Merge branch 'app_docsprint' of https://git.orfeo-toolbox.org/git/otb into app_docsprint

Modules/Applications/AppChangeDetection/app/otbMultivariateAlterationDetector.cxx

@@ -46,58 +46,59 @@ private:
   void DoInit() ITK_OVERRIDE
   {
     SetName("MultivariateAlterationDetector");
-    SetDescription("Multivariate Alteration Detector");
+    SetDescription("Change detection by Multivariate Alteration Detector (MAD) algorithm");
 
     // Documentation
-    SetDocName("Multivariate alteration detector");
-    SetDocLongDescription("This application detects change between two given images.");
-    SetDocLimitations("None");
+    SetDocName("Multivariate Alteration Detector");
+    SetDocLongDescription("This application performs change detection between two multispectral"
+                          " images using the Multivariate Alteration Detector (MAD) [1]"
+                          " algorithm.\n\n"
+                          "The MAD algorithm produces a set of N change maps (where N is the"
+                          " maximum number of bands in first and second input images), with the"
+                          " following properties:\n"
+                          " - Change maps are differences of a pair of linear combinations of "
+                          " bands from image 1 and bands from image 2 chosen to maximize the "
+                          " correlation, \n"
+                          " - Each change map is orthogonal to the others.\n"
+                          " \n"
+                          "This is a statistical method which can handle different modalities"
+                          " and even different bands and number of bands between images. \n"
+                          " \n"
+                          "The application will output all change maps into a single multiband"
+                          " image. If numbers of bands in image 1 and 2 are equal, then change"
+                          " maps are sorted by increasing correlation. If number of bands is"
+                          " different, the change maps are sorted by decreasing correlation. \n"
+                          " \n"
+                          "The application will also print the following information:\n"
+                          "- Mean1 and Mean2 which are the mean values of bands for both input"
+                          " images,\n"
+                          "- V1 and V2 which are the two linear transform that are applied to"
+                          " input image 1 and input image 2 to build the change map,\n"
+                          "- Rho, the vector of correlation associated to each change map.\n"
+                          " \n"
+                          "The OTB filter used in this application has been implemented from the"
+                          " Matlab code kindly made available by the authors here [2]. Both cases"
+                          " (same and different number of bands) have been validated"
+                          " by comparing the output image to the output produced by the Matlab "
+                          " code, and the reference images for testing have been generated from "
+                          " the Matlab code using Octave." );
+
+                          
+    SetDocLimitations("Input images 1 and 2 should share exactly the same origin, spacing, size, and projection if any.");
     SetDocAuthors("OTB-Team");
-    SetDocSeeAlso(" This filter implements the Multivariate Alteration Detector, based "
-                  " on the following work: \n"
-                  " A. A. Nielsen and K. Conradsen, Multivariate alteration detection"
-                  " (mad) in multispectral, bi-temporal image data: a new approach to"
-                  " change detection studies, Remote Sens. Environ., vol. 64,"
-                  " pp. 1-19, (1998) \n"
-                  " \n"
-                  " Multivariate Alteration Detector takes two images as inputs and "
-                  " produce a set of N change maps as a VectorImage (where N is the "
-                  " maximum of number of bands in first and second image) with the "
-                  " following properties: \n"
-                  " - Change maps are differences of a pair of linear combinations of "
-                  " bands from image 1 and bands from image 2 chosen to maximize the "
-                  " correlation. \n"
-                  " - Each change map is orthogonal to the others. \n"
-                  " \n"
-                  " This is a statistical method which can handle different modalities "
-                  " and even different bands and number of bands between images. \n"
-                  " \n"
-                  " If numbers of bands in image 1 and 2 are equal, then change maps "
-                  " are sorted by increasing correlation. If number of bands is "
-                  " different, the change maps are sorted by decreasing correlation. \n"
-                  " \n"
-                  " The GetV1() and GetV2() methods allow retrieving the linear "
-                  " combinations used to generate the Mad change maps as a vnl_matrix of "
-                  " double, and the GetRho() method allows retrieving the correlation "
-                  " associated to each Mad change maps as a vnl_vector. \n"
-                  " \n"
-                  " This filter has been implemented from the Matlab code kindly made "
-                  " available by the authors here: \n"
-                  " http://www2.imm.dtu.dk/~aa/software.html \n"
-                  " \n"
-                  " Both cases (same and different number of bands) have been validated "
-                  " by comparing the output image to the output produced by the Matlab "
-                  " code, and the reference images for testing have been generated from "
-                  " the Matlab code using Octave." );
-
-    AddDocTag(Tags::FeatureExtraction);
+    SetDocSeeAlso("[1] Nielsen, A. A., & Conradsen, K. (1997). Multivariate alteration"
+                  "detection (MAD) in multispectral, bi-temporal image data: A new"
+                  "approach to change detection studies.\n"
+                  "[2] http://www2.imm.dtu.dk/~aa/software.html");
+
+    AddDocTag(Tags::ChangeDetection);
 
