Commit 9eb21ec8 authored by Antoine Regimbeau's avatar Antoine Regimbeau

MRG: Merge branch bug_dragndrop into release-6.2

parents 6e732a3d 02cb5268
......@@ -110,7 +110,7 @@ set(main_project_name ${_OTBModuleMacros_DEFAULT_LABEL})
#-----------------------------------------------------------------------------
# OTB version number.
set(OTB_VERSION_MAJOR "6")
set(OTB_VERSION_MINOR "1")
set(OTB_VERSION_MINOR "2")
set(OTB_VERSION_PATCH "0")
set(OTB_VERSION_STRING "${OTB_VERSION_MAJOR}.${OTB_VERSION_MINOR}.${OTB_VERSION_PATCH}")
......
......@@ -87,7 +87,7 @@ You can add it by using these command-lines:
sudo aptitude install add-apt-repository
sudo apt-add-repository ppa:ubuntugis/ubuntugis-unstable
After you can run:
You will then need to run:
::
......@@ -114,8 +114,8 @@ OpenSuse 12.X and higher
For OpenSuse 12.X and higher, OTB Applications packages are available
through *zypper*.
First, you need to add the appropriate repositories with these
command-lines (please replace :math:`11.4` by your OpenSuse version):
First, you need to add the appropriate repositories with the following
commands (please replace :math:`11.4` with your version of OpenSuse):
::
......@@ -126,7 +126,7 @@ command-lines (please replace :math:`11.4` by your OpenSuse version):
sudo zypper ar
http://download.opensuse.org/repositories/home:/tzotsos/openSUSE_11.4/ tzotsos
Now run:
You should then run:
::
......
We provide a binary package for GNU/Linux x86_64. This package includes
all of OTB applications along with commandline and graphical launchers.
Download it from `OTB's download page
all of the OTB applications along with command line and graphical launchers.
It can be downloaded from `OTB's download page
<https://www.orfeo-toolbox.org/download>`__.
This package is a self-extractible archive. You may uncompress it with a
double-click on the file, or with the command line:
This package is a self-extractable archive. You may uncompress it with a
double-click on the file, or from the command line as follows:
.. parsed-literal::
chmod +x OTB-|release|-Linux64.run
./OTB-|release|-Linux64.run
The self-extractible archive only needs common tools found on most Linux
The self-extractable archive only needs common tools found on most Linux
distributions ("sed", "grep", "find", "cat", "printf", "ln", ...). However, be
aware that it requires tools such as "which" and "file" (they are not always
present, for instance when building a container).
Please note that the resulting installation is not meant to be moved,
you should uncompress the archive in its final location. Once the
archive is extracted, the directory structure is made of:
archive is extracted, the directory structure consists of:
- ``monteverdi.sh``: A launcher script for Monteverdi
......@@ -38,9 +38,9 @@ archive is extracted, the directory structure is made of:
In order to run the command line launchers, this package doesn’t require
any special library that is not present in most modern Linux
distributions. There is a small caveat for "expat" though. The binaries depend
distributions. There is a small caveat for "expat" though as these binaries depend
on "libexpat.so", which can be supplied by most package managers (apt, yum, ...).
If not already present, look for one of the following packages:
If not already present, it is necessary to install one of the following packages:
::
......@@ -65,14 +65,14 @@ with ``source otbenv.profile``.
Python bindings
~~~~~~~~~~~~~~~
Starting from OTB 5.8.0, OTB python bindings are distributed with binary package.
currently only Python 2.x is supported. If no compatible python is found, installation
notify you about it. If everything works fine, you will be given information about
using python bindings.
Starting from OTB 5.8.0, OTB Python bindings are distributed with binary package.
Currently only Python 2.x is supported and if no compatible Python version is found a
notification is generated during the installation process. If the installation completes
without issue, information relating to your Python bindings will be provided.
