With the launch of new satellite constellations (like Sentinel and Planet) and disclosure of archived satellite images (like Landsat) more and more dense time series of observations become available that can be used to monitor phenomena and activities on earth.
NLR is developing a monitoring system that can automatically process and analyse series of images from different types of satellites (and airborne platforms and drones). The monitoring system contains several elements that need to be filled in: easy access to the raw data, pre-processing of the data to calibrated and integrated time series, application of analytics to extract patterns and anomalies, and create visualisations and products of the results for decision support.
Objective of this assignment is to enhance the pre-processing element, namely the radiometric and atmospheric calibration of series of optical satellite images from multiple satellites.
Methodologies exist for the correction of a single satellite image, using the satellite sensor calibration information and an atmospheric correction model. The parameters on the atmosphere and also sensor calibration values are of limited accuracy however. As a consequence in practise the calibrated images have significant inaccuracies, which complicate the analysis of multiple observations over time.
With the availability of larger series of observations it is possible to use the information of the complete series. The series of observations can be used to determine corrections based on objects with constant reflection patterns (pseudo invariant features or PIFs) or known reflection patterns over time (e.g. vegetation NDVI profile over time). Based on this also systematic differences between different type of sensors can be found.
During the assignment an approach for this will be defined, implemented and validated for an actual dataset of multi-sensor satellite images.
Research questions to be answered:
- What are suitable methods and tools for radiometric and atmospheric correction of satellite images?
- How can information on features with known spectral behaviour be used to optimize the correction?
- How can information from series of images be used to optimize the correction?
Tasks to be foreseen:
- Literature study on methods and tools for radiometric and atmospheric correction of satellite imagery.
- Selection of a test dataset based on Landsat, Sentinel, RapidEye and Dove images.
- Application of radiometric and atmospheric correction with available correction tools (Erdas, Envi, ATCOR).
- Selection/definition and Implementation of a time series based correction method, by making use of features with invariant or know reflection characteristics over time.
- A report in which the research questions are answered and the test results are described.
- An implemented time series based correction radiometric and atmospheric correction method.
When sufficient results become available, a publication of a paper in cooperation with an NLR expert could be achieved for presenting in an (inter)national forum.
- Master student in Geomatics, Aerospace, Informatics, Physics or equivalent, final phase;
- Interest in the area of satellite earth observation and image processing;
- Enthusiasm and willingness to work with existing image processing tools and implement new software routines (Matlab/Python/Java), not afraid to learn something new;
- Practical, “Getting Things Done” mentality;
- We prefer to fill in this assignment as a graduation project, but scoping the work to an internship can be discussed.
- An inspiring high-tech aerospace-oriented working environment;
- Informal culture with room for personal initiative, in which results and commitment are important pillars.
- A compensation for general expenditures and travel expenses
6 months. Start: as soon as possible.
The assignment will be performed at the NLR ASIS department in Marknesse (partial performance in Amsterdam can be discussed).
The ISR and Space Utilisation department (ASIS) works on the application of satellite, aircraft and drone observations for military and civil applications and on sensor chains for small satellites and drones.
Information & application
For more information or to discuss possibilities for a graduation project in this field, please contact Ir. Mark van Persie (firstname.lastname@example.org, +31 88 511 4256)
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Datum : 05/11/2018
Locatie : Marknesse
Uren : 40
Opleidingsniveau : University
Werkniveau : Graduation project (preferably)
Achtergrond : Geomatics, Aerospace, Informatics, Physics or equivalent