DOC: improve documentation of MultiImageSamplingRate

Modules/Applications/AppClassification/app/otbMultiImageSamplingRate.cxx

@@ -75,13 +75,36 @@ private:
       "  - byclass : set the required number for each class manually, with an "
       "input CSV file (first column is class name, second one is the required "
       "samples number).\n"
-      "The multi-image modes are :\n"
-      "  - proportional : we try to split proportionally the number of required"
-      " samples in each image\n"
-      "  - equal : we split equally the number of required samples in each "
-      "image.\n"
-      "  - custom : the user chooses how to split the number of required "
-      "samples for each image.\n"
+      "The multi-image modes (mim) are : proportional, equal and custom. The custom "
+      "mode lets the users choose the distribution of samples among the "
+      "images. The different behaviours are described below. Ti(c) and Ni(c) "
+      " refers resp. to the total number and needed number of samples in "
+      "image i for class c. Let's call L the total number of images.\n"
+      "  > strategy = all\n"
+      "    + Same behaviour for all modes : take all samples\n"
+      "  > strategy = constant\n"
+      "    (let's call M the global number of samples required per class)\n"
+      "    + mim = proportional : For each image i and each class c,\n"
+      "       Ni( c ) = M * Ti( c ) / sum_k( Tk(c) )\n"
+      "    + mim = equal : For each image i and each class c,\n"
+      "       Ni( c ) = M / L\n"
+      "    + mim = custom : For each image i and each class c,\n"
+      "       Ni( c ) = Mi where Mi is the custom requested number of samples for image i\n"
+      "  > strategy = byClass\n"
+      "    (let's call M(c) the global number of samples for class c)\n"
+      "    + mim = proportional : For each image i and each class c\n"
+      "       Ni( c ) = M(c) * Ti( c ) / sum_k( Tk(c) )\n"
+      "    + mim = equal : For each image i and each class c,\n"
+      "       Ni( c ) = M(c) / L\n"
+      "    + mim = custom : For each image i and each class c,\n"
+      "       Ni( c ) = Mi(c) where Mi(c) is the custom requested number of samples for image i and class c\n"
+      "  > strategy = smallest class\n"
+      "    + mim = proportional :\n"
+      "       The smallest class size (computed globally) is used for the strategy constant+proportional\n"
+      "    + mim = equal :\n"
+      "       The smallest class size (computed globally) is used for the strategy constant+equal\n"
+      "    + mim = custom :\n"
+      "       The smallest class is computed and used for each image separately\n"
       );
     SetDocLimitations("None");
     SetDocAuthors("OTB-Team");