You must have python numpy bindings installed in your system. you can install it locally
without admin rights with "pip install --user numpy". This is to give users to choose
their own existing python installation rather than distributing one in OTB package
You must have Python numpy bindings installed in your system. They can be installed locally
without admin rights as follows: "pip install --user numpy". This is to give users the option
to select their own existing Python installation rather than the one dibstributed by the OTB package.
Notes:
......
......@@ -35,7 +35,7 @@ using python bindings.
You must have python numpy bindings installed in your system. you can install it locally
without admin rights with "pip install --user numpy". This is to give users to choose
their own existing python installation rather than distributing one in OTB package
their own existing python installation rather than distributing one in OTB package.
Notes:
......
......@@ -25,14 +25,14 @@ with ``otbenv.bat``.
Python bindings
~~~~~~~~~~~~~~~
Starting from OTB 5.8.0, OTB python bindings are distributed with binary package.
currently only Python 2.x is supported. If no compatible python is found, installation
notify you about it. If everything works fine, you will be given information about
using python bindings.
You must have python numpy bindings installed in your system. you can install it locally
without admin rights with "pip install --user numpy". This is to give users to choose
their own existing python installation rather than distributing one in OTB package
Starting from OTB 5.8.0, OTB Python bindings are distributed with binary package.
Currently only Python 2.x is supported and if no compatible Python version is found a
notification is generated during the installation process. If the installation completes
without issue, information relating to your Python bindings will be provided.
You must have Python numpy bindings installed in your system. They can be installed locally
without admin rights as follows: "pip install --user numpy". This is to give users the option
to select their own existing Python installation rather than the one dibstributed by the OTB package.
Notes
~~~~~
......
......@@ -72,11 +72,11 @@ Image displaying
This part of the main window is intended to display the images loaded by
the user. There are many nice keyboard shortcuts or mouse tricks that
let the user have a better experience in navigating throughout the
loaded images. These shortcuts and tricks are given within the Help item
of the main menu, by clicking Keymap; here is a short list of the most
useful ones:
loaded images. These shortcuts and tricks are provided within the Help item
of the main menu under Keymap. Here is a short list of the most
commonly used ones:
The classical ones:
The standard ones:
- CTRL+O = Open file(s)
......@@ -108,8 +108,8 @@ Right side dock
The dock on the right side is divided into four tabs:
- Quicklook: gives the user a degraded view of the whole extent,
letting him/her easily select the area to be displayed
- Quicklook: provides an overview of the full extent of the image,
and allows one to easily select the area to be displayed.
- Histogram: gives the user information about the value distribution
of the selected channels. By clicking the mouse’s left button, user
......@@ -208,21 +208,20 @@ BandMath
BandMath application is intended to apply mathematical operations on
pixels (launch it with shortcut CTRL+A). In this example, we are going
to use this application to change the dynamics of an image, and check
the result by looking at histogram tab, in the right side dock. The
the result by looking at the histogram tab on the right-hand side of the GUI. The
formula used is the following: :math:`\text{im1b1} \times 1000`. In the
figures below ( [fig:BM]), one can notice that the mode of the
distribution is located at position :math:`356.0935`, whereas in the
transformed image, the mode is located at position :math:`354737.1454`,
that’s to say 1000 times farther away approximately (the cursors aren’t
that’s to say approximately 1000 times further away (the cursors aren’t
placed exactly at the same position in the screenshots).
.. figure:: Art/MonteverdiImages/BM.png
Segmentation
~~~~~~~~~~~~
Now, let’s use the segmentation application (launch it with shortcut
CTRL+A). We let the user take a look at the application’s documentation;
From within Monteverdi, the Segmentation application can be launched using the
shortcut CTRL+A. We let the user take a look at the application’s documentation;
let’s simply say that as we wish we could display the segmentation with
, we must tell the application to output the segmentation in raster
format. Thus, the value of the mode option must be set to raster. The
......
......@@ -3,15 +3,16 @@ A brief tour of OTB Applications
OTB ships with more than 90 ready to use applications for remote sensing tasks.
