diff --git a/Modules/Adapters/GdalAdapters/include/otbGeometriesSource.h b/Modules/Adapters/GdalAdapters/include/otbGeometriesSource.h
index 0d87528dd52c736b0648115d0133c3ef1d114859..aa1da8c42e9b794930c464c21ed1b33a68ff7bd1 100644
--- a/Modules/Adapters/GdalAdapters/include/otbGeometriesSource.h
+++ b/Modules/Adapters/GdalAdapters/include/otbGeometriesSource.h
@@ -31,7 +31,7 @@ class Layer;
class GeometriesSet;
} // otb namespace
-/**\defgroup GeometriesFilters
+/**\defgroup GeometriesFilters Filters of geometries sets
* \ingroup gGeometry Filters
* Filters of geometries sets.
*/
diff --git a/Modules/Adapters/GdalAdapters/include/otbGeometriesToGeometriesFilter.h b/Modules/Adapters/GdalAdapters/include/otbGeometriesToGeometriesFilter.h
index 3e61efceec59b74c7c8b8ed544a62ea461d7f520..f5b9de54a347a66dc4fc795c389dd7a68bfe6a7f 100644
--- a/Modules/Adapters/GdalAdapters/include/otbGeometriesToGeometriesFilter.h
+++ b/Modules/Adapters/GdalAdapters/include/otbGeometriesToGeometriesFilter.h
@@ -180,7 +180,6 @@ struct FieldCopyTransformation
}
/**
* In-place transformation: does nothing.
- * \param[in] inoutFeature \c Feature to change.
* \throw Nothing
*/
void fieldsTransform(ogr::Feature const& itkNotUsed(inoutFeature)) const
@@ -189,8 +188,8 @@ struct FieldCopyTransformation
}
/**
* By-Copy transformation: copies all fields.
- * \param[in] inFeature input \c Feature
- * \param[in,out] outFeature output \c Feature
+ * \param [in] inFeature input \c Feature
+ * \param [in,out] outFeature output \c Feature
*
* \throw itk::ExceptionObject if the fields cannot be copied.
*/
@@ -199,8 +198,8 @@ struct FieldCopyTransformation
/**
* Defines the fields in the destination layer.
* The default action is to copy all fieds from one layer to another.
- * \param[in] source source \c Layer
- * \param[in,out] dest destination \c Layer
+ * \param [in] source source \c Layer
+ * \param [in,out] dest destination \c Layer
* \throw itk::ExceptionObject in case the operation can't succeed.
*/
void DefineFields(ogr::Layer const& source, ogr::Layer & dest) const;
diff --git a/Modules/Core/LabelMap/include/otbBandsStatisticsAttributesLabelMapFilter.h b/Modules/Core/LabelMap/include/otbBandsStatisticsAttributesLabelMapFilter.h
index f3de02b9a69504c3c06afbf681e6ec353c3293d3..242affc0cac6a4158d04abd7269e6818edf7cc9f 100644
--- a/Modules/Core/LabelMap/include/otbBandsStatisticsAttributesLabelMapFilter.h
+++ b/Modules/Core/LabelMap/include/otbBandsStatisticsAttributesLabelMapFilter.h
@@ -121,7 +121,7 @@ private:
* StatisticsAttributesLabelMapFilter on each channel independently
*
* The feature name is constructed as:
- * 'STATS' + '::' + 'Band' + #BandIndex + '::' + StatisticName
+ * 'STATS' + '::' + 'Band' + band_index + '::' + statistic_name
*
* The ReducedAttributesSet flag allows to tell the internal
* statistics filter to compute only the main attributes (mean, variance, skewness and kurtosis).
diff --git a/Modules/Core/Transform/include/otbInverseLogPolarTransform.h b/Modules/Core/Transform/include/otbInverseLogPolarTransform.h
index 9be87af8981d69d512b7b2b6cbc3ec391efc6931..7cc4447bb86a74e7078a69c6059db067ed706143 100644
--- a/Modules/Core/Transform/include/otbInverseLogPolarTransform.h
+++ b/Modules/Core/Transform/include/otbInverseLogPolarTransform.h
@@ -88,7 +88,7 @@ public:
virtual ParametersType& GetParameters(void) const;
/**
* Set the Fixed Parameters
- * \param The Fixed parameters of the transform.
