Oil spill and ship detection using high resolution polarimetric X-band SAR data

Among illegal human activities, marine pollution and target detection are the key concern of Maritime Security and Safety. This thesis deals with oil spill and ship detection using high resolution X-band polarimetric SAR (PolSAR). Polarimetry aims at analysing the polarization state of a wave field, in order to obtain physical information from the observed object. In this dissertation PolSAR techniques are suggested as improvement of the current State-of-the-Art of SAR marine pollution and target detection, by examining in depth Near Real Time suitability

Remote Sensing for Landslide Investigations: An Overview of Recent Achievements and Perspectives

Landslides represent major natural hazards, which cause every year significant loss of lives and damages to buildings, properties and lifelines. In the last decades, a significant increase in landslide frequency took place, in concomitance to climate change and the expansion of urbanized areas. Remote sensing techniques represent a powerful tool for landslide investigation: applications are traditionally divided into three main classes, although this subdivision has some limitations and borders are sometimes fuzzy. The first class comprehends techniques for landslide recognition, i.e., the mapping of past or active slope failures. The second regards landslide monitoring, which entails both ground deformation measurement and the analysis of any other changes along time (e.g., land use, vegetation cover). The third class groups methods for landslide hazard analysis and forecasting. The aim of this paper is to give an overview on the applications of remote-sensing techniques for the three categories of landslide investigations, focusing on the achievements of the last decade, being that previous studies have already been exhaustively reviewed in the existing literature. At the end of the paper, a new classification of remote-sensing techniques that may be pertinently adopted for investigating specific typologies of soil and rock slope failures is proposed

TanDEM-X multiparametric data features in sea ice classification over the Baltic sea

In this study, we assess the potential of X-band Interferometric Synthetic Aperture Radar imagery for automated classification of sea ice over the Baltic Sea. A bistatic SAR scene acquired by the TanDEM-X mission over the Bothnian Bay in March of 2012 was used in the analysis. Backscatter intensity, interferometric coherence magnitude, and interferometric phase have been used as informative features in several classification experiments. Various combinations of classification features were evaluated using Maximum likelihood (ML), Random Forests (RF) and Support Vector Machine (SVM) classifiers to achieve the best possible discrimination between open water and several sea ice types (undeformed ice, ridged ice, moderately deformed ice, brash ice, thick level ice, and new ice). Adding interferometric phase and coherence-magnitude to backscatter-intensity resulted in improved overall classification performance compared to using only backscatter-intensity. The RF algorithm appeared to be slightly superior to SVM and ML due to higher overall accuracies, however, at the expense of somewhat longer processing time. The best overall accuracy (OA) for three methodologies were achieved using combination of all tested features were 71.56, 72.93, and 72.91% for ML, RF and SVM classifiers, respectively. Compared to OAs of 62.28, 66.51, and 63.05% using only backscatter intensity, this indicates strong benefit of SAR interferometry in discriminating different types of sea ice. In contrast to several earlier studies, we were particularly able to successfully discriminate open water and new ice classes.Peer reviewe

Automatic near real-time flood detection in high resolution X-band synthetic aperture radar satellite data using context-based classification on irregular graphs

