DOC: modifications to the cookbook

Documentation/Cookbook/rst/Monteverdi.rst

@@ -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 by 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

Documentation/Cookbook/rst/OTB-Applications.rst

@@ -492,7 +492,7 @@ 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

Documentation/Cookbook/rst/Recipes.rst

 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

Documentation/Cookbook/rst/recipes/optpreproc.rst

@@ -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.