The SARptical Dataset for Joint Analysis of SAR and Optical Image in Dense Urban Area

The joint interpretation of very high resolution SAR and optical images in dense urban area are not trivial due to the distinct imaging geometry of the two types of images. Especially, the inevitable layover caused by the side-looking SAR imaging geometry renders this task even more challenging. Only until recently, the "SARptical" framework [1], [2] proposed a promising solution to tackle this. SARptical can trace individual SAR scatterers in corresponding high-resolution optical images, via rigorous 3-D reconstruction and matching. This paper introduces the SARptical dataset, which is a dataset of over 10,000 pairs of corresponding SAR, and optical image patches extracted from TerraSAR-X high-resolution spotlight images and aerial UltraCAM optical images. This dataset opens new opportunities of multisensory data analysis. One can analyze the geometry, material, and other properties of the imaged object in both SAR and optical image domain. More advanced applications such as SAR and optical image matching via deep learning [3] is now also possible.Comment: This manuscript was submitted to IGARSS 201

Reconstruction of building façades using spaceborne multiview TomoSAR point clouds

In this paper we present an approach that allows automatic reconstruction of building façades from 4D point cloud generated from tomographic SAR processing. The approach is modular and works by extracting façade points from the point density projected onto the ground plane. Individual façades are segmented using an unsupervised clustering procedure. Surface (flat or curved) model parameters of the segmented building façades are further estimated and finally the geometric primitives such as intersection points of the adjacent façades are determined to complete the reconstruction process. The proposed approach is illustrated and validated by examples using TomoSAR point clouds generated from TerraSAR-X high resolution spotlight images

Non-Local Compressive Sensing Based SAR Tomography

Tomographic SAR (TomoSAR) inversion of urban areas is an inherently sparse reconstruction problem and, hence, can be solved using compressive sensing (CS) algorithms. This paper proposes solutions for two notorious problems in this field: 1) TomoSAR requires a high number of data sets, which makes the technique expensive. However, it can be shown that the number of acquisitions and the signal-to-noise ratio (SNR) can be traded off against each other, because it is asymptotically only the product of the number of acquisitions and SNR that determines the reconstruction quality. We propose to increase SNR by integrating non-local estimation into the inversion and show that a reasonable reconstruction of buildings from only seven interferograms is feasible. 2) CS-based inversion is computationally expensive and therefore barely suitable for large-scale applications. We introduce a new fast and accurate algorithm for solving the non-local L1-L2-minimization problem, central to CS-based reconstruction algorithms. The applicability of the algorithm is demonstrated using simulated data and TerraSAR-X high-resolution spotlight images over an area in Munich, Germany.Comment: 10 page

Towards SAR Tomographic Inversion via Sparse Bayesian Learning

Existing SAR tomography (TomoSAR) algorithms are mostly based on an inversion of the SAR imaging model, which are often computationally expensive. Previous study showed perspective of using data-driven methods like KPCA to decompose the signal and reduce the computational complexity. This paper gives a preliminary demonstration of a new data-driven method based on sparse Bayesian learning. Experiments on simulated data show that the proposed method significantly outperforms KPCA methods in estimating the steering vectors of the scatterers. This gives a perspective of data-drive approach or combining it with model-driven approach for high precision tomographic inversion of large areas.Comment: accepted in preliminary version for EUSAR2020 conferenc

Evaluation of insar dem from high-resolution spaceborne sar data

In recent years a new generation of high-resolution SAR satellites became operational like the Canadian Radarsat-2, the Italian Cosmo/Skymed, and the German TerraSAR-X systems. The spatial resolution of such devices achieves the meter domain or even below. Key products derived from remote sensing imagery are Digital Elevation Models (DEM). Based on SAR data various techniques can be applied for such purpose, for example, Radargrammetry (i.e., SAR Stereo) and SAR Interferometry (InSAR). In the framework of the ISPRS Working Group VII/2 "SAR Interferometry" a long term scientific project is conducted that aims at the validation of DEM derived from data of modern SAR satellite sensors. In this paper, we present DEM results yield for the city of Barcelona which were generated by means of SAR Interferometry.DL

