Commit 8b2dae74 authored by Marta Enesco's avatar Marta Enesco
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Update README.md

parent 6c32a734
......@@ -13,7 +13,7 @@ The first dimension of **dates**, **spectra**, and **classes** are aligned such
The figure below shows the layout of the file and some sample data:
![hdf5 file](fig/screenshot_hdfview.png)
![hdf5 file](fig/h5file.png)
A technical note on how the data was produced is currently under preparation.
......@@ -44,13 +44,12 @@ By means of different spectral tools, granule pixels are selected and classified
Spectral tools include *false-color composites*, *image enhancements* and *graphical visualization of spectra*. Our aim is to create highly heterogeneous classes with a balanced number of pixels.
The figure below exposes the benefit of false-color composites for snow distinction. For this RGB display of the Atlas mountains in Morocco, bands 12/7/3 are selected. Snow pixels will appear in blue, whereas cloud pixels in pink orange.
![marokko](fig/screenshot_marokko.png)
The figure above exposes the use of false-color composites for snow distinction. To display the Atlas mountains in Marokko, bands 12/7/3 are selected. Snow pixels will appear in blue, whereas cloud pixels in pink orange.
And next figure illustrates the pixel classification. The Fiji coastline is displayed in two different false-composites: (a) bands 4/3/2 and (b) bands 8a/3/2. Colored polygons represent four different classes. Cyan, yellow, dark blue and green colors stand for water, shadow, cloud and clear-sky pixels.
![fiji](fig/screenshot_fiji.jpg)
And previous figure illustrates some pixel classification. The image of Fiji coastline is displayed in two different false-composites: (a) bands 4/3/2 and (b) bands 8a/3/2. Colored polygons represent four different classes: cyan, yellow, dark blue and green correspond to water, shadow, cloud and clear-sky pixels.
## Dataset
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