+ * \param param The fixed parameters of the transform.
*/
virtual void SetFixedParameters( const ParametersType & param)
{ this->m_FixedParameters = param; }
diff --git a/Modules/Core/Transform/include/otbLogPolarTransform.h b/Modules/Core/Transform/include/otbLogPolarTransform.h
index f8f82401add1c76160e46770dd6558e7ef1bcbb3..e0e8278fba2d8500f60ffd59299b7990e8153336 100644
--- a/Modules/Core/Transform/include/otbLogPolarTransform.h
+++ b/Modules/Core/Transform/include/otbLogPolarTransform.h
@@ -90,7 +90,7 @@ public:
/**
* Set the Fixed Parameters
- * \param The Fixed parameters of the transform.
+ * \param param The fixed parameters of the transform.
*/
virtual void SetFixedParameters( const ParametersType & param)
{ this->m_FixedParameters = param; }
diff --git a/Modules/Core/VectorDataBase/include/otbDataNode.h b/Modules/Core/VectorDataBase/include/otbDataNode.h
index 9765f7795568a06586843c69a0dbd0bc9a57901c..ba6ba788f2a997bee02ae0d718763cc7a625d870 100644
--- a/Modules/Core/VectorDataBase/include/otbDataNode.h
+++ b/Modules/Core/VectorDataBase/include/otbDataNode.h
@@ -254,7 +254,7 @@ public:
/**
* Copy the field list from a DataNode
- * \param datanode where to get the keywordlist to copy.
+ * \param dataNode datanode where to get the keywordlist to copy.
*/
void CopyFieldList(const DataNode * dataNode);
diff --git a/Modules/Feature/Descriptors/include/otbImageToSIFTKeyPointSetFilter.h b/Modules/Feature/Descriptors/include/otbImageToSIFTKeyPointSetFilter.h
index 3dae4d1e84744722c3b011706a8031ec0bd80051..211c7f9fd274385cfc075ad7f0140f887cd7226d 100644
--- a/Modules/Feature/Descriptors/include/otbImageToSIFTKeyPointSetFilter.h
+++ b/Modules/Feature/Descriptors/include/otbImageToSIFTKeyPointSetFilter.h
@@ -106,7 +106,7 @@ public:
*
* Orientation is expressed in degree in the range [0, 360] with a precision of 10 degrees.
*
- * \example FeatureExtraction/SIFTExample.cxx
+ * \example Patented/SIFTExample.cxx
*
*
* \ingroup OTBDescriptors
diff --git a/Modules/Feature/Descriptors/include/otbSiftFastImageFilter.h b/Modules/Feature/Descriptors/include/otbSiftFastImageFilter.h
index e07a0af4a1e95999bc62f272fb3b8eb1d86c7821..8ab518caf8aa18f3d28ae27cd4c15dfacd48c308 100644
--- a/Modules/Feature/Descriptors/include/otbSiftFastImageFilter.h
+++ b/Modules/Feature/Descriptors/include/otbSiftFastImageFilter.h
@@ -41,7 +41,7 @@ namespace otb
*
* \sa ImageToSIFTKeyPointSetFilter
*
- * \example FeatureExtraction/SIFTFastExample.cxx
+ * \example Patented/SIFTFastExample.cxx
*
* \ingroup OTBDescriptors
*/
diff --git a/Modules/Filtering/MathParserX/include/otbBandMathXImageFilter.h b/Modules/Filtering/MathParserX/include/otbBandMathXImageFilter.h
index a48fea6f0f47ed711645541964b337e7200e4bce..f259390e069da651bf223a463dcca5eef435010d 100644
--- a/Modules/Filtering/MathParserX/include/otbBandMathXImageFilter.h
+++ b/Modules/Filtering/MathParserX/include/otbBandMathXImageFilter.h
@@ -40,7 +40,7 @@ namespace otb
*
* This filter is based on the mathematical parser library muParserX.