ANALYSIS OF X-BAND VERY HIGH RESOLUTION PERSISTENT SCATTERER INTERFEROMETRY DATA OVER URBAN AREAS

Persistent Scatterer Interferometry (PSI) is a satellite-based Synthetic Aperture Radar (SAR) remote sensing technique used to measure and monitor land deformation from a stack of interferometric SAR images. This work concerns X-band PSI and, in particular, PSI based on very high resolution (VHR) StripMap CosmoSkyMed and TerraSAR-X SAR imagery. In fact, it mainly focuses on the technical aspects of deformation measurement and monitoring over urban areas. A key technical aspect analysed in this paper is the thermal expansion component of PSI observations, which is a result of temperature differences in the imaged area between SAR acquisitions. This component of PSI observations is particularly important in the urban environment. This is an interesting feature of PSI, which can be surely used to illustrate the high sensitivity of X-band PSI to very subtle displacements. Thermal expansion can have a strong impact on the PSI products, especially on the deformation velocity maps and deformation time series, if not properly handled during the PSI data processing and analysis, and a comprehensive discussion of this aspect will be provided in this paper. The importance of thermal expansion is related to the fact that the PSI analyses are often performed using limited stacks of images, which may cover a limited time period, e.g. several months only. These two factors (limited number of images and short period) make the impact of a non-modelled thermal expansion particularly critical. This issue will be illustrated considering different case studies based on TerraSAR-X and CosmoSkyMed PSI data. Besides, an extended PSI model which alleviates this problem will be described and case studies from the Barcelona metropolitan area will demonstrate the effectiveness of the proposed strategy

Evaluation of InSAR and TomoSAR for monitoring deformations caused by mining in a mountainous area with high resolution satellite-based SAR

Interferometric Synthetic Aperture Radar (InSAR) and Differential Interferometric Synthetic Aperture Radar (DInSAR) have shown numerous applications for subsidence monitoring. In the past 10 years, the Persistent Scatterer InSAR (PSI) and Small BAseline Subset (SBAS) approaches were developed to overcome the problem of decorrelation and atmospheric effects, which are common in interferograms. However, DInSAR or PSI applications in rural areas, especially in mountainous regions, can be extremely challenging. In this study we have employed a combined technique, i.e., SBAS-DInSAR, to a mountainous area that is severely affected by mining activities. In addition, L-band (ALOS) and C-band (ENVISAT) data sets, 21 TerraSAR-X images provided by German Aerospace Center (DLR) with a high resolution have been used. In order to evaluate the ability of TerraSAR-X for mining monitoring, we present a case study of TerraSAR-X SAR images for Subsidence Hazard Boundary (SHB) extraction. The resulting data analysis gives an initial evaluation of InSAR applications within a mountainous region where fast movements and big phase gradients are common. Moreover, the experiment of four-dimension (4-D) Tomography SAR (TomoSAR) for structure monitoring inside the mining area indicates a potential near all-wave monitoring, which is an extension of conventional InSAR

Buildings Detection in VHR SAR Images Using Fully Convolution Neural Networks

This paper addresses the highly challenging problem of automatically detecting man-made structures especially buildings in very high resolution (VHR) synthetic aperture radar (SAR) images. In this context, the paper has two major contributions: Firstly, it presents a novel and generic workflow that initially classifies the spaceborne TomoSAR point clouds $-$ generated by processing VHR SAR image stacks using advanced interferometric techniques known as SAR tomography (TomoSAR) $-$ into buildings and non-buildings with the aid of auxiliary information (i.e., either using openly available 2-D building footprints or adopting an optical image classification scheme) and later back project the extracted building points onto the SAR imaging coordinates to produce automatic large-scale benchmark labelled (buildings/non-buildings) SAR datasets. Secondly, these labelled datasets (i.e., building masks) have been utilized to construct and train the state-of-the-art deep Fully Convolution Neural Networks with an additional Conditional Random Field represented as a Recurrent Neural Network to detect building regions in a single VHR SAR image. Such a cascaded formation has been successfully employed in computer vision and remote sensing fields for optical image classification but, to our knowledge, has not been applied to SAR images. The results of the building detection are illustrated and validated over a TerraSAR-X VHR spotlight SAR image covering approximately 39 km$^2$ $-$ almost the whole city of Berlin $-$ with mean pixel accuracies of around 93.84%Comment: Accepted publication in IEEE TGR