This thesis is an outcome of the project “Flood and damage assessment using very high resolution SAR data” (SAR-HQ), which is embedded in the interdisciplinary oriented RIMAX (Risk Management of Extreme Flood Events) programme, funded by the Federal Ministry of Education and Research (BMBF). It comprises the results of three scientific papers on automatic near real-time flood detection in high resolution X-band synthetic aperture radar (SAR) satellite data for operational rapid mapping activities in terms of disaster and crisis-management support. Flood situations seem to become more frequent and destructive in many regions of the world. A rising awareness of the availability of satellite based cartographic information has led to an increase in requests to corresponding mapping services to support civil-protection and relief organizations with disaster-related mapping and analysis activities. Due to the rising number of satellite systems with high revisit frequencies, a strengthened pool of SAR data is available during operational flood mapping activities. This offers the possibility to observe the whole extent of even large-scale flood events and their spatio-temporal evolution, but also calls for computationally efficient and automatic flood detection methods, which should drastically reduce the user input required by an active image interpreter. This thesis provides solutions for the near real-time derivation of detailed flood parameters such as flood extent, flood-related backscatter changes as well as flood classification probabilities from the new generation of high resolution X-band SAR satellite imagery in a completely unsupervised way. These data are, in comparison to images from conventional medium-resolution SAR sensors, characterized by an increased intra-class and decreased inter-class variability due to the reduced mixed pixel phenomenon. This problem is addressed by utilizing multi-contextual models on irregular hierarchical graphs, which consider that semantic image information is less represented in single pixels but in homogeneous image objects and their mutual relation. A hybrid Markov random field (MRF) model is developed, which integrates scale-dependent as well as spatio-temporal contextual information into the classification process by combining hierarchical causal Markov image modeling on automatically generated irregular hierarchical graphs with noncausal Markov modeling related to planar MRFs. This model is initialized in an unsupervised manner by an automatic tile-based thresholding approach, which solves the flood detection problem in large-size SAR data with small a priori class probabilities by statistical parameterization of local bi-modal class-conditional density functions in a time efficient manner. Experiments performed on TerraSAR-X StripMap data of Southwest England and ScanSAR data of north-eastern Namibia during large-scale flooding show the effectiveness of the proposed methods in terms of classification accuracy, computational performance, and transferability. It is further demonstrated that hierarchical causal Markov models such as hierarchical maximum a posteriori (HMAP) and hierarchical marginal posterior mode (HMPM) estimation can be effectively used for modeling the inter-spatial context of X-band SAR data in terms of flood and change detection purposes. Although the HMPM estimator is computationally more demanding than the HMAP estimator, it is found to be more suitable in terms of classification accuracy. Further, it offers the possibility to compute marginal posterior entropy-based confidence maps, which are used for the generation of flood possibility maps that express that the uncertainty in labeling of each image element. The supplementary integration of intra-spatial and, optionally, temporal contextual information into the Markov model results in a reduction of classification errors. It is observed that the application of the hybrid multi-contextual Markov model on irregular graphs is able to enhance classification results in comparison to modeling on regular structures of quadtrees, which is the hierarchical representation of images usually used in MRF-based image analysis. X-band SAR systems are generally not suited for detecting flooding under dense vegetation canopies such as forests due to the low capability of the X-band signal to penetrate into media. Within this thesis a method is proposed for the automatic derivation of flood areas beneath shrubs and grasses from TerraSAR-X data. Furthermore, an approach is developed, which combines high resolution topographic information with multi-scale image segmentation to enhance the mapping accuracy in areas consisting of flooded vegetation and anthropogenic objects as well as to remove non-water look-alike areas

Ocean Remote Sensing with Synthetic Aperture Radar

The ocean covers approximately 71% of the Earth’s surface, 90% of the biosphere and contains 97% of Earth’s water. The Synthetic Aperture Radar (SAR) can image the ocean surface in all weather conditions and day or night. SAR remote sensing on ocean and coastal monitoring has become a research hotspot in geoscience and remote sensing. This book—Progress in SAR Oceanography—provides an update of the current state of the science on ocean remote sensing with SAR. Overall, the book presents a variety of marine applications, such as, oceanic surface and internal waves, wind, bathymetry, oil spill, coastline and intertidal zone classification, ship and other man-made objects’ detection, as well as remotely sensed data assimilation. The book is aimed at a wide audience, ranging from graduate students, university teachers and working scientists to policy makers and managers. Efforts have been made to highlight general principles as well as the state-of-the-art technologies in the field of SAR Oceanography

Assessment of landslide susceptibility in Structurally Complex Formations by integration of different A-DInSAR techniques

