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Analiza časovnih vrst satelitskih posnetkov Sentinel-2 na vektorskih podatkih : magistrsko delo
Lipuš, Blaž (Author), Oštir, Krištof (Mentor) More about this mentor... This link opens in a new window, Kokalj, Žiga (Co-mentor)

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Abstract
V nalogi so na kratko opisani satelitski podatki s poudarkom na satelitih Sentinel-2 ter definicija časovne vrste. Opisane so tehnologije, ki omogočajo prenos satelitskih podatkov preko spleta. Primerjali smo izbrano metodo pridobivanja maske oblakov , uporabljeno na portalu Sentinel-hub, z drugimi v stroki razširjenimi metodami. Na kratko smo opisali metode glajenja časovnih vrst Savitzky-Golay oz. LOESS ter Whittaker-Eilers. Podali smo tri različne vegetacijske indekse in sicer NDVI, EVI in EVI2. V nalogi smo podali enostaven način pridobivanja podatkov s programskim jezikom Python. Opisali smo način shranjevanja podatkov časovnih vrst v SQLite in Spatialite datotečni podatkovni bazi. Primerjali smo metode glajenja časovnih vrst vegetacijskih indeksov glede na to, kako različne vrednosti parametrov pri funkcijah glajenja vplivajo na obliko časovnih vrst. Primerjali smo trende zglajene časovne vrste na treh različnih vegetacijskih indeksih. Nato smo podali način grajenja vektorjev iz časovnih vrst, ki se lahko uporabijo v različnih metodah strojnega učenja. Na koncu smo za demonstracijo podanega sistema izvedli klasifikacijo. Za potrebe naloge smo napisali knjižnico za programski jezik Python, ki je javno objavljena in omogoča enostavno pridobivanje in shranjevanje podatkov časovnih vrst satelitov Sentinel-2.

Language:Slovenian
Keywords:Sentinel-2, časovne vrste, Python, Spatialite, klasifikacija, glajenje, vegetacijski indeksi
Work type:Master's thesis/paper (mb22)
Tipology:2.09 - Master's Thesis
Organization:FGG - Faculty of Civil and Geodetic Engineering
Year:2019
Publisher:B. Lipuš]
UDC:528.7/.8:582(497.4)(043.3)
COBISS.SI-ID:8887649 Link is opened in a new window
Views:325
Downloads:164
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Secondary language

Language:English
Title:Sentinel-2 time series analysis on vector data : master thesis
Abstract:
In this thesis, we have described satellite data with emphasis on Sentinel-2 satellites. We showed definition of time series and methods for their collection over internet. We compared cloud mask algorithm, used and developed for sentinel-hub portal, with other commonly used cloud mask algorithms. We gave short description of Saviztky-Golay, LOESS and Whittaker-Eilers signal smoothing algorithms with NDVI, EVI and EVI2 vegetation indices. In the second part of the thesis, we provide a simplified way for getting and storing generalised raster statistical data in Python programming language and Spatialite database. We compared two series smoothing methods concerning input smoothing parameters. Similarly, we compared time series of three smoothed vegetation indices. In the end, we provided method for building comparable vectors and demonstrated our program on simple SVM classification model. For this thesis, we written program in Python, which is freely available online and simplifies work with Sentinel-2 time series.

Keywords:Sentinel-2, time series, Python, Spatialite, classification, smoothing, vegetation indicies

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