DOC: add some documentation in filters headers

include/otbTensorflowMultisourceModelBase.h

@@ -50,14 +50,14 @@ namespace otb
  * Target nodes names of the TensorFlow graph that must be triggered can be set
  * with the SetTargetNodesNames.
  *
- * The OutputTensorNames consists in a strd::vector of std::string, and
+ * The OutputTensorNames consists in a std::vector of std::string, and
  * corresponds to the names of tensors that will be computed during the session.
  * As for input placeholders, output tensors field of expression
  * (OutputExpressionFields, a std::vector of SizeType), i.e. the output
  * space that the TensorFlow model will "generate", must be provided.
  *
  * Finally, a list of scalar placeholders can be fed in the form of std::vector
- * of std::string, each one expressing the assigment of a signle valued
+ * of std::string, each one expressing the assignment of a single valued
  * placeholder, e.g. "drop_rate=0.5 learning_rate=0.002 toto=true".
  * See otb::tf::ExpressionToTensor() to know more about syntax.
  *

include/otbTensorflowMultisourceModelLearningBase.h

@@ -23,7 +23,15 @@ namespace otb
 
 /**
  * \class TensorflowMultisourceModelLearningBase
- * \brief This filter is the base class for all learning filters.
+ * \brief This filter is the base class for all filters that input patches images.
+ *
+ * One input patches image consist in an image of size (pszx, pszy*n, nbands) where:
+ * -pszx   : is the width of one patch
+ * -pszy   : is the height of one patch
+ * -n      : is the number of patches in the patches image
+ * -nbands : is the number of channels in the patches image
+ *
+ * This filter verify that every patches images are consistent.
  *
  * The batch size can be set using the SetBatchSize() method.
  * The streaming can be activated to allow the processing of huge datasets.