They usually expose existing processing functions from the underlying C++
library, or compose them into high level pipelines. OTB applications allow to:
library, or integrate them into high level pipelines. OTB applications allow the user
to:
- Combine together two or more functions from the Orfeo Toolbox,
- Combine two or more functions from the Orfeo ToolBox,
- Provide a nice high level interface to handle: parameters, input
data, output data and communication with the user.
- Provide a high level interface to handle: input and output data,
definition of parameters and communication with the user.
OTB applications can be launched in different ways, and accessed from different
entry points. The framework can be extended, but Orfeo Toolbox ships with the following:
entry points. While the framework can be extended, the Orfeo ToolBox ships with the following:
- A command-line launcher, to call applications from the terminal,
......@@ -50,16 +51,15 @@ results in the following help to be displayed:
Usage: ./otbApplicationLauncherCommandLine module_name [MODULEPATH] [arguments]
The ``module_name`` parameter corresponds to the application name. The
``[MODULEPATH]`` argument is optional and allows to pass to the launcher
a path where the shared library (or plugin) corresponding to
``module_name`` is.
``[MODULEPATH]`` argument is optional and allows the path to the shared library
(or plugin) correpsonding to the ``module_name`` to be passed to the launcher.
It is also possible to set this path with the environment variable
``OTB_APPLICATION_PATH``, making the ``[MODULEPATH]`` optional. This
variable is checked by default when no ``[MODULEPATH]`` argument is
given. When using multiple paths in ``OTB_APPLICATION_PATH``, one must
make sure to use the standard path separator of the target system, which
is ``:`` on Unix, and ``;`` on Windows.
is ``:`` on Unix and ``;`` on Windows.
An error in the application name (i.e. in parameter ``module_name``)
will make the ``otbApplicationLauncherCommandLine`` lists the name of
......@@ -73,12 +73,12 @@ standard application installation path to the ``OTB_APPLICATION_PATH``
environment variable.
These scripts are named ``otbcli_<ApplicationName>`` and do not need any
path settings. For example you can start the Orthorectification
path settings. For example, you can start the Orthorectification
application with the script called ``otbcli_Orthorectification``.
Launching an application with no or incomplete parameters will make the
launcher display a summary of the parameters, indicating the mandatory
parameters missing to allow for application execution. Here is an
Launching an application without parameters, or with incomplete parameters, will cause the
launcher to display a summary of the parameters. This summary will display the minimum set
of parameters that are required to execute the application. Here is an
example with the OrthoRectification application:
::
......@@ -129,7 +129,7 @@ chapter [chap:apprefdoc], page  or follow the ``DOCUMENTATION``
hyperlink provided in ``otbApplicationLauncherCommandLine`` output.
Parameters are passed to the application using the parameter key (which
might include one or several ``.`` character), prefixed by a ``-``.
Command-line examples are provided in chapter [chap:apprefdoc], page .
Command-line examples are provided in chapter [chap:apprefdoc], page.
Graphical launcher
------------------
......@@ -138,8 +138,7 @@ The graphical interface for the applications provides a useful
interactive user interface to set the parameters, choose files, and
monitor the execution progress.
This launcher needs the same two arguments as the command line launcher
:
This launcher needs the same two arguments as the command line launcher:
::
......@@ -207,10 +206,10 @@ configured automatically so you don’t need to tweak
In the ``otbApplication`` module, two main classes can be manipulated :
- ``Registry``, which provides access to the list of available
applications, and can create applications
applications, and can create applications.
- ``Application``, the base class for all applications. This allows to
interact with an application instance created by the ``Registry``
interact with an application instance created by the ``Registry``.
Here is one example of how to use Python to run the ``Smoothing``
application, changing the algorithm at each iteration.