* The built in functions and operators list is available at:
- * \url{http:*articles.beltoforion.de/article.php?a=muparserx}.
+ * http://articles.beltoforion.de/article.php?a=muparserx.
*
* In order to use this filter, at least one input image is to be
* set. An associated variable name can be specified or not by using
diff --git a/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.h b/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.h
index 9f1db559d2cdea10cd0b7392417b86323c3c3259..eb82efc56f97ff2dbdef3ccba03a3447793081f2 100644
--- a/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbBoostMachineLearningModel.h
@@ -79,7 +79,7 @@ public:
/** Setters/Getters to the threshold WeightTrimRate.
* A threshold between 0 and 1 used to save computational time.
- * Samples with summary weight \leq 1 - WeightTrimRate do not participate in the next iteration of training.
+ * Samples with summary weight \f$ w \leq 1 - WeightTrimRate \f$ do not participate in the next iteration of training.
* Set this parameter to 0 to turn off this functionality.
* Default is 0.95
* \see http://docs.opencv.org/modules/ml/doc/boosting.html#cvboostparams-cvboostparams
diff --git a/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.h b/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.h
index 3fe427458e2347ad91025fefdd768ba08523aee8..6e11039327b4cd531343ba470eb390b1eb63375f 100644
--- a/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbDecisionTreeMachineLearningModel.h
@@ -90,7 +90,7 @@ public:
itkGetMacro(UseSurrogates, bool);
itkSetMacro(UseSurrogates, bool);
- /** Cluster possible values of a categorical variable into K \leq max_categories clusters to find
+ /** Cluster possible values of a categorical variable into \f$ K \leq MaxCategories \f$ clusters to find
* a suboptimal split. If a discrete variable, on which the training procedure tries to make a split,
* takes more than max_categories values, the precise best subset estimation may take a very long time
* because the algorithm is exponential. Instead, many decision trees engines (including ML) try to find
diff --git a/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.h b/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.h
index af99bb9da0375e4fee3a1454489ef1d45bba765e..a45608624f67afaa0554a4a00d77f4378244c079 100644
--- a/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbKNearestNeighborsMachineLearningModel.h
@@ -55,14 +55,14 @@ public:
/** Setters/Getters to the number of neighbors to use
* Default is 32
- * see @http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html
+ * \see http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html
*/
itkGetMacro(K, int);
itkSetMacro(K, int);
/** Setters/Getters to IsRegression flag
* Default is False
- * see @http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html
+ * \see http://docs.opencv.org/modules/ml/doc/k_nearest_neighbors.html
*/
itkGetMacro(IsRegression, bool);
itkSetMacro(IsRegression, bool);
diff --git a/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h b/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h
index 4c50e6b9f6d66f02f49b17af44e2653fd59f34f4..ff2d6de384dc71071e8cbc981154c2ccc7f33ff5 100644
--- a/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbNeuralNetworkMachineLearningModel.h
@@ -117,14 +117,14 @@ public:
itkGetMacro(BackPropMomentScale, double);
itkSetMacro(BackPropMomentScale, double);
- /** Initial value \Delta_0 of update-values \Delta_{ij} in RPROP method.
+ /** Initial value \f$ \Delta_0 \f$ of update-values \f$ \Delta_{ij} \f$ in RPROP method.
* Default is 0.1
* \see http://docs.opencv.org/modules/ml/doc/neural_networks.html
*/
itkGetMacro(RegPropDW0, double);
itkSetMacro(RegPropDW0, double);
- /** Update-values lower limit \Delta_{min} in RPROP method.
+ /** Update-values lower limit \f$ \Delta_{min} \f$ in RPROP method.
