Skip to main content

PhD Student Position in SAR Interferometry/Tomography ETH Zürich






PhD Student Position in SAR Interferometry/Tomography ETH Zürich


The Earth Observation and Remote Sensing Group, Institute of Environmental Engineering, at ETH Zurich is seeking a PhD student for a research project on Interferometric / Tomographic Techniques in Synthetic Aperture Radar (SAR) Remote Sensing to Monitor Surface Displacements starting from autumn/winter 2020/2021.
Job description
The research focuses on investigating methods and algorithms in the context of synthetic aperture radar (SAR) multibaseline interferometry and SAR tomography for space-based monitoring of ground-surface displacements. By using repeat-pass SAR interferometry, deformation measurements at cm/mm level over longer time spans can be obtained for extended areas. SAR interferometry therefore complements point-based measurement techniques such as observations with total station theodolites, Global Navigation Satellite System (GNSS)-based, or levelling-based measurements of surface displacements.
Mountainous areas with large topographic variations are prone to various geohazards. At the same time, mountainous areas are challenging to monitor with SAR interferometry due to strong and relatively small-scale spatiotemporal variations in the tropospheric conditions, obstructed views (layover and shadow), partial snow or vegetation cover, and other surface processes.
The research builds upon previous work performed in our group and aims at improving the spatiotemporal coverage, the precision, and the automated generation of spaceborne-radar-based maps of surface displacements in mountainous areas.
Your profile
We are looking for a highly motivated candidate holding a master's degree or a diploma in electrical engineering, geomatics engineering, geophysics, physics or a related field with a background in digital signal processing and/or image processing. Previous experience in SAR signal processing or another field of array signal processing is an asset. The successful candidate has strong analytical skills and programming experience in Matlab, Python, C/C++, or equivalent, and is capable to develop and implement signal-processing algorithms in such a programming language. Fluency in English is required (oral and written), and it is essential that the candidate is willing to work in a multidisciplinary and international research team. Applicants should hold a valid driver's license (European Cat. B).
We are offering a position in an attractive research environment within a young, highly motivated, and international research team.





Vineesh V
Assistant Professor of Geography,
Directorate of Education,
Government of Kerala.
https://www.facebook.com/Applied.Geography
http://geogisgeo.blogspot.com

Comments

Popular posts from this blog

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification:   - You have prior knowledge about the c...

History of GIS

The history of Geographic Information Systems (GIS) is rooted in early efforts to understand spatial relationships and patterns, long before the advent of digital computers. While modern GIS emerged in the mid-20th century with advances in computing, its conceptual foundations lie in cartography, spatial analysis, and thematic mapping. Early Roots of Spatial Analysis (Pre-1960s) One of the earliest documented applications of spatial analysis dates back to  1832 , when  Charles Picquet , a French geographer and cartographer, produced a cholera mortality map of Paris. In his report  Rapport sur la marche et les effets du choléra dans Paris et le département de la Seine , Picquet used graduated color shading to represent cholera deaths per 1,000 inhabitants across 48 districts. This work is widely regarded as an early example of choropleth mapping and thematic cartography applied to epidemiology. A landmark moment in the history of spatial analysis occurred in  1854 , when  John Snow  inv...

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...

Architecture of GIS

GIS architecture encompasses the overall design and organization of a Geographic Information System (GIS). The components of GIS architecture include hardware, software, data, people, and methods. The architecture determines how these components interact and work together to create an efficient GIS system. There are two main types of GIS architecture: client-server and web-based architecture. In client-server architecture, GIS software runs on a server and is accessed by users through client computers. The server is responsible for data storage, processing, and analysis, while the client is responsible for data visualization and user interaction. Multiple users can work on the same dataset simultaneously, making it ideal for collaborative work. In web-based architecture, the GIS software is accessed through a web browser, eliminating the need to install software on local machines. The GIS data and software are stored on a server and accessed through a web interface, making it ideal for...