     AddParameter(ParameterType_InputImage,  "in1", "Input Image 1");
-    SetParameterDescription("in1","Image which describe initial state of the scene.");
+    SetParameterDescription("in1","Multiband image of the scene before perturbations");
     AddParameter(ParameterType_InputImage,  "in2", "Input Image 2");
-    SetParameterDescription("in2","Image which describe scene after perturbations.");
+    SetParameterDescription("in2","Mutliband image of the scene after perturbations.");
     AddParameter(ParameterType_OutputImage, "out", "Change Map");
-    SetParameterDescription("out","Image of detected changes.");
+    SetParameterDescription("out","Multiband image containing change maps. Each map will be in range [-1,1], so a floating point output type is advised.");
 
     AddRAMParameter();
 

Modules/Applications/AppClassification/app/otbComputeImagesStatistics.cxx

@@ -48,12 +48,19 @@ private:
   void DoInit() ITK_OVERRIDE
   {
     SetName("ComputeImagesStatistics");
-    SetDescription("Computes global mean and standard deviation for each band from a set of images and optionally saves the results in an XML file.");
     SetDocName("Compute Images second order statistics");
-    SetDocLongDescription("This application computes a global mean and standard deviation for each band of a set of images and optionally saves the results in an XML file. The output XML is intended to be used an input for the TrainImagesClassifier application to normalize samples before learning.");
-    SetDocLimitations("Each image of the set must contain the same bands as the others (i.e. same types, in the same order).");
+    SetDescription("Computes global mean and standard deviation for each band "
+      "from a set of images and optionally saves the results in an XML file.");
+    SetDocLongDescription("This application computes a global mean and standard deviation "
+      "for each band of a set of images and optionally saves the results in an XML file."
+      " The output XML is intended to be used an input "
+      "for the TrainImagesClassifier application to normalize samples before learning. "
+      "You can also normalize the image with the XML file in the ImageClassifier application.");
+
+    SetDocLimitations("Each image of the set must contain the same bands as the others"
+                      " (i.e. same types, in the same order).");
     SetDocAuthors("OTB-Team");
-    SetDocSeeAlso("Documentation of the TrainImagesClassifier application.");
+    SetDocSeeAlso("Documentation of the TrainImagesClassifier and ImageClassifier application.");
 
     AddDocTag(Tags::Learning);
     AddDocTag(Tags::Analysis);

Modules/Applications/AppSARCalibration/app/otbSARDeburst.cxx

@@ -47,23 +47,38 @@ private:
   void DoInit() ITK_OVERRIDE
   {
     SetName("SARDeburst");
-    SetDescription("This application performs a deburst operation by removing redundant lines. \n");
+    SetDescription("This application performs deburst of Sentinel1 IW SLC images by removing redundant lines.\n");
 
     // Documentation
     SetDocName("SAR Deburst");
-    SetDocLongDescription("This application performs a deburst operation by removing redundant lines between burst. This operation is useful when dealing with Sentinel1 IW SLC products, where each subswath is composed of several overlapping burst separated by black lines. Lines to remove are computed by SAR sensor model in OSSIM plugins. The output image is smaller in azimuth direction than the input line, because of removed lines. Note that the output sensor model is updated accordingly. This deburst filter is the perfect preprocessing step to orthorectify S1 IW SLC product with OTB without suffering from artifacts caused by bursts separation.\n");
-    SetDocLimitations("Only Sentinel1 IW SLC products are supported for now.");
+    SetDocLongDescription("Sentinel1 IW SLC products are composed of several burst overlapping in"
+                          " azimuth time for each subswath, separated by black lines [1]. The deburst"
+                          " operation consist in generating a continuous image in terms of azimuth"
+                          " time, by removing black separation lines as well as redundant lines"
+                          " between bursts.\n\n"
+                          
+                          "Note that the output sensor model is updated accordingly. This deburst"
+                          " operation is the perfect preprocessing step to orthorectify S1 IW SLC"
+                          " product with OTB [2] without suffering from artifacts caused by"
+                          " bursts separation.");
+    
+    SetDocLimitations("Only Sentinel1 IW SLC products are supported for now. Processing of"
+                      " other Sentinel1 modes or TerrasarX images will result in no changes in"
+                      " the image and metadata. Images from other sensors will lead to an"
+                      " error.");
+    
     SetDocAuthors("OTB-Team");
-    SetDocSeeAlso("OrthoRectification");
+    SetDocSeeAlso("[1] Sentinel1 User Handbook, p. 52: https://sentinel.esa.int/documents/247904/685163/Sentinel-1_User_Handbook\n"
+                  "[2] OrthoRectification application");
 
-    AddDocTag(Tags::Calibration);
     AddDocTag(Tags::SAR);
+    AddDocTag(Tags::Calibration);
 
-    AddParameter(ParameterType_InputImage,  "in", "Input Image");
-    SetParameterDescription("in", "Input image");
+    AddParameter(ParameterType_InputImage,  "in", "Input Sentinel1 IW SLC Image");
+    SetParameterDescription("in", "Raw Sentinel1 IW SLC image, or any extract of such made by OTB (geom file needed)");
 
     AddParameter(ParameterType_OutputImage,  "out", "Output Image");
-    SetParameterDescription("out", "Output deburst image");
+    SetParameterDescription("out", "Deburst image, with updated geom file that can be further used by Orthorectification application. If the input image is a raw Sentinel1 product, uint16 output type should be used (encoding of S1 product). Otherwise, output type should match type of input image.");
 
     AddRAMParameter();