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sources/01b-visualization.ipynb
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@ -6,13 +6,13 @@
"source": [
"# Visualization\n",
"\n",
"This notebook show how to access the content of the products downloaded in [](01a-download-process.ipynb) and plot a true-color rendering. \n",
"This notebook shows how to access the content of the products downloaded in [](01a-download-process.ipynb) and plot a true-color rendering. \n",
"While the products already contain a True-Color Image (TCI), this approach is useful for two reasons:\n",
"\n",
"1. It allows comparing the readings with a rendering provided by official sources, thereby allowing us to find errors\n",
"2. Generating a custom True-Color Image can be useful for further image manipulations, changing contrast or changing out single bands for others to highlight specific phenomena.\n",
"\n",
"We start by reading the shape of Berlin previously downloaded from OpenStreetMap:"
"First the shape of Berlin is created from data previously downloaded from OpenStreetMap:"
]
},
{
@ -61,7 +61,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"We know the product with the lowest cloud cover percentage from the previous notebook."
"The information which product contains the least amount of clouds is given in the previous notebook."
]
},
{
@ -92,7 +92,7 @@
"source": [
"The product path contains a lot of information:\n",
"\n",
"- `S2B` shows that the downloaded products was captured by the Sentinel-2 satellite B. At the moment there are two satellites in the mission, A and B.\n",
"- `S2B` shows that the downloaded product was captured by the Sentinel-2 satellite B. At the moment there are two satellites in the mission, A and B.\n",
"- `MSI` stands for Multi Spectral Instrument.\n",
"- `L2A` is the processing level. Level 2A is the highest processing level and lower processing levels may need further processing to be useful.\n",
"- The first timestamp, `20200602T100559`, is the date at which the data was captured.\n",
@ -175,7 +175,7 @@
"source": [
"Using the compressed zip-file, while slightly inconvenient, makes sense because it allows saving disk space and allows us to avoid the extra step of decompressing every single downloaded product.\n",
"\n",
"There is a pre-rendered True-Color Image (\"TCI\") that we can use to get a quick plot of the contents:"
"There is a pre-rendered True-Color Image (\"TCI\") that can be plotted for an impression of the product's contents:"
]
},
{
@ -203,7 +203,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Using the `rasterio` library we can open this image and render its contents:"
"The `rasterio` library is used to open this image and render its contents:"
]
},
{
@ -238,7 +238,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"You will see this pattern repeatedly:\n",
"This pattern shows repeatedly across many notebooks:\n",
"\n",
"``` python\n",
"with r.open(...) as src:\n",
@ -252,7 +252,7 @@
"While for many use cases using the TCI can be enough, knowing how to compose True-Color Images provides additional merit as explained above.\n",
"\n",
"The blue, green, and red parts of the spectrum are represented in the raster files for the bands 2, 3 and 4 respectively\n",
"`sentinel_helpers.py` contains a helper that wraps `scihub_band_paths` to retrieve those bands in a resolution of our choice:"
"`sentinel_helpers.py` contains a function wrapping `scihub_band_paths` to retrieve those bands in a resolution of choice:"
]
},
{
@ -313,7 +313,7 @@
"source": [
"### Full Range Plot\n",
"\n",
"We continue with a plot of the combination of these bands:"
"Next, a plot of the combination of these bands is plotted:"
]
},
{
@ -510,7 +510,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"We can compare the histograms of `included_tci` and the `normalized_rgb` array: "
"A comparison of the histograms of `included_tci` and the `normalized_rgb` array offers more details:"
]
},
{
@ -555,7 +555,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see each the red, green and blue band peaking higher in the prerendered TCI around a value of 50 - the curves match closely. Most of the pixels are using the designated nodata-value 0, which is the black stripe in the top left corner of the image.\n",
"Each the red, green and blue band have higher peaks in the prerendered TCI at value of around 50 - the curves match closely. Most of the pixels are using the designated nodata-value 0, which is the black stripe in the top left corner of the image.\n",
"\n",
"Because the purpose of this visualization is not creating a one-to-one replica of the included TCI but rather demonstrate how to interpret and manipulate the raster file contents, the approximation is sufficient.\n",
"\n",
@ -563,7 +563,7 @@
"\n",
"It is rare to plot the entire product because the data in this product can be partially missing depending on the orbit position (see [](01c-coverage-analysis.ipynb) for more information).\n",
"\n",
"We can create a rectangular cutout of the created image using code provided in the `rasterio` library for its `rio` command line tool. The code uses a data structure called `Window`, which is a rectangle with an x- and y-offset that is provided by `rasterio` to partially read or write raster data.\n",
"The created image can be cropped using code provided in the `rasterio` library for its `rio` command line tool. This requires constructing a `Window`, which is a rectangle with an x- and y-offset that is provided by `rasterio` to partially read or write raster data.\n",
"\n",
"The position of the `Window` is calculated by transforming the area of interest `berlin` into the Coordinate Reference System that is used by `src` and then calculating the intersection:"
]