......@@ -372,7 +371,7 @@ The processing toolbox
OTB applications are available from QGIS. Use them from the processing
toolbox, which is accessible with Processing :math:`\rightarrow`
Toolbox. Switch to “advanced interface” in the bottom of the application
ToolBox. Switch to “advanced interface” in the bottom of the application
widget and OTB applications will be there.
.. figure:: Art/QtImages/qgis-otb.png
......@@ -492,7 +491,7 @@ Here is an example of MPI call on a cluster::
------------ END JOB INFO 1043196.tu-adm01 ---------
One can see that the registration and pan-sharpening of the
panchromatic and multi-spectral bands of a Pleiades image has bee split
panchromatic and multi-spectral bands of a Pleiades image has been split
among 560 cpus and took only 56 seconds.
Note that this MPI parallel invocation of applications is only
......
Recipes
=======
This chapter presents guideline to perform various remote sensing and
This chapter presents guidelines to perform various remote sensing and
image processing tasks with either , or both. Its goal is not to be
exhaustive, but rather to help the non-developer user to get familiar
with these two packages, so that he can use and explore them for his
future needs.
exhaustive, but rather to familiarise users with the OTB package functionality
and demonstrate how the can be applied.
.. toctree::
:maxdepth: 6
......
......@@ -139,8 +139,8 @@ relevant channels in brackets are:
Soil:BI - Brightness index (Red, Green)
Soil:BI2 - Brightness index 2 (NIR, Red, Green)
The application can be used like this, which leads to an output image
with 3 bands, respectively with the Vegetation:NDVI, Vegetation:RVI and
The application can be used as follows, which would produce an output image
containing 3 bands, respectively with the Vegetation:NDVI, Vegetation:RVI and
Vegetation:IPVI radiometric indices in this exact order:
::
......@@ -153,8 +153,8 @@ Vegetation:IPVI radiometric indices in this exact order:
-list Vegetation:NDVI Vegetation:RVI
Vegetation:IPVI
or like this, which leads to a single band output image with the
Water:NDWI2 radiometric indice:
or as follows, which would produce a single band output image with the
Water:NDWI2 radiometric index:
::
......
......@@ -124,7 +124,6 @@ segmentation of very large image with theoretical guarantees of getting
identical results to those without tiling.
It has been developed by David Youssefi and Julien Michel during David
internship at CNES.
For more a complete description of the LSMS method, please refer to the
......@@ -262,6 +261,35 @@ set the tile size using the *tilesizex* and *tilesizey* parameters.
However unlike the *LSMSSegmentation* application, it does not require
to write any temporary file to disk.
All-in-one
~~~~~~~~~~
The *LargeScaleMeanShift* application is a composite application that chains
all the previous steps:
- Mean-Shift Smoothing
- Segmentation
- Small region merging
- Vectorization
Most of the settings from the previous applications are also exposed in this
composite application. The range and spatial radius used for the segmentation
step are half the values used for Mean-Shift smooting, which are obtained from
LargeScaleMeanShift parameters. There are two output modes: vector (default)
and raster. When the raster output is chosen, last step (vectorization) is
skipped.
::
otbcli_LargeScaleMeanShift -in input_image.tif
-spatialr 5
-ranger 30
-minsize 10
-mode.vector.out segmentation_merged.shp
There is a cleanup option that can be disabled in order to check intermediate
outputs of this composite application.
Dempster Shafer based Classifier Fusion
---------------------------------------
......
......@@ -2,14 +2,14 @@ From raw image to calibrated product
====================================
This section presents various pre-processing tasks that are presented in
a classical order to obtain a calibrated, pan-sharpened image.
a standard order to obtain a calibrated, pan-sharpened image.
Optical radiometric calibration
-------------------------------
In remote sensing imagery, pixel values are called DN (for Digital
Numbers) and can not be physically interpreted and compared: they are
influenced by various factors such as the amount of light flowing trough
In remote sensing imagery, pixel values are referred to as Digital
Numbers (DN) and they cannot be physically interpreted or compared. They are
influenced by various factors such as the amount of light flowing through
the sensor, the gain of the detectors and the analogic to numeric
converter.
