TrainImagesClassifier-ann otbcli_TrainImagesClassifier TrainImagesClassifier (ann) Learning Train a classifier from multiple pairs of images and training vector data. ParameterMultipleInput io.il Input Image List A list of input images. False ParameterMultipleInput io.vd Input Vector Data List A list of vector data to select the training samples. False ParameterFile io.imstat Input XML image statistics file Input XML file containing the mean and the standard deviation of the input images. True OutputFile io.confmatout Output confusion matrix Output file containing the confusion matrix (.csv format). OutputFile io.out Output model Output file containing the model estimated (.txt format). ParameterNumber elev.default Default elevation This parameter allows setting the default height above ellipsoid when there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles, and no geoid file has been set. This is also used by some application as an average elevation value. 0 False ParameterNumber sample.mt Maximum training sample size per class Maximum size per class (in pixels) of the training sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available training sample list per class will be equal to the surface area of the smallest class multiplied by the training sample ratio. 1000 False ParameterNumber sample.mv Maximum validation sample size per class Maximum size per class (in pixels) of the validation sample list (default = 1000) (no limit = -1). If equal to -1, then the maximal size of the available validation sample list per class will be equal to the surface area of the smallest class multiplied by the validation sample ratio. 1000 False ParameterNumber sample.bm Bound sample number by minimum Bound the number of samples for each class by the number of available samples by the smaller class. Proportions between training and validation are respected. Default is true (=1). 1 False ParameterBoolean sample.edg On edge pixel inclusion Takes pixels on polygon edge into consideration when building training and validation samples. True True ParameterNumber sample.vtr Training and validation sample ratio Ratio between training and validation samples (0.0 = all training, 1.0 = all validation) (default = 0.5). 0.5 False ParameterString sample.vfn Name of the discrimination field Name of the field used to discriminate class labels in the input vector data files. Class False ParameterSelection classifier Classifier to use for the training Choice of the classifier to use for the training. ann 0 False ParameterSelection classifier.ann.t Train Method Type Type of training method for the multilayer perceptron (MLP) neural network. reg back 0 False ParameterString classifier.ann.sizes Number of neurons in each intermediate layer The number of neurons in each intermediate layer (excluding input and output layers). False ParameterSelection classifier.ann.f Neuron activation function type Neuron activation function. ident sig gau 1 False ParameterNumber classifier.ann.a Alpha parameter of the activation function Alpha parameter of the activation function (used only with sigmoid and gaussian functions). 1 False ParameterNumber classifier.ann.b Beta parameter of the activation function Beta parameter of the activation function (used only with sigmoid and gaussian functions). 1 False ParameterNumber classifier.ann.bpdw Strength of the weight gradient term in the BACKPROP method Strength of the weight gradient term in the BACKPROP method. The recommended value is about 0.1. 0.1 False ParameterNumber classifier.ann.bpms Strength of the momentum term (the difference between weights on the 2 previous iterations) Strength of the momentum term (the difference between weights on the 2 previous iterations). This parameter provides some inertia to smooth the random fluctuations of the weights. It can vary from 0 (the feature is disabled) to 1 and beyond. The value 0.1 or so is good enough. 0.1 False ParameterNumber classifier.ann.rdw Initial value Delta_0 of update-values Delta_{ij} in RPROP method Initial value Delta_0 of update-values Delta_{ij} in RPROP method (default = 0.1). 0.1 False ParameterNumber classifier.ann.rdwm Update-values lower limit Delta_{min} in RPROP method Update-values lower limit Delta_{min} in RPROP method. It must be positive (default = 1e-7). 1e-07 False ParameterSelection classifier.ann.term Termination criteria Termination criteria. iter eps all 2 False ParameterNumber classifier.ann.eps Epsilon value used in the Termination criteria Epsilon value used in the Termination criteria. 0.01 False ParameterNumber classifier.ann.iter Maximum number of iterations used in the Termination criteria Maximum number of iterations used in the Termination criteria. 1000 False ParameterNumber rand set user defined seed Set specific seed. with integer value. 0 True