TrainImagesRegression

从特征图像、预测图像和训练向量数据的多个三元组训练回归模型。

描述

从预测图像、标签图像和训练向量数据的多个三元组中训练分类器。

训练向量数据必须包含对应于输入采样位置的多边形。该数据用于使用预测器图像的每个频带中的像素值和从拉格尔图像中提取的对应地面实况来提取样本。如果没有提供训练向量数据，则将在全图像范围内提取样本。

在应用程序结束时，使用输出模型和验证向量数据来计算基本真实和预测值之间的均方误差。请注意，如果未提供验证数据，则将使用训练数据进行验证。

训练和验证样本的数量可以用参数指定。如果没有给出尺寸，将使用所有样品。

该应用程序基于LibSVM、OpenCV机器学习和Shark ML。这个应用程序的输出是一个文本模型文件，其格式与所选的ML模型类型相对应。没有图像也没有矢量数据输出。

参数

输入和输出数据

这组参数允许设置输入和输出数据。

Input predictor Image List -io.il image1 image2... Mandatory
A list of input predictor images.

Input label Image List -io.ip image1 image2... Mandatory
A list of input label images.

Input Vector Data List -io.vd vectorfile1 vectorfile2...
A list of vector data to select the training samples.

Validation Vector Data List -io.valid vectorfile1 vectorfile2...
A list of vector data to select the validation samples.

Input XML image statistics file -io.imstat filename [dtype]
XML file containing mean and variance of each feature.

Output model -io.out filename [dtype] Mandatory
Output file containing the model estimated (.txt format).

Mean Square Error -io.mse float
Mean square error computed using the validation dataset

抽样参数

这组参数允许设置采样参数

Number of training samples -sample.nt int
Number of training samples.

Number of validation samples -sample.nv int
Number of validation samples.

Training and validation sample ratio -sample.ratio float Default value: 0.5
Ratio between training and validation samples.

Sampler type -sample.type [periodic|random] Default value: periodic
Type of sampling (periodic, pattern based, random)

  • Periodic sampler
    Takes samples regularly spaced
  • Random sampler
    The positions to select are randomly shuffled.

周期性采样器选项

Jitter amplitude -sample.type.periodic.jitter int Default value: 0
Jitter amplitude added during sample selection (0 = no jitter)

Random seed -rand int
Set a specific random seed with integer value.

Available RAM (MB) -ram int Default value: 256
Available memory for processing (in MB).

高程管理

这组参数允许管理高程值。

DEM directory -elev.dem directory
This parameter allows selecting a directory containing Digital Elevation Model files. Note that this directory should contain only DEM files. Unexpected behaviour might occurs if other images are found in this directory. Input DEM tiles should be in a raster format supported by GDAL.

Geoid File -elev.geoid filename [dtype]
Use a geoid grid to get the height above the ellipsoid in case there is no DEM available, no coverage for some points or pixels with no_data in the DEM tiles. A version of the geoid can be found on the OTB website (egm96.grd and egm96.grd.hdr at https://gitlab.orfeo-toolbox.org/orfeotoolbox/otb/-/tree/master/Data/Input/DEM).

Default elevation -elev.default float Default value: 0
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.

Classifier to use for the training -classifier [libsvm|dt|ann|rf|knn|sharkrf] Default value: libsvm
Choice of the classifier to use for the training.

LibSVM分类器选项

SVM Kernel Type -classifier.libsvm.k [linear|rbf|poly|sigmoid] Default value: linear
SVM Kernel Type.

  • Linear
    Linear Kernel, no mapping is done, this is the fastest option.
  • Gaussian radial basis function
    This kernel is a good choice in most of the case. It is an exponential function of the euclidean distance between the vectors.
  • Polynomial
    Polynomial Kernel, the mapping is a polynomial function.
  • Sigmoid
    The kernel is a hyperbolic tangente function of the vectors.

SVM Model Type -classifier.libsvm.m [epssvr|nusvr] Default value: epssvr
Type of SVM formulation.

  • Epsilon Support Vector Regression
    The distance between feature vectors from the training set and the fitting hyper-plane must be less than Epsilon. For outliers the penalty multiplier C is used
  • Nu Support Vector Regression
    Same as the epsilon regression except that this time the bounded parameter nu is used instead of epsilon

Cost parameter C -classifier.libsvm.c float Default value: 1
SVM models have a cost parameter C (1 by default) to control the trade-off between training errors and forcing rigid margins.