* It must be positive. Default is FLT_EPSILON
* \see http://docs.opencv.org/modules/ml/doc/neural_networks.html
*/
diff --git a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
index deddd5456f70fe4c31d2932d2b2025dee0c03597..5f2241d005f1596519d06add9ee49170f1c801ee 100644
--- a/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
+++ b/Modules/Learning/Supervised/include/otbRandomForestsMachineLearningModel.h
@@ -83,55 +83,21 @@ public:
//@}
//Setters of RT parameters (documentation get from opencv doxygen 2.4)
- /* the depth of the tree. A low value will likely underfit and conversely a
- * high value will likely overfit. The optimal value can be obtained using cross
- * validation or other suitable methods. */
itkGetMacro(MaxDepth, int);
itkSetMacro(MaxDepth, int);
- /* minimum samples required at a leaf node for it to be split. A reasonable
- * value is a small percentage of the total data e.g. 1%. */
+
itkGetMacro(MinSampleCount, int);
itkSetMacro(MinSampleCount, int);
- /* Termination criteria for regression trees. If all absolute differences
- * between an estimated value in a node and values of train samples in this node
- * are less than this parameter then the node will not be split */
itkGetMacro(RegressionAccuracy, double);
itkSetMacro(RegressionAccuracy, double);
itkGetMacro(ComputeSurrogateSplit, bool);
itkSetMacro(ComputeSurrogateSplit, bool);
- /* Cluster possible values of a categorical variable into K \leq
- * max_categories clusters to find a suboptimal split. If a discrete variable,
- * on which the training procedure tries to make a split, takes more than
- * max_categories values, the precise best subset estimation may take a very
- * long time because the algorithm is exponential. Instead, many decision
- * trees engines (including ML) try to find sub-optimal split in this case by
- * clustering all the samples into max categories clusters that is some
- * categories are merged together. The clustering is applied only in n>2-class
- * classification problems for categorical variables with N > max_categories
- * possible values. In case of regression and 2-class classification the
- * optimal split can be found efficiently without employing clustering, thus
- * the parameter is not used in these cases.
- */
+
itkGetMacro(MaxNumberOfCategories, int);
itkSetMacro(MaxNumberOfCategories, int);
- /* The array of a priori class probabilities, sorted by the class label
- * value. The parameter can be used to tune the decision tree preferences toward
- * a certain class. For example, if you want to detect some rare anomaly
- * occurrence, the training base will likely contain much more normal cases than
- * anomalies, so a very good classification performance will be achieved just by
- * considering every case as normal. To avoid this, the priors can be specified,
- * where the anomaly probability is artificially increased (up to 0.5 or even
- * greater), so the weight of the misclassified anomalies becomes much bigger,
- * and the tree is adjusted properly. You can also think about this parameter as
- * weights of prediction categories which determine relative weights that you
- * give to misclassification. That is, if the weight of the first category is 1
- * and the weight of the second category is 10, then each mistake in predicting
- * the second category is equivalent to making 10 mistakes in predicting the
- first category. */
-
std::vector<float> GetPriors() const
{
return m_Priors;
@@ -141,29 +107,22 @@ public:
{
m_Priors = priors;
}
- /* If true then variable importance will be calculated and then it can be
- retrieved by CvRTrees::get_var_importance(). */
+
itkGetMacro(CalculateVariableImportance, bool);
itkSetMacro(CalculateVariableImportance, bool);
- /* The size of the randomly selected subset of features at each tree node and
- * that are used to find the best split(s). If you set it to 0 then the size will
- be set to the square root of the total number of features. */
+
itkGetMacro(MaxNumberOfVariables, int);
itkSetMacro(MaxNumberOfVariables, int);
- /* The maximum number of trees in the forest (surprise, surprise). Typically
- * the more trees you have the better the accuracy. However, the improvement in
- * accuracy generally diminishes and asymptotes pass a certain number of
