Benefit of Using Multiple Baselines and Multiple Aspects for SAR Interferometry of Urban Areasby Michael Schmitt, Johannes L. Schonberger, Uwe Stilla

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

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Year
2014
DOI
10.1109/JSTARS.2014.2311505
Subject
Computers in Earth Sciences / Atmospheric Science

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Text

Benefit of Using Multiple Baselines and Multiple

Aspects for SAR Interferometry of Urban Areas

Michael Schmitt, Student Member, IEEE, Johannes L. Schönberger, and Uwe Stilla, Senior Member, IEEE

Abstract—In this paper, extensive real-data experiments for the investigation of the benefit of exploiting multiple aspects and multiple baselines for the reconstruction of urban surface models by synthetic aperture radar interferometry are documented. These experiments are carried out within a recently proposed reconstruction framework that allows the fusion of almost arbitrary configurations of multi-aspect multi-baseline InSAR data. The results based on airborne decimeter-resolution millimeterwave imagery prove and quantify that multiple baselines help to solve the phase ambiguity problem, while multiple aspects reduce the parts of the scene affected by radar shadowing. In addition, the inherent redundancy provides a significant improvement in the achievable reconstruction accuracy, which is evaluated relative to the reconstruction error common for conventional single-aspect single-baseline SAR interferometry.

Index Terms—Maximum-likelihood estimation, multi-aspect, multi-baseline, synthetic aperture radar interferometry (InSAR), synthetic aperture radar (SAR), urban areas.

I. INTRODUCTION

W HILE synthetic aperture radar interferometry (InSAR)has been used operationally for terrain reconstruction for years [1]–[3], the reconstruction of digital surface models (DSMs) of urban areas from InSARdata is still a challenging task [4], [5]. This is caused by thewell-known layover and shadowing effects,which lead to amixture of phasemeasurements and a lack of exploitable phase observations, respectively, if elevated objects are imaged. Besides, the nontrivial phase unwrapping operation needed for resolving ambiguous phase measurements usually fails at large phase jumps as they appear, e.g., at building edges [6]. Therefore, many sophisticated processing strategies have been proposed during the last decade. Many of them are based on multi-baseline [7]–[11] or multi-aspect techniques [12]–[15]. It has to be mentioned, however, that most of the hitherto proposed multi-baseline approaches rely on repeat-pass data acquired by spaceborne platforms. This introduces a couple of drawbacks: First, the capability of timely data delivery unique to weather-independent SAR sensors is lost, if imagery is collected over relatively long periods of time. Second, multitemporal SAR data suffer from decorrelation of moving objects (e.g., cars) and vegetated areas (e.g., trees), which frequently appear in urban regions. Third, state-of-the-art satellite missions only allow for the fusion of ascending and descending acquisitions (i.e., two opposing aspects), which only partly helps to achieve a comprehensive analysis of the scene of interest.

Therefore, this paper focuses on the concept of multiaspect multi-baseline SAR interferometry (MAMBInSAR) in a maximum-likelihood (ML)-based estimation framework with a special emphasis on the exploitation of airborne single-pass

InSAR data. With this sensor configuration, highly coherent data from almost arbitrary aspect angles can be acquired in very short time, thus providing the full advantages and flexibilities of radar remote sensing. In this context, the proposed work can be seen as an extension and generalizationof existingMLapproaches in SAR interferometry. The basic concept for low-resolution twoimage interferograms of rural areas was first introduced by [16].

A theoretical investigation ofMLSAR interferometry in the phase unwrapping context was presented in [17], while [18] was among the first manuscripts demonstrating the exploitation of multibaseline data of natural terrain by a ML framework. Finally, [19] extended the well-established persistent scatterer technique to the distributed scatterer case, also employing the ML principle.

The most sophisticated approach so far was described in [20], allowing for the fusion of arbitrary single-baseline interferograms acquired over mountainous landscapes.

In contrast, the main purpose of the presented article is to show the benefit gained by fusing SAR measurements from multiple baselines and multiple aspect angles simultaneously. Therefore, many possible different acquisition configurations are investigated based on the MAMBInSAR algorithm first proposed in [21].

The remainder of the text is organized as follows: Section II describes the ML-based framework for MAMBInSAR; Section III introduces the airborne SAR system and the test area that is used for the experiments; and Section IV shows the experimental results that are further discussed in Section V.

II. ML-BASED MULTI-ASPECT MULTI-BASELINE SAR

INTERFEROMETRY

Recently, in [21], a ML-based approach for multi-aspect multibaseline SAR interferometry was introduced. The general idea of this algorithm is inspired by [20], which, however, is limited to the fusion of individual single-baseline interferograms with different master images each. In contrast, our more general estimation framework also enables to consider correlated interferograms as they are frequently delivered by airborne single-pass multibaseline sensors. Furthermore, the focus has shifted from medium-resolution SAR data of mountainous terrain to decimeterresolution InSAR imagery of densely built-up urban areas.

Manuscript received October 24, 2013; revised February 19, 2014; accepted

March 08, 2014. Date of publication April 13, 2014; date of current version

December 22, 2014. This work was supported by the German Research

Foundation (DFG project STI 545/4-1).

The authors arewith theDepartment of Photogrammetry andRemote Sensing,

Technische Universitaet Muenchen (TUM), Munich 80333, Germany (e-mail: michael.schmitt@bv.tum.de; johannes.schoenberger@tum.de; stilla@tum.de).

Color versions of one ormore of the figures in this paper are available online at http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/JSTARS.2014.2311505