pass X-band SAR interferometry. It is, moreover, the first genuinely bistatic spaceborne SAR mission, and, independently of its usual quasi-monostatic configuration, includes many of the peculiarities of bistatic SAR. An experimental, versatile, and flexible interferometric chain has been developed at DLR Microwaves and Radar Institute for the scientific exploitation of TanDEM-X data acquired in non-standard configurations. The paper describes the structure of the processing chain and focusses on some essential aspects of its bistatic part

A P-band 5-way Unequal Split High Power Divider for SAR Applications

The design and test of 5-way high power divider operating at P-band for the airborne Synthetic Aperture Radar (SAR) system of DLR is presented. Distinctive features are high bandwidth, high power and an unequal power split on the 5 output ports

Topography dependent motion compensation for repeat-pass interferometric SAR systems

This letter presents a new motion compensation algorithm to process airborne interferometric repeat-pass synthetic aperture radar (SAR) data. It accommodates topography variations during SAR data processing, using an external digital elevation model. The proposed approach avoids phase artifacts, azimuth coregistration errors, and impulse response degradation, which usually appear due to the assumption of a constant reference height during motion compensation. It accurately modifies phase history of all targets before azimuth compression, resulting in an enhanced image quality. Airborne L-band repeat-pass interferometric data of the German Aerospace Center experimental airborne SAR (E-SAR) is used to validate the algorithm.Peer Reviewe

Refined estimation of time-varying baseline errors in airborne SAR interferometry

The processing of airborne synthetic aperture radar (SAR) data requires a precise compensation of the deviations of the platform movement from a straight line. This is usually carried out by recording the trajectory with a high-precision navigation system and correcting them during SAR focusing. However, due to the lack of accuracy in current navigation systems, residual motion errors persist in the images. Such residual motion errors are mainly noticeable in repeat-pass systems, where they are causing time-varying baseline errors, visible as artefacts in the derived phase maps. In this letter, a refined method for the estimation of time-varying baseline errors is presented. An improved multisquint processing approach is used for obtaining robust estimates of higher order baseline errors over the entire scene, even if parts of the scene are heavily decorrelated. In a subsequent step, the proposed method incorporates an external digital elevation model for detection of linear and constant components of the baseline error along azimuth. Calibration targets in the scene are not necessary.Peer Reviewe

Interpolation-free Coregistration and Phase-Correction of Airborne SAR Interferograms

This letter discusses the detection and correction of residual motion errors that appear in airborne synthetic aperture radar (SAR) interferograms due to the lack of precision in the navigation system. As it is shown, the effect of this lack of precision is twofold: azimuth registration errors and phase azimuth undulations. Up to now, the correction of the former was carried out by estimating the registration error and interpolating, while the latter was based on the estimation of the phase azimuth undulations to compensate the phase of the computed interferogram. In this letter, a new correction method is proposed, which avoids the interpolation step and corrects at the same time the azimuth phase undulations. Additionally, the spectral diversity technique, used to estimate registration errors, is critically analyzed. Airborne L-band repeat-pass interferometric data of the German Aerospace Center (DLR) experimental airborne SAR is used to validate the metho

Additional illustrations of NL-SAR method for resolution-preserving (Pol)(In)SAR denoising

This document provides additional information and results of the method NL-SAR described in our paper: "NL-SAR: a unified Non-Local framework for resolution-preserving (Pol)(In)SAR denoising" submitted to IEEE Trans. on Geoscience and Remote Sensing [Deledalle et al., 2013]. NL-SAR is a fully automatic method for speckle reduction that handles amplitude, polarimetric and/or interferometric SAR data. It can process single look and multi-look images. The source code of the method is freely available at: http://www.math.u-bordeaux1.fr/cdeledal/nlsar.php

Pol-InSAR-Island - A benchmark dataset for multi-frequency Pol-InSAR data land cover classification

This paper presents Pol-InSAR-Island, the first publicly available multi-frequency Polarimetric Interferometric Synthetic Aperture Radar (Pol-InSAR) dataset labeled with detailed land cover classes, which serves as a challenging benchmark dataset for land cover classification. In recent years, machine learning has become a powerful tool for remote sensing image analysis. While there are numerous large-scale benchmark datasets for training and evaluating machine learning models for the analysis of optical data, the availability of labeled SAR or, more specifically, Pol-InSAR data is very limited. The lack of labeled data for training, as well as for testing and comparing different approaches, hinders the rapid development of machine learning algorithms for Pol-InSAR image analysis. The Pol-InSAR-Island benchmark dataset presented in this paper aims to fill this gap. The dataset consists of Pol-InSAR data acquired in S- and L-band by DLR\u27s airborne F-SAR system over the East Frisian island Baltrum. The interferometric image pairs are the result of a repeat-pass measurement with a time offset of several minutes. The image data are given as 6 × 6 coherency matrices in ground range on a 1 m × 1m grid. Pixel-accurate class labels, consisting of 12 different land cover classes, are generated in a semi-automatic process based on an existing biotope type map and visual interpretation of SAR and optical images. Fixed training and test subsets are defined to ensure the comparability of different approaches trained and tested prospectively on the Pol-InSAR-Island dataset. In addition to the dataset, results of supervised Wishart and Random Forest classifiers that achieve mean Intersection-over-Union scores between 24% and 67% are provided to serve as a baseline for future work. The dataset is provided via KITopenData: https://doi.org/10.35097/170