Commit ea6cd320 authored by Marta Enesco's avatar Marta Enesco
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Update README.md

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...@@ -44,23 +44,13 @@ By means of different spectral tools, granule pixels are selected and classified ...@@ -44,23 +44,13 @@ 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. 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 following figure exposes the use of false-color composites for snow distinction.
![marokko](fig/screenshot_marokko.png) ![marokko](fig/screenshot_marokko.png)
For this RGB display of the Atlas mountains in Marokko, bands 12/7/3 are selected. Snow pixels appear in blue, whereas cloud pixels in pink orange. 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 some classes generation.
![fiji](fig/screenshot_fiji.jpg) ![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.
This 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.
| ![marokko](fig/screenshot_marokko.png) | ![fiji](fig/screenshot_fiji.jpg)) |
|:---: | :---: |
| Marokko | Fiji |
## Dataset ## Dataset
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