The TerraSAR-X Mission and System Design

This paper describes the TerraSAR-X Mission Concept within the context of a public-private-partnership (PPP) agreement between the German Aerospace Center DLR and industry. It briefly describes the PPP-concept as well as the overall project organization. The paper then gives an overview of the satellite design, the corresponding Ground Segment as well as the main mission parameters. After a short introduction to the scientific and commercial exploitation scheme, the paper finally focuses on the mission accomplishments achieved so far during the ongoing mission

A framework of rapid regional tsunami damage recognition from post-event TerraSAR-X imagery using deep neural networks

Near real-time building damage mapping is an indispensable prerequisite for governments to make decisions for disaster relief. With high-resolution synthetic aperture radar (SAR) systems, such as TerraSAR-X, the provision of such products in a fast and effective way becomes possible. In this letter, a deep learning-based framework for rapid regional tsunami damage recognition using post-event SAR imagery is proposed. To perform such a rapid damage mapping, a series of tile-based image split analysis is employed to generate the data set. Next, a selection algorithm with the SqueezeNet network is developed to swiftly distinguish between built-up (BU) and nonbuilt-up regions. Finally, a recognition algorithm with a modified wide residual network is developed to classify the BU regions into wash away, collapsed, and slightly damaged regions. Experiments performed on the TerraSAR-X data from the 2011 Tohoku earthquake and tsunami in Japan show a BU region extraction accuracy of 80.4% and a damage-level recognition accuracy of 74.8%, respectively. Our framework takes around 2 h to train on a new region, and only several minutes for prediction.This work was supported in part by JST CREST, Japan, under Grant JPMJCR1411 and in part by the China Scholarship Council. (JPMJCR1411 - JST CREST, Japan; China Scholarship Council

Building profile reconstruction using TerraSAR-X data time-series and tomographic techniques

This work aims to show the potentialities of SAR Tomography (TomoSAR) techniques for the 3-D characterization (height, reflectivity, time stability) of built-up areas using data acquired by the satellite sensor TerraSAR-X. For this purpose 19 TerraSAR-X single-polarimetric multibaseline images acquired over Paris urban area have been processed applying classical nonparametric (Beamforming and Capon) and parametric (MUSIC) spectral estimation techniques

Sentinel-1 InSAR coherence for land cover mapping: a comparison of multiple feature-based classifiers

This article investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. In addition, this study analyzes the performance of this feature along with polarization and intensity products according to different classification strategies and algorithms. Seven different classification workflows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain—interferometric coherence, backscattered intensities, and polarization. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximize diversity of land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on the study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Doñana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%) obtained promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherence-based results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinel-1 A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1 A/B stacks, i.e., six-day sampling, are considered.Peer ReviewedPostprint (published version

Discrimination of land cover from a multiparameter SAR data set

The identification of the most valuable radar observation parameters (e.g., frequency, polarisation, incidence angle) is important both for designing nonredundant high-performance sensors (i.e. selection of frequency bands and polarisations) and for specifying mission operation requirements (i.e. temporal sampling, incidence angle). Moreover, the task of classifying multiparameter SAR images may require to adopt a strategy that implies the selection of a number of features among those available fromthis kind of sensors. In this paper we have performed this kind of analysis in a specific area of interest to account for the particular conditions in which remotely sensed data are going to be used. The paper summarises the results of the analysis of the radar data acquired during the MAC Europe ’91 and X-SAR/SIR-C campaigns over the Montespertoli test site in Italy. The analysis is based mainly on a statistical approach aiming at demonstrating what is the contribution of different measurements performed by the polarimetric SAR for discriminating the surface coverage. The work is intended to furnish a guideline to develop an optimal strategy for acquiring and processing polarimetric data to be used for land classification

Discrimination of land cover from a multiparameter SAR data set

The identification of the most valuable radar observation parameters (e.g., frequency, polarisation, incidence angle) is important both for designing nonredundant high-performance sensors (i.e. selection of frequency bands and polarisations) and for specifying mission operation requirements (i.e. temporal sampling, incidence angle). Moreover, the task of classifying multiparameter SAR images may require to adopt a strategy that implies the selection of a number of features among those available fromthis kind of sensors. In this paper we have performed this kind of analysis in a specific area of interest to account for the particular conditions in which remotely sensed data are going to be used. The paper summarises the results of the analysis of the radar data acquired during the MAC Europe ’91 and X-SAR/SIR-C campaigns over the Montespertoli test site in Italy. The analysis is based mainly on a statistical approach aiming at demonstrating what is the contribution of different measurements performed by the polarimetric SAR for discriminating the surface coverage. The work is intended to furnish a guideline to develop an optimal strategy for acquiring and processing polarimetric data to be used for land classification

Polarimetric SAR Change Detection with the Complex Hotelling-Lawley Trace Statistic

Accepted manuscript version. Published version at http://dx.doi.org/10.1109/TGRS.2016.2532320.In this paper, we propose a new test statistic for unsupervised change detection in polarimetric radar images. We work with multilook complex covariance matrix data, whose underlying model is assumed to be the scaled complex Wishart distribution. We use the complex-kind Hotelling-Lawley trace statistic for measuring the similarity of two covariance matrices. The distribution of the Hotelling-Lawley trace statistic is ap- proximated by a Fisher-Snedecor distribution, which is used to define the significance level of a false alarm rate regulated change detector. Experiments on simulated and real PolSAR data sets demonstrate that the proposed change detection method gives detections rates and error rates that are comparable with the generalized likelihood ratio test

Change detection in SAR time-series based on the coefficient of variation

This paper discusses change detection in SAR time-series. Firstly, several statistical properties of the coefficient of variation highlight its pertinence for change detection. Then several criteria are proposed. The coefficient of variation is suggested to detect any kind of change. Then other criteria based on ratios of coefficients of variations are proposed to detect long events such as construction test sites, or point-event such as vehicles. These detection methods are evaluated first on theoretical statistical simulations to determine the scenarios where they can deliver the best results. Then detection performance is assessed on real data for different types of scenes and sensors (Sentinel-1, UAVSAR). In particular, a quantitative evaluation is performed with a comparison of our solutions with state-of-the-art methods