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Ktisis
2022
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Χαρτογράφηση της ευρύτερης περιοχής στο λιμάνι της Βηρυτού, έπειτα από την έκρηξη στις 4 Αυγούστου 2020 με τη χρήση δορυφόρων Sentinel

Χαρτογράφηση της ευρύτερης περιοχής στο λιμάνι της Βηρυτού, έπειτα από την έκρηξη στις 4 Αυγούστου 2020 με τη χρήση δορυφόρων Sentinel

Abstract

The topic I chose for my dissertation is the Sentinel-2 satellite image classification for the disaster that occurred on 4/8/20 in the port of Beirut after an explosion. In the dissertation we used the SNAP program, and two Sentinel-2 satellite images. Through the thesis we could elaborate the steps on how to properly perform a supervised classification using the appropriate software. The classification is done to identify the categories of all terrestrial objects where they can consist of lakes, rivers, dams, sea, mountains, land, vegetation and urban areas. The initial classification process was done in the image before the disaster in the port of Beirut, in which the categories of objects were made as follows: soil, sea, vegetation and urban area. The Random Forest Classifier, Maximum Likelihood Classifier and Minimum Distance Classifier were then classified. Based on these results, the best classification was made based on the Random Forest Classifier. We then placed the classes in the second Sentinel 2 image after the disaster occurred and created the disaster class. Finally, we repeated the classification only in the category Random Forest Classifier for the second image which was the best classification before and the classification based on the disaster that has taken place in the port of Beirut came out. Το θέμα που επιλέχτηκε στην πτυχιακή εργασία είναι η ταξινόμηση δορυφορικής εικόνας Sentinel-2 για την καταστροφή που έγινε στις 4/8/20 στο λιμάνι της Βηρυτού μετά από έκρηξη. Στην πτυχιακή εργασία χρησιμοποιήσαμε το πρόγραμμα SNAP και δύο δορυφορικές εικόνες Sentinel-2. Αρχικά γίνεται σύγκριση με βάση της εικόνες που πάρθηκαν πριν και μετά την έκρηξη. Παράλληλα μπορούμε να εφαρμόσουμε τα βήματα επιβλεπόμενης ταξινόμησης χρησιμοποιώντας κατάλληλα λογισμικά. Η αρχική διαδικασία της ταξινόμησης έγινε στην εικόνα πριν την καταστροφή στο λιμάνι της Βηρυτού, στην οποία επιλέγηκαν ως κατηγορίες των αντικείμενων οι εξής: χώμα, θάλασσα, βλάστηση και αστική περιοχή. Στην συνέχεια έγινε ταξινόμηση με βάση τις τεχνικές Random Forest Classifier, Maximum Likelihood Classifier και Minimum Distance Classifier. Βάση των αποτελεσμάτων αυτών, η καλύτερη ταξινόμηση έγινε βάση της Random Forest Classifier. Στην συνέχεια τοποθετήσαμε τις ίδιες κλάσεις στην δεύτερη εικόνα Sentinel 2 μετά που έγινε η καταστροφή και δημιουργήσαμε μια επιπρόσθετη κλάση. Τέλος επαναλάβαμε την ταξινόμηση μόνο στην κατηγόρια τις Random Forest Classifier για την δεύτερη εικόνα η οποία ήταν η καλύτερη ταξινόμηση από πριν και βγήκε η ταξινόμηση βάση της καταστροφής που έχει γίνει στο λιμάνι της Βηρυτού. Completed

Country
Cyprus
Related Organizations
Keywords

Remote Sensing, Τηλεσκόπηση, Sentinel-2, SNAP, Classification, Ταξινόμηση

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    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Related to Research communities
Rural Digital Europe
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