TrainImagesClassifier-svm otbcli_TrainImagesClassifier TrainImagesClassifier (svm) 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 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 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 ParameterBoolean sample.edg On edge pixel inclusion Takes pixels on polygon edge into consideration when building training and validation samples. 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 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. svm 0 ParameterSelection classifier.svm.m SVM Model Type Type of SVM formulation. csvc nusvc oneclass 0 ParameterSelection classifier.svm.k SVM Kernel Type SVM Kernel Type. linear rbf poly sigmoid 0 ParameterNumber classifier.svm.c Cost parameter C SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins. 1 ParameterNumber classifier.svm.nu Parameter nu of a SVM optimization problem (NU_SVC / ONE_CLASS) Parameter nu of a SVM optimization problem. 0 ParameterNumber classifier.svm.coef0 Parameter coef0 of a kernel function (POLY / SIGMOID) Parameter coef0 of a kernel function (POLY / SIGMOID). 0 ParameterNumber classifier.svm.gamma Parameter gamma of a kernel function (POLY / RBF / SIGMOID) Parameter gamma of a kernel function (POLY / RBF / SIGMOID). 1 ParameterNumber classifier.svm.degree Parameter degree of a kernel function (POLY) Parameter degree of a kernel function (POLY). 1 ParameterBoolean classifier.svm.opt Parameters optimization SVM parameters optimization flag. -If set to True, then the optimal SVM parameters will be estimated. Parameters are considered optimal by OpenCV when the cross-validation estimate of the test set error is minimal. Finally, the SVM training process is computed 10 times with these optimal parameters over subsets corresponding to 1/10th of the training samples using the k-fold cross-validation (with k = 10). -If set to False, the SVM classification process will be computed once with the currently set input SVM parameters over the training samples. -Thus, even with identical input SVM parameters and a similar random seed, the output SVM models will be different according to the method used (optimized or not) because the samples are not identically processed within OpenCV. True ParameterNumber rand set user defined seed Set specific seed. with integer value. 0