- * trees. Also to keep in mind, the number of tree increases the prediction time
- linearly. */
+
itkGetMacro(MaxNumberOfTrees, int);
itkSetMacro(MaxNumberOfTrees, int);
- /* Sufficient accuracy (OOB error) */
+
itkGetMacro(ForestAccuracy, float);
itkSetMacro(ForestAccuracy, float);
- /* The type of the termination criteria */
+
itkGetMacro(TerminationCriteria, int);
itkSetMacro(TerminationCriteria, int);
- /* Perform regression instead of classification */
+
itkGetMacro(RegressionMode, bool);
itkSetMacro(RegressionMode, bool);
@@ -193,17 +152,66 @@ private:
void operator =(const Self&); //purposely not implemented
CvRTrees * m_RFModel;
+ /** The depth of the tree. A low value will likely underfit and conversely a
+ * high value will likely overfit. The optimal value can be obtained using cross
+ * validation or other suitable methods. */
int m_MaxDepth;
+ /** minimum samples required at a leaf node for it to be split. A reasonable
+ * value is a small percentage of the total data e.g. 1%. */
int m_MinSampleCount;
+ /** Termination criteria for regression trees. If all absolute differences
+ * between an estimated value in a node and values of train samples in this node
+ * are less than this parameter then the node will not be split */
float m_RegressionAccuracy;
bool m_ComputeSurrogateSplit;
+ /** Cluster possible values of a categorical variable into
+ * \f$ K \leq MaxCategories \f$
+ * clusters to find a suboptimal split. If a discrete variable,
+ * on which the training procedure tries to make a split, takes more than
+ * max_categories values, the precise best subset estimation may take a very
+ * long time because the algorithm is exponential. Instead, many decision
+ * trees engines (including ML) try to find sub-optimal split in this case by
+ * clustering all the samples into max categories clusters that is some
+ * categories are merged together. The clustering is applied only in n>2-class
+ * classification problems for categorical variables with N > max_categories
+ * possible values. In case of regression and 2-class classification the
+ * optimal split can be found efficiently without employing clustering, thus
+ * the parameter is not used in these cases.
+ */
int m_MaxNumberOfCategories;
+ /** The array of a priori class probabilities, sorted by the class label
+ * value. The parameter can be used to tune the decision tree preferences toward
+ * a certain class. For example, if you want to detect some rare anomaly
+ * occurrence, the training base will likely contain much more normal cases than
+ * anomalies, so a very good classification performance will be achieved just by
+ * considering every case as normal. To avoid this, the priors can be specified,
+ * where the anomaly probability is artificially increased (up to 0.5 or even
+ * greater), so the weight of the misclassified anomalies becomes much bigger,
+ * and the tree is adjusted properly. You can also think about this parameter as
+ * weights of prediction categories which determine relative weights that you
+ * give to misclassification. That is, if the weight of the first category is 1
+ * and the weight of the second category is 10, then each mistake in predicting
+ * the second category is equivalent to making 10 mistakes in predicting the
+ * first category. */
std::vector<float> m_Priors;
+ /** If true then variable importance will be calculated and then it can be
+ * retrieved by CvRTrees::get_var_importance(). */
bool m_CalculateVariableImportance;
+ /** The size of the randomly selected subset of features at each tree node and
+ * that are used to find the best split(s). If you set it to 0 then the size will
+ * be set to the square root of the total number of features. */
int m_MaxNumberOfVariables;
+ /** The maximum number of trees in the forest (surprise, surprise). Typically
+ * the more trees you have the better the accuracy. However, the improvement in
+ * accuracy generally diminishes and asymptotes pass a certain number of
+ * trees. Also to keep in mind, the number of tree increases the prediction time
+ *linearly. */
int m_MaxNumberOfTrees;
+ /** Sufficient accuracy (OOB error) */
float m_ForestAccuracy;
+ /** The type of the termination criteria */
int m_TerminationCriteria;
+ /** Perform regression instead of classification */
bool m_RegressionMode;
};
} // end namespace otb