Satellite Image Processing Using SNAP
Learn how to use SNAP to process multispectral data, namely Sentinel-2 MSI, to map land cover near the Tisza-Tó area, Hungary, in 2016.
Learn how to use SNAP to process multispectral data, namely Sentinel-2 MSI, to map land cover near the Tisza-Tó area, Hungary, in 2016.
Download Sentinel-2 MSI data, open the product on SNAP, and visualize it on RGB color composite.
Use Sen2Cor algorithm to atmospherically correct Level-1C data and convert them to Level-2A. Learn the concept of RGB color composite and how it can be used for object identification. Resample and subset Sentinel-2 MSI image and then calculate various spectral indices (NDVI, NDWI, SAVI, etc..) and biophysical parameters (LAI and FVC). Apply k-means, Random Forest, and Maximum Likelihood classifiers on the region of interest, and then compare the accuracy of two supervised learning methods. Define Principal Component Analysis and apply it to reduce the dimensionality of processed multispectral data.
Copernicus Sentinel-2 Mission
How to download Sentinel-2 imagery
SEN2COR atmospheric correction
Resampling satellite image
Creating a subset
FREE PREVIEWVisualising an RGB-Color Composite
Calculation of spectral indices
Biophysical processors
K-means classifier
Maximum Likelihood classifier
Random Forest classifier
Accuracy assessment and comparison
Demonstration and application
Course notes
Sentinel-2 imagery used in course
Vector data
Discussion on the course
€15,00
Regular price
Ηow to download Sentinel-2 MSI data.
Ηow to preprocess a Sentinel-2 MSI L1C product.
Ηow to calculate spectral indices and biophysical parameters.
How to apply unsupervised and supervised classifiers for land cover mapping.
Review how to compare random forest and maximum likelihood classifiers in terms of accuracy.
How to apply a features-reduction technique.
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