Principal component analysis (PCA) in SNAP using Sentinel-1 image

Principal component analysis (PCA) is the process of computing the principal components and using them to perform a change of basis on the data, sometimes using only the first few principal components and ignoring the rest. Principal Component Analysis, or PCA, is a dimensionality-reduction method that is often used to reduce the dimensionality of large data sets, by transforming a large set of variables into a smaller one that still contains most of the information in the large set and many other applications that can be utilized for.
in this video we show you how to pre-process your images and apply PCA to your images in the SNAP software.
As always you can ask your questions about the video in the comments.
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Principal component analysis (PCA) in SNAP using Sentinel-1 image
#PCA #Sentinel_1 #SNAP #Image #Principal_component_analysis #Sentinel_1_image #remotesensing #GIS #GISandRS #RSandGIS #preprocess #preprocess_Sentinel_1 RS & GIS #RSandGIS

Видео Principal component analysis (PCA) in SNAP using Sentinel-1 image канала RS & GIS