......@@ -87,14 +87,14 @@ Pan-sharpening
Because of physical constrains on the sensor design, it is difficult to
achieve high spatial and spectral resolution at the same time: a better
spatial resolution means a smaller detector, which in turns means lesser
spatial resolution means a smaller detector, which in turn means lesser
optical flow on the detector surface. On the contrary, spectral bands
are obtained through filters applied on the detector surface, that
lowers the optical flow, so that it is necessary to increase the
detector size to achieve an acceptable signal to noise ratio.
For these reasons, many high resolution satellite payload are composed
of two sets of detectors, which in turns delivers two different kind of
of two sets of detectors, which in turn delivers two different kind of
images:
- The multi-spectral (XS) image, composed of 3 to 8 spectral bands
......@@ -194,7 +194,7 @@ Figure 5: Pan-sharpened image using Orfeo ToolBox.
Please also note that since registration and zooming of the
multi-spectral image with the panchromatic image relies on sensor
modelling, this tool will work only for images whose sensor models is
available in **Orfeo Toolbox** (see :ref:`section3` for a detailed
available in **Orfeo ToolBox** (see :ref:`section3` for a detailed
list). It will also work with ortho-ready products in cartographic
projection.
......@@ -207,37 +207,37 @@ A Digital Elevation Model (DEM) is a georeferenced image (or collection
of images) where each pixel corresponds to a local elevation. DEM are
useful for tasks involving sensor to ground and ground to sensor
coordinate transforms, like during ortho-rectification (see :ref:`section3`). These transforms need to find the intersection
between the line of sight of the sensor and the earth geoid. If a simple
spheroid is used as the earth model, potentially high localisation
between the line of sight of the sensor and the Earth geoid. If a simple
spheroid is used as the Earth model, potentially high localisation
errors can be made in areas where elevation is high or perturbed. Of
course, DEM accuracy and resolution have a great impact on the precision
of these transforms.
of these transformations.
Two main available DEM, free of charges, and with worldwide cover, are
both delivered as 1 degree by 1 degree tiles:
- `The Shuttle Radar topographic Mission
(SRTM) <http://www2.jpl.nasa.gov/srtm/>`_ is a 90 meters resolution
DEM, obtained by radar interferometry during a campaign of the
(SRTM) <http://www2.jpl.nasa.gov/srtm/>`_ is a DEM with a resolution of 90 metres,
obtained by radar interferometry during a campaign of the
Endeavour space shuttle from NASA in 2000.
- The `Advanced Spaceborne Thermal Emission and Reflection Radiometer
(ASTER) <http://www.ersdac.or.jp/GDEM/E/2.html>`_ is a 30 meters
resolution DEM obtained by stereoscopic processing of the archive of
(ASTER) <http://www.ersdac.or.jp/GDEM/E/2.html>`_ is a DEM with a resolution of
30 metres obtained by stereoscopic processing of the archive of
the ASTER instrument.
The **Orfeo Toolbox** relies on `OSSIM <http://www.ossim.org/>`_
The **Orfeo ToolBox** relies on `OSSIM <http://www.ossim.org/>`_
capabilities for sensor modelling and DEM handling. Tiles of a given DEM
are supposed to be located within a single directory. General elevation
support is also supported from GeoTIFF files.
Whenever an application or **Monteverdi** module requires a DEM, the
option **elev.dem** allows set the DEM directory. This directory must
contains the DEM tiles, either in DTED or SRTM format, either as GeoTIFF
files. Subdirectories are not supported.
contain the DEM tiles, either in DTED or SRTM format or as a GeoTIFF.
Subdirectories are not supported.
Depending on the reference of the elevation, you also need to use a
geoid to manage elevation accurately. For this, you need to specify a
geoid to accurately manage the elevation. For this, you need to specify a
path to a file which contains the geoid. `Geoid <http://en.wikipedia.org/wiki/Geoid>`_
corresponds to the equipotential surface that would coincide with the mean ocean surface of
the Earth.