Instability events are recurring phenomena in Southern Italy due to its geological history and tectonic-geomorphological evolution leading to the occurrence of several formations identified as Structurally Complex Formations (SCFs; Esu, 1977) in a territory mainly composed of densely populated areas also in mountainous and hilly regions. SCFs are clay-dominant terrains that, usually, give origin from very-slow to extremely-slow phenomena (Cruden and Varnes, 1996) with a long evolutionary history made up of multiple reactivations that makes difficult their identification, monitoring and susceptibility evaluation. The study has been carried out from point-wise (Bisaccia, Costa della Gaveta and Nerano cases) to wide areas (Palermo province case) where crops out SCFs as the Termini sandstones Formation (CARG, 2011), the Varicoloured Clays of Calaggio Formation (Ciaranfi et al., 1973), the Varicoloured Clays Unit (Mattioni et al., 2006) the Sicilide Unit (Vitale and Ciarcia, 2013 and references therein), the Numidian Flysch (Johansson et al., 1998) and the Corleone Calcarenites (Catalano R. et al., 2002). The aim of this thesis is to produce updated Landslide Inventory Maps and, whenever possible, Landslide Susceptibility Maps following a new approach during the landslide mapping and landslide monitoring stages. The Landslide Inventory Maps have taken into account the combination of geological, geomorphological, and stereoscopic surveys, as well as engineering geological investigations, namely conventional techniques. In addition innovative Advanced-Differential Interferometry Synthetic Aperture Radar (A-DInSAR) techniques have been used: the Coherent Pixels Technique – CPT (Mora et al., 2003; Blanco et al., 2008), the Intermittent Small BAseline Subset – ISBAS (Sowter et al., 2013) and the Ground-Based Synthetic Aperture Radar. Finally, the Weight of Evidence method (van Westen, 1993) has been chosen to generate the Landslide Susceptibility Maps only for the point-wise studies. In the case of Nerano (Province of Naples), the ISBAS analysis on ENVISAT images (for the period 2003-2010) has been carried out and compared with inclinometric and rainfall data. These have revealed several reactivations of a rotational slide + earth flow (Cruden and Varnes, 1996) that involves reworked clay olistostromes and limestone olistoliths inside the Termini sandstones Formation; even in recent years the landslide, despite many engineering works, has given evidence of a continuing activity. The results highlight a very slow movement in the detachment zone (<1 mm/yr), which assumes slightly higher values in the accumulation area (5 mm/yr). The Landslide Susceptibility Map confirms the high levels in the flow track and the accumulation area. In Bisaccia (Province of Avellino), a conglomeratic slab undergoes a Deep Seated Gravitational Slope Deformation (DSGSD; Pasuto and Soldati, 2013 and references therein) due to the bedrock consolidation, made of the Varicoloured Clays of Calaggio Formation. Here the CPT processing on ENVISAT images (covering the period between 2002 and 2010), displays a vertical displacement for the town center, suffering a progressively increasing velocity from the southern (4.2 mm/yr) to the northern (15.5 mm/yr) portion of the slab that localizes four different sectors. The pattern is confirmed from the building damage map. The landslides susceptibility reaches the highest values in the adjacent valleys and at the edges of each sector. Multiple datasets have been employed for the Costa della Gaveta case-study (Province of Potenza), these encompass: ENVISAT, TerraSAR-X and COSMO-SkyMed constellations together with Ground Based Synthetic Aperture Radar (GBSAR). The A-DInSAR data have been compared with stereoscopic analysis and the available rainfall and inclinometric data. The analysis allows for the identification of 16 landslides (complexes and earth flows; Cruden and Varnes, 1996) developed in the Varicoloured Clays Unit that show, according to all the existing instruments, velocities between 1.5 and 30 mm/yr. The western side of Costa della Gaveta slope is the portion which suffers the highest landslides susceptibility levels. In the Province of Palermo (northwestern Sicily) information deriving from A-DInSAR processing, specifically the ISBAS technique, have been focused on three subareas (Piana degli Albanesi, Marineo and Ventimiglia di Sicilia) for a total extension of 182 Km2 where standard A-DInSAR algorithms showed limitations due to the widespread presence of densely vegetated areas. The radar-detected landslides have been validated through field geomorphological mapping and stereoscopic analysis proving to be highly consistent especially with slow phenomena. The outcome has allowed to confirm 152 preexisting landslides, to detect 81 new events and to change 133 previously mapped landslides, modifying their typology, boundary and/or state of activity. The study demonstrates how a better knowledge of landslide development and their cause-effect mechanisms provided by new Earth Observation techniques is useful for Landslide Inventory and Susceptibility Maps. The research project has been carried out at the University of Naples "Federico II", including nine months (September 2013 – May 2014) spent in the United Kingdom, at the British Geological Survey under the supervision of Dr. Francesca Cigna and Dr. Jordan Colm and at the University of Nottingham (Department of Civil Engineering), under the supervision of Dr. Andrew Sowter where the ISBAS technique has been recently developed