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Synthetic Aperture Radar basics
SAR is mostly a coherent airborne or space-borne side-looking radar system mounted on a moving platform and offers dramatically high-resolution remote sensing imagery data. SAR emits series of coherent microwave pulses and records the echoes scattered back from the imaged area in its line of sight (LOS), these records form the complex SAR image and named Single Look Complex (SLC) data. SLC image can be seen as a two-dimensional array of complex-valued pixels. Each pixel is the coherent sum of microwave back-scattered within corresponding resolution cell. The complex number at each pixel presents the amplitude of the back-scattered energy at each scatters and the phase that is determined by the slant range distance from the pixel location to the radar sensor. SAR has several advantages when compared to optical sensors operating in the visible or infrared bands, these advantages:
(1) SAR system can operate at all times (day, night) and in all weather conditions due to the electromagnetic wave characteristics.
(2) SAR is a coherent system that retains both amplitude and the phase of the back-scattered echoes. Thus the SAR images consist of information about the physical proper-ties and the range of the objects.
(3) SAR uses polarized radiation (Horizontal and vertical polarization), so it can exploit the polarization signature of the imaged scatters and obtain additional information about the structure of such scatterers.
(4) Modern SAR sensors transmit echoes over large bandwidth (in order of tens/hun-dreds of MHz), thus leading to fine spatial resolution along the slant range direction.
A cylindrical coordinate system is used to specify the 3D position of scatterers Figure 2.1: (1) The azimuth axis x defines the position of the scatterers along the sensor path. (2) The slant range axis r defines the distance of the scatterers from the SAR sensor. (3) The look angle θ represents the angle between the sensor-to-scatterers line and nadir.
Single look complex data
SAR system collects raw data and compresses them in the range and azimuth direc-tions. The compression of the raw data produces the focused SAR image. The focused SAR image is known as Single Look Complex (SLC) image and can be seen as a two-dimensional array of complex-valued pixels. Each pixel has a complex number, where the amplitude of this complex number presents the coherent sum of the microwave field backscattered by all the scatters (rocks, sands, vegetation, buildings, etc.) within corresponding resolution cells projected on the Earth surface. The phase of the complex number is determined by the slant range distance from the pixel location to the sensor location.
SAR resolution
The resolution of the SAR image is one of the most important characteristics of the SAR system. SAR data pixels are characterized by spatial and temporal resolutions. Spatial reso-lution is defined as the ability of the SAR system to distinguish between two closely targets. In SAR systems, there are two resolutions, azimuth resolution, and range resolution. An-other resolution that should be taken into account when applying a multi-temporal time series analysis of the SAR data is the temporal resolution. It is determined by the revisit time of the SAR sensor at the same imaged area.
Speckle in SAR images
SAR data are subjected to speckle noise which interferes with the sharpness of the im-ages. Speckle noise affects both the amplitude and the phase of radar signals. Speckle noise degrades the quality of the images and therefore affects the performance of their analysis and interpretations.
SAR data characteristics
The main data-sets described hereby are the ones utilized in the dissertation. These data-sets consists of spaceborne and airborne data acquired over different locations. For the TomoSAR study applied over tropical forests, airborne SAR data acquired by different platforms (L-band UAVSAR from JPL/NASA and P-band SETHI from ONERA) are utilized. While, for InSAR analysis, we used sentinel-1 C-band SAR data acquired over Lebanon as a case study.
Sentinel-1 data
The SENTINEL-1 spaceborne mission is the European Radar Observatory for the Copernicus joint initiative of the European Commission (EC) and the European Space Agency (ESA). This Copernicus Program satellite constellation consists of two satellites, Sentinel-1A and Sentinel-1B, respectively launched in 2014 and 2016 [Sentinel, 2013]. Sentinel-1 is an imaging radar that operates at C-band (wavelength range: 3.75-7.50cm). Sentinel-1 mission is designed to provide high-resolution imagery data that cover all global landmasses, coastal zones, and shipping routes. Sentinel-1 sensor operates in four acqui-sition modes, the configuration parameters and imaging modes of the Sentinel-1 sensor are shown in Table 2.1.