......@@ -290,7 +290,7 @@ Ortho-rectification can be performed either with **OTB Applications** or
**Monteverdi** . Sensor parameters and image meta-data are seamlessly
read from the image files without needing any user interaction, provided
that all auxiliary files are available. The sensor for which **Orfeo
Toolbox** supports ortho-rectification of raw products are the
ToolBox** supports ortho-rectification of raw products are the
following:
- Pleiades
......@@ -306,7 +306,7 @@ following:
- WorldView
In addition, GeoTiff and other file format with geographical information
are seamlessly read by **Orfeo Toolbox** , and the ortho-rectification
are seamlessly read by **Orfeo ToolBox** , and the ortho-rectification
tools can be used to re-sample these images in another map projection.
Beware of ortho-ready products
......@@ -331,7 +331,7 @@ it. Obviously, this map projection is not as accurate as the sensor
parameters of the raw geometry. In addition, the impact of the elevation
model cant be observed if the map projection is used. In order to
perform an ortho-rectification on this type of product, the map
projection has to be hidden from **Orfeo Toolbox** .
projection has to be hidden from **Orfeo ToolBox** .
You can see if a product is an ortho-ready product by using ``gdalinfo`` or
OTB ReadImageInfo application.
......@@ -343,7 +343,7 @@ Check if your product verifies following two conditions:
- The product has a map projection: you should see a projection name
with physical origin and spacing.
In that case, you can hide the map projection from the **Orfeo Toolbox**
In that case, you can hide the map projection from the **Orfeo ToolBox**
by using *extended* filenames. Instead of using the plain input image
path, you append a specific key at the end:
......
......@@ -33,7 +33,7 @@ model algorithm to train. You have the possibility to do the unsupervised
classification,for it, you must to choose the Shark kmeans classifier.
Please refer to the ``TrainVectorClassifier`` application reference documentation.
In case of multiple samples files, you can add them to the ``-io.vd``
In case of multiple sample files, you can add them to the ``-io.vd``
parameter.
The feature to be used for training must be explicitly listed using
......@@ -49,14 +49,14 @@ can be set using the ``-cfield`` option.
By default, the application will estimate the trained classifier
performances on the same set of samples that has been used for
training. The ``-io.vd`` parameter allows to specify a different
samples file for this purpose, for a more fair estimation of the
performances. Note that this performances estimation scheme can also
be estimated afterward (see `Validating the classification model`_
training. The ``-io.vd`` parameter allows for the specification of different
sample files for this purpose, for a more fair estimation of the
performances. Note that this scheme to estimate the performance can also
be carried out afterwards (see `Validating the classification model`_
section).
Features classification
Feature classification
~~~~~~~~~~~~~~~~~~~~~~~
Once the classifier has been trained, one can apply the model to
......@@ -71,8 +71,8 @@ classify a set of features on a new vector data file using the
-cfield predicted
-out classifiedData.shp
This application output a vector data file storing sample values
and classification label. The output is optional, in this case the
This application outputs a vector data file storing sample values
and classification labels. The output is optional, in this case the
input vector data classification label field is updated.
Validating classification
......@@ -83,7 +83,7 @@ or *TrainImagesClassifier* applications is directly estimated by the
application itself, which displays the precision, recall and F-score
of each class, and can generate the global confusion matrix for
supervised algorithms. For unsupervised algorithms a contingency table
is generated. Those results are output as an \*.CSV file.
is generated. These results are output as an \*.CSV file.
Pixel based classification
--------------------------
......@@ -173,13 +173,13 @@ The output XML file will look like this::
Samples selection
Sample selection
~~~~~~~~~~~~~~~~~
Now, we know exactly how many samples are available in the image for
each class and each geometry in the training set. From this
each class and each geometry in the training set. From these
statistics, we can now compute the sampling rates to apply for each
classes, and perform the sample selection. This will be done by the
class, and perform the sample selection. This will be done by the
``SampleSelection`` application.
