Commit 9238e674 authored by Manuel Grizonnet's avatar Manuel Grizonnet

DOC: typos in app-python

parent 4453a4b8
** OTB Applications on Python API :slides:
*** Data and Objectives
**** Objectives
- Know how to set the environnement to use OTB application in Python
- Know how to set the environment to use OTB application in Python
- Know how to set application parameter in Python
- Know how to use in memory connection of applications
- Know how to use OTB/NumPy bridge.
......@@ -92,12 +92,12 @@ ExtractROI.STARTX=140
**** Prerequisites
- Installed Monteverdi and Orfeo ToolBox software
- Installed Python(2.7.X or 3.5.X), with Numpy dependencies and the right
environmental variables setup (help: source the otbenv.profile in linux or launch otbenv.bat
- Installed Python(2.7.X or 3.5.X), with NumPy dependencies and the right
environmental variables setup (help: source the otbenv.profile in Linux or launch otbenv.bat
in Windows).
*Test*: launch "import otbApplication" on the python command line to check this point
- Downloaded dataset (~Data/app-python~)
- Understandig of Orfeo Toolbox applications (see relevant exercise)
- Understanding of Orfeo Toolbox applications (see relevant exercises)
**** Goals
......@@ -111,7 +111,7 @@ ExtractROI.STARTX=140
Data are located in ~Data/app-python~ folder, with the following sub-folders:
- ~images~ contains a set of Sentinel 2 images (Level 2A) in Laguna de la
Nina, Perou
Nina, Peru
- ~ref~ contains ancillary testing data (occurrence water masks) in
raster format
This folder also contains the following python scripts:
......@@ -122,7 +122,7 @@ ExtractROI.STARTX=140
The region of interest for this exercise is Laguna de la Nina, Peru
(-5.8101 lat, -80.7155 lon). In 2017 water surface extents
have drastically changed due to heavy rains during "El nino" periods.
The final objective is to analyse this change by means of satellite image
The final objective is to analyze this change by means of satellite image
processing.
In this exercise we will use three Sentinel-2 Level2A images
......@@ -156,7 +156,7 @@ ExtractROI.STARTX=140
- ATB: atmospheric and biophysical parameters with 2 bands :
- 1st band: water vapor content (WVC) coded over 8 bits
- 2st band: aerosol optical thickness (AOT) coded over 8 bits
- CLM: cloud mask computed by MACCS software, made of 1 band coded over 8 useful bits.
- CLM: cloud mask computed by MAJA software, made of 1 band coded over 8 useful bits.
- SAT: saturation mask coded over 8 bits
In this exercise, water maps will be calculated from ground reflectance
......@@ -241,7 +241,7 @@ ExtractROI.STARTX=140
[[file:Images/app-python-1.png]]
*Note:* Superimpose may be configured to used different interpolations
(linear, bicubic or nearest neighbor)
(linear, bi-cubic or nearest neighbor)
The necessary inputs and outputs of the Superimpose application
(https://www.orfeo-toolbox.org/CookBook/Applications/app_Superimpose.html)
......@@ -397,7 +397,7 @@ ExtractROI.STARTX=140
- Apply a threshold on the GSW resampled product with different probabilities:
10%, 20%, 30%, 50%, 75%, 95% to obtain different reference images
- Compare the water extent masks of exercise 4 with each of the reference
images issued from GSW. This comparison will help us to understant how often do
images issued from GSW. This comparison will help us to understand how often do
we observe a water extent map along time.
#+ATTR_LATEX: :float t :width 0.7\textwidth
[[file:Images/app-python-8.png]]
......@@ -442,7 +442,7 @@ ExtractROI.STARTX=140
seen by the naked eye and seen as we did not have any atmosphere.
The images show how this region goes over three phases:
- empty lagoon on December 2016
- max extension of the flooded lagoon in Avril 2017
- max extension of the flooded lagoon in April 2017
- flooding lagoon decreasing its size.
......@@ -452,7 +452,7 @@ ExtractROI.STARTX=140
surfaces, what band kind of file would you use between SRE and FRE?
Solution : FRE images corrects the effects of the atmosphere, and hence,
the physical propierties of the ground are better described on the FRE image.
the physical properties of the ground are better described on the FRE image.
2. Look at the disk size of B3 and B11 files of one the datasets in
~app-python/images/SENTINEL2A_*/~ Do all files have the same disk
......@@ -480,7 +480,7 @@ ExtractROI.STARTX=140
avoid false detections of water.
5. Open in Monteverdi the B8A and B4 and check the values in a water surface.
What is the reflectance behaviour of these bands on water surfaces?
What is the reflectance behavior of these bands on water surfaces?
Solution : On water regions, B4(RED) has higher reflectance values than B8A(NIR).
......@@ -531,7 +531,7 @@ ExtractROI.STARTX=140
6. At the generation of the NDVI mask(with two possible values: water(1) and land(0)
), there is a line like :
appX.SetParameterOutputImagePixelType("out", otbApplication.ImagePixelType_uint8)
What is the purpose of this line? What would happend without it?
What is the purpose of this line? What would happened without it?
*** Water detection chain with NoData management: exercise4.py
......
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