JPL/NASA UAVSAR data and ONERA SETHI data
The data-sets acquired over Gabon Lopé National Park consists of seven fully polari-metric Single Look Complex (SLC) data L-band NASA/JPL UAVSAR from AfriSAR data con-ducted during the AfriSAR campaign in 2016. UAVSAR platform operates at a frequency band 1217.5–1297.5 MHz. The imaged area is about 22 km wide, with incidence angles ex-tending from 25 to 60 degrees. The scanning is performed by the Gulfstream III jet at that flight at an altitude of 12.5 km. The ground range and azimuth resolution of the UAVSAR polarimetric SLC are 1 m and 2.5 m, respectively. The configuration of L-band UAVSAR is shown in Table 2.2. P-band data consists of 13 fully polarimetric SLC data acquired by ONERA over the Lopé in the AfriSAR airborne campaign (July 2015). The data collection is performed using the SETHI SAR system developed by ONERA and onboard a Falcon 20 aircraft. The pixel resolution of the SLC images is 1.54 m in azimuth range and 3 m in slant range. The configuration of P-band SETHI data including acquisition geometry, bandwidth, carrier frequency, and aircraft altitude are shown in Table 2.3.
The tomographic 125 MHz data set considered in the Paracou experimental site con-sists of six fully polarimetric SLC images at L and P-band acquired on 24 August 2009. All the acquisitions took about 1 h (from 9:00 a.m. to 10:00 a.m.), resulting in almost no tem-poral decorrelation.
In this thesis, we also used six tracks fully polarimetric images acquired during the TropiSAR campaign over Paracou.
Permanent scatterer interferometry
InSAR results accuracy is strongly affected by the atmospheric disturbances and phase noise, thus a proposition was found to use Permanent scatterers (PS) and distributed scat-terers (DS) starting from long temporal series of interferometric SAR images. Permanent Scatterers Interferometry (PSI) is an advanced type of DInSAR based on large number of stacked SAR images that make it able to estimate the deformation time series, the average displacement rate, and the RTE. Different PSI techniques were proposed in the last years, the first PSI technique was developed by [Ferretti et al., 2001] for long-term deformation monitoring, especially in urban environments. After that, similar approaches have been proposed by different scientists [Adam et al., 2003; Hooper et al., 2004; Meyer et al., 2006]. Different PSI techniques are followed by this approach, the small baseline subset SBAS technique which is one of the most important and documented technique [Berardino et al., 2002; Lanari et al., 2004; Pepe and Lanari, 2006; Pepe et al., 2011]. A similar approach was proposed by [Mora et al., 2003]. Another approach was done based on step-wise linear functions for deformation estimates are described in [Crosetto et al., 2008; Werner et al., 2003]. Recently a new algorithm SqueezeSAR is proposed to jointly process PS and DS taking into account their different statistical behavior. SqueeSAR is proposed by [Ferretti et al., 2011]. More relevant contributions are: 1. Adapting LAMBDA method used in GPS to the PSI parameter estimation [Kampes and Hanssen, 2004]. 2. Use of different adaptive deformation model in PSI [Van Leijen and Hanssen, 2007]. 3. Methods to derive different characteristics of PS and to classify them especially in urban areas [Perissin and Ferretti, 2007].