There are several strategies to compute those sampling rates:
......@@ -192,27 +192,27 @@ There are several strategies to compute those sampling rates:
* **Percent strategy:** Each class will be sampled with a user-defined
percentage (same value for all classes) of samples available in this
class.
* **Total strategy:** A global number of samples to generate is
divided proportionally among each class (classes proportions are
* **Total strategy:** A global number of samples to select is
divided proportionally among each class (class proportions are
enforced).
* **Take all strategy:** Take all the available samples
* **Take all strategy:** Take all the available samples.
* **By class strategy:** Set a target number of samples for each
class. The number of samples for each class is read from a CSV file.
To actually select the sample positions, there are two available
sampler:
sampling techniques:
* **Random:** Randomly select samples while respecting the sampling
rate
* **Periodic:** Sample periodically using the sampling rate
rate.
* **Periodic:** Sample periodically using the sampling rate.
The application will make sure that samples spans the whole training
set extent by adjusting the sampling rate. Depending on the strategy
to determine the sampling rate, some geometries of the training set
might not be sampled.
may not be sampled.
The application will accept as input the input image and training
geometries, as well class statistics XML file computed during previous
geometries, as well class statistics XML file computed during the previous
step. It will output a vector file containing point geometries which
indicate the location of the samples.
......@@ -227,7 +227,7 @@ indicate the location of the samples.
-out samples.sqlite
The csv file written by the optional ``-outrates`` parameter sums-up what
has been done during samples selection::
has been done during sample selection::
#className requiredSamples totalSamples rate
11 941 56774 0.0165745
......@@ -253,8 +253,8 @@ has been done during samples selection::
Samples extraction
~~~~~~~~~~~~~~~~~~
Now that we selected the location of the samples, we will attach
measurement to them. This is the purpose of the ``SampleExtraction``
Now that the locations of the samples are selected, we will attach
measurements to them. This is the purpose of the ``SampleExtraction``
application. It will walk through the list of samples and extract the
underlying pixel values. If no ``-out`` parameter is given, the
``SampleExtraction`` application can work in update mode, thus allowing
......@@ -286,8 +286,7 @@ Working with several images
If the training set spans several images, the ``MultiImageSamplingRate``
application allows to compute the appropriate sampling rates per image
and per class, in order to get samples that spans the whole images
coverage.
and per class, in order to get samples that span the entire extents of the images.
It is first required to run the ``PolygonClassStatistics`` application
on each image of the set separately. The ``MultiImageSamplingRate``
......
......@@ -173,7 +173,7 @@ Orthorecrtify image using the affine geometry
Now we will show how we can use this new sensor model. In our case we’ll
use this sensor model to orthorectify the image over the Pléiades
reference. **Orfeo Toolbox** offers since version 3.16 the possibility
reference. **Orfeo ToolBox** offers since version 3.16 the possibility
to use
hrefhttp://wiki.orfeo-toolbox.org/index.php/ExtendedFileNameextend image
path to use different metadata file as input. That’s what we are going
......
......@@ -20,7 +20,7 @@ If SARimg.tif is a TerraSAR-X or a COSMO-SkyMed image:
otbcli_SarRadiometricCalibration -in SARimg.tif
-out SARimg-calibrated.tif
If SARimg.tif is a RadarSat2 or a Sentinel1 image, it s possible to
If SARimg.tif is a RadarSat2 or a Sentinel1 image, it is possible to
specify the look-up table (automatically found in the metadata provided
with such image):
......
......@@ -194,7 +194,7 @@ can be directly linked to the local elevation
An almost complete spectrum of `stereo correspondence algorithms
<http://vision.middlebury.edu/stereo/eval3/>`_ has been
published and it is still augmented at a significant rate!
The **Orfeo Toolbox** implements different local strategies for block