High resolution radargrammetry with COSMO-SkyMed, TerraSAR-X and RADARSAT-2 imagery: development and implementation of an image orientation model for Digital Surface Model generation

Digital Surface and Terrain Models (DSM/DTM) have large relevance in several territorial applications, such as topographic mapping, monitoring engineering, geology, security, land planning and management of Earth's resources. The satellite remote sensing data offer the opportunity to have continuous observation of Earth's surface for territorial application, with short acquisition and revisit times. Meeting these requirements, the SAR (Synthetic Aperture Radar) high resolution satellite imagery could offer night-and-day and all-weather functionality (clouds, haze and rain penetration). Two different methods may be used in order to generate DSMs from SAR data: the interferometric and the radargrammetric approaches. The radargrammetry uses only the intensity information of the SAR images and reconstructs the 3D information starting from a couple of images similarly to photogrammetry. Radargrammetric DSM extraction procedure consists of two basic steps: the stereo pair orientation and the image matching for the automatic detection of homologous points. The goal of this work is the definition and the implementation of a geometric model in order to orientate SAR imagery in zero Doppler geometry. The radargrammetric model implemented in SISAR (Software per Immagini Satellitari ad Alta Risoluzione - developed at the Geodesy and Geomatic Division - University of Rome "La Sapienza") is based on the equation of radar target acquisition and zero Doppler focalization Moreover a tool for the SAR Rational Polynomial Coefficients (RPCs) generation has been implemented in SISAR software, similarly to the one already developed for the optical sensors. The possibility to generate SAR RPCs starting from a radargrammetric model sounds of particular interest since, at present, the most part of SAR imagery is not supplied with RPCs, although the RPFs model is available in several commercial software. Only RADARSAT-2 data are supplied with vendors RPCs. To test the effectiveness of the implemented RPCs generation tool and the SISAR radargrammetric orientation model the reference results were computed: the stereo pairs were orientated with the two model. The tests were carried out on several test site using COSMO-SkyMed, TerraSAR-X and RADARSAT-2 data. Moreover, to evaluate the advantages and the different accuracy between the orientation models computed without GCPs and the orientation model with GCPs a Monte Carlo test was computed. At last, to define the real effectiveness of radargrammetric technique for DSM extraction and to compare the radrgrammetric tool implemented in a commercial software PCI-Geomatica v. 2012 and SISAR software, the images acquired on Beauport test site were used for DSM extraction. It is important underline that several test were computed. Part of this tests were carried out under the supervision of Prof. Thierry Toutin at CCRS (Canada Centre of Remote Sensing) where the PCI-Geomatica orientation model was developed, in order to check the better parameters solution to extract radargrammetric DSMs. In conclusion, the results obtained are representative of the geometric potentialities of SAR stereo pairs as regards 3D surface reconstruction

Offshore oil spill detection using synthetic aperture radar

Among the different types of marine pollution, oil spill has been considered as a major threat to the sea ecosystems. The source of the oil pollution can be located on the mainland or directly at sea. The sources of oil pollution at sea are discharges coming from ships, offshore platforms or natural seepage from sea bed. Oil pollution from sea-based sources can be accidental or deliberate. Different sensors to detect and monitor oil spills could be onboard vessels, aircraft, or satellites. Vessels equipped with specialised radars, can detect oil at sea but they can cover a very limited area. One of the established ways to monitor sea-based oil pollution is the use of satellites equipped with Synthetic Aperture Radar (SAR).The aim of the work presented in this thesis is to identify optimum set of feature extracted parameters and implement methods at various stages for oil spill detection from Synthetic Aperture Radar (SAR) imagery. More than 200 images of ERS-2, ENVSAT and RADARSAT 2 SAR sensor have been used to assess proposed feature vector for oil spill detection methodology, which involves three stages: segmentation for dark spot detection, feature extraction and classification of feature vector. Unfortunately oil spill is not only the phenomenon that can create a dark spot in SAR imagery. There are several others meteorological and oceanographic and wind induced phenomena which may lead to a dark spot in SAR imagery. Therefore, these dark objects also appear similar to the dark spot due to oil spill and are called as look-alikes. These look-alikes thus cause difficulty in detecting oil spill spots as their primary characteristic similar to oil spill spots. To get over this difficulty, feature extraction becomes important; a stage which may involve selection of appropriate feature extraction parameters. The main objective of this dissertation is to identify the optimum feature vector in order to segregate oil spill and ‘look-alike’ spots. A total of 44 Feature extracted parameters have been studied. For segmentation, four methods; based on edge detection, adaptive theresholding, artificial neural network (ANN) segmentation and the other on contrast split segmentation have been implemented. Spot features are extracted from both the dark spots themselves and their surroundings. Classification stage was performed using two different classification techniques, first one is based on ANN and the other based on a two-stage processing that combines classification tree analysis and fuzzy logic. A modified feature vector, including both new and improved features, is suggested for better description of different types of dark spots. An ANN classifier using full spectrum of feature parameters has also been developed and evaluated. The implemented methodology appears promising in detecting dark spots and discriminating oil spills from look-alikes and processing time is well below any operational service requirements

Measuring Coseismic Deformation With Spaceborne Synthetic Aperture Radar: A Review

In the past 25 years, space-borne Synthetic Aperture Radar imagery has become an increasingly available data source for the study of crustal deformation associated with moderate to large earthquakes (M > 4.0). Coseismic surface deformation can be measured with several well-established techniques, the applicability of which depends on the ground displacement pattern, on several radar parameters, and on the surface properties at the time of the radar acquisitions. The state-of-the-art concerning the measurement techniques is reviewed, and their application to over 100 case-studies since the launch of the Sentinel-1a satellite is discussed, including the performance of the different methods and the data processing aspects, which still constitute topics of ongoing research

Extension of Wavenumber Domain Focusing for spotlight COSMO-SkyMed SAR Data

In this work we describe a method to handle curved orbits in wavenumber domain focusing algorithm for high-resolution SAR data acquired by Low Earth Orbit satellites using spotlight mode. The stand..

Remote sensing of snow-cover for the boreal forest zone using microwave radar

This doctoral dissertation describes the development of an operationally feasible snow monitoring methodology utilizing spaceborne synthetic aperture radar (SAR) imagery, intended for hydrological applications on the boreal forest zone. The snow-covered area (SCA) estimation methodology developed is characterized using extensive satellite-based datasets, including SAR-based estimation and optical reference data gathered during the snow-melt seasons of 1997-1998, 2000-2002 and 2004-2006 from northern Finland. The methodology applies satellite-based C-band SAR data for snow monitoring during the spring snow-melt season. The SCA information can be utilized for river discharge forecasting and flood predictions and for the optimization of hydropower production. The development efforts included 1) demonstration of a forest compensation algorithm, 2) establishing the use of wide-swath SAR data 3) development of a weather station assimilation procedure and 4) creation of an enhanced reference image selection algorithm for the SCA estimation methodology. The feasibility of a proposed, non-boreal forest specific, SAR-based SCA estimation method was evaluated for the boreal forest zone. The acquired results were compared with the characteristics determined for the boreal-forest specific methodology developed within this dissertation. These results can be used when selecting appropriate SCA estimation approaches for future snow monitoring systems whether conducted in different regions or intended for larger i.e. continental or global scale purposes. An automatic processing system for SCA estimation was developed and demonstrated as part of this work; the system has been delivered to the Finnish Environment Institute for operational use