All the mentioned PSI techniques differ either in method choosing the PS candidate or in the deformation model used in the PSI estimation. Since the PSI needs to retrieve the coordinates of single points, the imaging in 3D is done by tomography, estimating the reflectivity in elevation is derived. In urban areas, the multiple scatterers must be consid-ered in the tomographic 3D reconstruction and model selection must be applied in the estimation of the relevant scatterers. The first experiment on SAR tomography was carried out in the laboratory, under ideal experimental conditions, and by using airborne systems [Reigber and Moreira, 2000]. Another SAR tomography method is advanced imaging in the 4D (space-time SAR imaging) enhancing another than the separation of the point scat-terers in the same azimuth-range resolution cell, it estimates their relative deformation also [Fornaro and Serafino, 2006]. The SAR tomography is a spectrum estimation prob-lem along with a specific resolution cell, the complex SAR image is the sampled Fourier transform of the reflectivity function in the elevation direction. There are many different spectrum estimation strategies are used, such as singular value decomposition (SVD) and nonlinear least square (NLS). Thus SAR tomography is a young method to determine the 3D reflectivity function, depending on multi pass acquisitions, and is discussed in different publications [Fornaro et al., 2006; Reigber and Moreira, 2000].
Table of contents :
I Introduction and Synthetic Aperture Radar algorithms and appli- cations
1 Introduction
1.1 General context
1.2 SAR missions
1.3 Research problem and Objectives
1.4 Thesis contributions
1.5 Thesis outline
2 Synthetic Aperture Radar algorithms and applications
2.1 Synthetic Aperture Radar basics
2.1.1 Single look complex data
2.1.2 SAR resolution
2.1.3 Speckle in SAR images
2.1.4 SAR data characteristics
2.1.5 Sentinel-1 data
2.1.6 Airborne data
2.1.7 SAR applications
2.2 Interferometry SAR
2.3 Permanent scatterer interferometry
2.4 Tomography SAR background
2.5 phase calibration
2.5.1 Phase Calibration algorithms
2.5.2 Phase Calibration by Permanent Scatterer Interferometry (PS-InSAR)
2.5.3 Phase Calibration by Algebraic Synthesis (AS)
2.5.4 Phase Calibration by Distributed Targets (DS-InSAR)
II Tomography SAR for forested areas
3 L-band UAVSAR tomographic imaging in dense forests, Gabon forests
3.1 Introduction
3.2 Materials andMethods
3.2.1 Study area
3.2.2 Data-sets
3.2.3 TomoSAR background
3.2.4 TomoSAR Phase Calibration
3.2.5 TomoSAR inversion
3.2.6 Estimate forests top height
3.3 Results
3.4 Discussion
3.5 Conclusion
4 Monitoring tropical forest structure using SAR tomography at L and P-band
4.1 Introduction
4.2 Materials andMethods
4.2.1 Study Area
4.2.2 Data-Set
4.2.3 Tomography SAR
4.2.4 Tomography Inversion
4.2.5 TomoSAR Phase Calibration
4.2.6 Forest strucuture indices
4.2.7 Forest Structure Parameters
4.3 Results
4.3.1 Limitation of L-Band TomoSAR in Tropical Forest (TropiSAR Data) .
4.3.2 TomoSAR Profiles at L- and P-Band (AfriSAR Data)
4.3.3 TomoSARMulti-Layers
4.3.4 Forest Top Height Estimation from L- and P-Band
4.4 Discussion
4.4.1 Limitation of L-Band TomoSAR in Tropical Forest (TropiSAR Data) .
4.4.2 TomoSAR Profiles at L- and P-Band (AfriSAR Data)
4.4.3 TomoSARMulti-Layers
4.4.4 Forest Top Height Estimation from L- and P-Band
4.4.5 Forest Structure Indices and Parameters
4.5 Conclusions
III Land-subsidencemonitoring using PS/DSInSAR technique
5 Comparison of multi temporal radar interferometry techniques using Sentinel-1: Lebanon case study
5.1 Introduction
5.2 Materials andMethods
5.2.1 Interferometry
5.2.2 PSI
5.2.3 PS/DS
5.2.4 Detail processing from step 6 stamps
5.3 Results
5.3.1 Lebanon site
5.3.2 PS results
5.3.3 PS/DS results
5.4 Discussion
5.4.1 Sentinel-1
5.4.2 Deformation velocity
5.4.3 Phase calibration via phase center double localization
5.5 Conclusion
General Conclusion
TomoSAR perspectives
InSAR perspectives
Bibliography
