Observation of near-Earth outer space is an important task for astronomers and scientists at the present time. This task is to determine the coordinate and non-coordinate characteristics of artificial space objects. According to data obtained, a catalogue of objects is created, which should be maintained and updated. For this, optical means (telescopes) can be involved. The data from these means should be processed in order to obtain information about objects in space.
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Processing of Space Monitoring Information
Moscow Institute of Physics and TechnologyAbout this Course
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Advanced Level
Learners are expected to know basic mathematical analysis, linear algebra, information system theory, mathematical methods of optimization.
Approx. 14 hours to complete
English
Subtitles: English
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Advanced Level
Learners are expected to know basic mathematical analysis, linear algebra, information system theory, mathematical methods of optimization.
Approx. 14 hours to complete
English
Subtitles: English
Offered by
Moscow Institute of Physics and Technology
Московский физико-технический институт (Физтех) является одним из ведущих вузов страны и входит в основные рейтинги лучших университетов мира. Институт обладает не только богатой историей – основателями и профессорами института были Нобелевские лауреаты Пётр Капица, Лев Ландау и Николай Семенов – но и большой научно-исследовательской базой.
Syllabus - What you will learn from this course
3 hours to complete
Week 1. Image processing pipeline
The lectures of the first week cover the following stages of space monitoring information processing: background suppression; detection of isolated groups of bright pixels (areas); estimation of shape parameters of extracted bright areas for classification as stars or tracks; estimation of positions of detected stars; estimation of parameters of detected tracks; astrometric reduction of the image; photometric reduction.
3 hours to complete
18 videos (Total 50 min), 3 readings, 1 quiz
18 videos
Promo1m
Introduction2m
Background suppression1m
Median filtering1m
Iterative sigma clipping2m
Proposed filter for background suppression1m
Detection of isolated groups of bright pixels2m
Estimation of shape parameters of extracted bright areas for classification as stars or tracks2m
Estimation of stars and satellite tracks parameters2m
Astrometric reduction of the image 4m
Star Catalogue Requirements2m
Determination of correspondences between detected stars and catalogue4m
Hashing the quads2m
Indexing the catalogue3m
Verification of hypothesis6m
Transformation estimation1m
Photometric reduction1m
Examples of processed images6m
3 readings
Lecture Notes of the Course10m
Presentations of the Course10m
Literature1h
1 practice exercise
Test 1.130m
3 hours to complete
Week 2. Correlation methods for determining the mutual displacement of images
Second week discusses a method for determining the mutual displacement of images based on the presence of spatial correlation of the observation background. This method can be used in a situation when the survey is carried out in such a way that the object does not go beyond the field of view of the telescope during the time between two successive frames, and these frames have a significant intersection in absolute angular space.
3 hours to complete
5 videos (Total 16 min), 1 reading, 1 quiz
5 videos
Problem statement2m
Algorithm for determining relative image shifts3m
Linear algorithm of shift estimation3m
Quadratic Algorithm of shift estimation4m
1 reading
Literature1h
1 practice exercise
Programming Assignment 2.11h 30m
3 hours to complete
Week 3. Spatiotemporal methods of filtering observation background
Lectures of the third week describe spatio-temporal methods of filtering the observation background. General principles of these methods are considered. The most commonly used methods are described in detail: the simplest nonparametric time filtering algorithm, adaptive autoregression algorithm and algorithm of calculation filter coefficients as an explicit shift function.
3 hours to complete
4 videos (Total 21 min), 1 reading, 2 quizzes
4 videos
Quality indicators for filtering observation background 1m
Adaptive time filtering algorithms4m
Calculating the filter coefficients as an explicit shift function6m
1 reading
Literature1h
1 practice exercise
Test 3.130m
6 hours to complete
Week 4. Extraction of spatially resolved point objects from digital image
The fourth week is dedicated to selecting and classifying allowed point objects in a digital image. The general limitations of using the proposed methods are considered. The general structure of the optimal algorithm is described. A method for detecting a single object in a fixed window is proposed. Based on this method, the case of determination the decision-making statistics in the image reference points for a symmetric PSF, conjugated with pixel dimensions, is considered. A suboptimal algorithm for detecting objects at image reference points is proposed.
6 hours to complete
6 videos (Total 21 min), 1 reading, 3 quizzes
6 videos
The general structure of the optimal algorithm2m
Detection of a single object in a fixed window4m
Determination of decision making statistics in the reference points of the image with a symmetric PSF, conjugated with pixel dimensions 4m
Suboptimal algorithm of detection the objects in the reference points of an image2m
Examples of image sequence processing4m
1 reading
Literature1h
3 practice exercises
Programming Assignment 4.11h 30m
Programming Assignment 4.21h 30m
Final Assignment1h 30m
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