PhD Student Position in SAR Interferometry/Tomography ETH Zürich

https://www.jobs.ethz.ch/job/view/JOPG_ethz_z8iB6dwPZotqZhGZ9t

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

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...

Lidar

LiDAR (Light Detection and Ranging) is an active remote sensing technology that measures distances by illuminating a target with laser pulses and analyzing the time it takes for the reflected light to return. Unlike passive systems (e.g., cameras, multispectral sensors), LiDAR provides its own energy source (laser), allowing it to operate both day and night and even penetrate through vegetation canopies . 🔹 How LiDAR Works (Step-by-Step Process) Laser Pulse Emission The system emits rapid, short pulses of laser light (commonly in the near-infrared wavelength, 1064 nm ). Some systems emit up to hundreds of thousands of pulses per second . Interaction with Target Surface The laser beam strikes objects such as vegetation, buildings, or bare ground. Depending on the object's structure, part of the pulse may scatter or reflect. Return Signal Detection The sensor records multiple returns : First Return → typically vegetation canopy tops. ...

Geometric Correction

When satellite or aerial images are captured, they often contain distortions (errors in shape, scale, or position) caused by many factors — like Earth's curvature, satellite motion, terrain height (relief), or the Earth's rotation . These distortions make the image not properly aligned with real-world coordinates (latitude and longitude). 👉 Geometric correction is the process of removing these distortions so that every pixel in the image correctly represents its location on the Earth's surface. After geometric correction, the image becomes geographically referenced and can be used with maps and GIS data. Types 1. Systematic Correction Systematic errors are predictable and can be modeled mathematically. They occur due to the geometry and movement of the satellite sensor or the Earth. Common systematic distortions: Scan skew – due to the motion of the sensor as it scans the Earth. Mirror velocity variation – scanning mirror moves at a va...

Image Transformation

In remote sensing, image transformation is used to create new images by manipulating existing ones, enhancing the information extracted from each pixel. Multi-image manipulation and other transformations allow analysts to highlight specific features, compare time-series data, or reduce data dimensions for better interpretation. 1. Multi-Image Manipulation This technique involves applying mathematical operations to multiple images, typically from different bands, dates, or sensors, to highlight specific features or changes. Common methods include: a. Image Addition Purpose : Highlights areas of similarity or enhances features when the same objects appear in multiple images. How it Works : By adding pixel values from two images, the output image emphasizes areas where brightness is high in both. Example : Enhancing overall brightness or detecting seasonal changes by adding images from different times. b. Image Subtraction Purpose : Identifies differences between images, useful for chan...

Spatial Feature Manipulation and Filtering

1. Spatial Feature Manipulation: Spatial Frequency Spatial frequency describes how quickly brightness changes across an image. It can reveal different levels of detail, based on how often these changes occur: High Spatial Frequency : Areas where brightness changes quickly, like edges, fine textures, or detailed patterns. These parts contain high detail but can also include noise. Low Spatial Frequency : Areas with gradual changes in brightness, like smooth surfaces or broad homogeneous areas. These regions are usually featureless, such as water bodies or large agricultural fields. Zero Spatial Frequency : Represents a completely uniform area with no brightness variation. This area is very smooth and has no texture or detail. By manipulating spatial frequencies, we can enhance or suppress certain features in an image. Spatial filtering is one of the primary methods to perform this manipulation. 2. Spatial Filtering and Spatial Domain Spatial filtering is the process of applying mathe...

Vineesh V, Geography

Search This Blog