Gamma parameter -classifier.libsvm.gamma float Default value: 1
Set gamma parameter in poly/rbf/sigmoid kernel function

Coefficient parameter -classifier.libsvm.coef0 float Default value: 0
Set coef0 parameter in poly/sigmoid kernel function

Degree parameter -classifier.libsvm.degree int Default value: 3
Set polynomial degree in poly kernel function

Cost parameter Nu -classifier.libsvm.nu float Default value: 0.5
Cost parameter Nu, in the range 0..1, the larger the value, the smoother the decision.

Parameters optimization -classifier.libsvm.opt bool Default value: false
SVM parameters optimization flag.

Probability estimation -classifier.libsvm.prob bool Default value: false
Probability estimation flag.

Epsilon -classifier.libsvm.eps float Default value: 0.001
The distance between feature vectors from the training set and the fitting hyper-plane must be less than Epsilon. For outliersthe penalty multiplier is set by C.

决策树分类器选项

Maximum depth of the tree -classifier.dt.max int Default value: 10
The training algorithm attempts to split each node while its depth is smaller than the maximum possible depth of the tree. The actual depth may be smaller if the other termination criteria are met, and/or if the tree is pruned.

Minimum number of samples in each node -classifier.dt.min int Default value: 10
If the number of samples in a node is smaller than this parameter, then this node will not be split.

Termination criteria for regression tree -classifier.dt.ra float Default value: 0.01
If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split further.

Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split -classifier.dt.cat int Default value: 10
Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split.

Set Use1seRule flag to false -classifier.dt.r bool Default value: false
If true, then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate.

Set TruncatePrunedTree flag to false -classifier.dt.t bool Default value: false
If true, then pruned branches are physically removed from the tree.

人工神经网络分类器选项

Train Method Type -classifier.ann.t [back|reg] Default value: reg
Type of training method for the multilayer perceptron (MLP) neural network.

  • Back-propagation algorithm
    Method to compute the gradient of the loss function and adjust weights in the network to optimize the result.
  • Resilient Back-propagation algorithm
    Almost the same as the Back-prop algorithm except that it does not take into account the magnitude of the partial derivative (coordinate of the gradient) but only its sign.

Number of neurons in each intermediate layer -classifier.ann.sizes string1 string2... Mandatory
The number of neurons in each intermediate layer (excluding input and output layers).

Neuron activation function type -classifier.ann.f [ident|sig|gau] Default value: sig
This function determine whether the output of the node is positive or not depending on the output of the transfer function.

  • Identity function
  • Symmetrical Sigmoid function
  • Gaussian function (Not completely supported)

Alpha parameter of the activation function -classifier.ann.a float Default value: 1
Alpha parameter of the activation function (used only with sigmoid and gaussian functions).

Beta parameter of the activation function -classifier.ann.b float Default value: 1
Beta parameter of the activation function (used only with sigmoid and gaussian functions).

Strength of the weight gradient term in the BACKPROP method -classifier.ann.bpdw float Default value: 0.1
Strength of the weight gradient term in the BACKPROP method. The recommended value is about 0.1.

Strength of the momentum term (the difference between weights on the 2 previous iterations) -classifier.ann.bpms float Default value: 0.1
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.

Initial value Delta_0 of update-values Delta_{ij} in RPROP method -classifier.ann.rdw float Default value: 0.1
Initial value Delta_0 of update-values Delta_{ij} in RPROP method (default = 0.1).

Update-values lower limit Delta_{min} in RPROP method -classifier.ann.rdwm float Default value: 1e-07
Update-values lower limit Delta_{min} in RPROP method. It must be positive (default = 1e-7).

Termination criteria -classifier.ann.term [iter|eps|all] Default value: all
Termination criteria.

  • Maximum number of iterations
    Set the number of iterations allowed to the network for its training. Training will stop regardless of the result when this number is reached
  • Epsilon
    Training will focus on result and will stop once the precision isat most epsilon
  • Max. iterations + Epsilon
    Both termination criteria are used. Training stop at the first reached

Epsilon value used in the Termination criteria -classifier.ann.eps float Default value: 0.01
Epsilon value used in the Termination criteria.

Maximum number of iterations used in the Termination criteria -classifier.ann.iter int Default value: 1000
Maximum number of iterations used in the Termination criteria.

随机森林分类器选项

Maximum depth of the tree -classifier.rf.max int Default value: 5
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.

Minimum number of samples in each node -classifier.rf.min int Default value: 10
If the number of samples in a node is smaller than this parameter, then the node will not be split. A reasonable value is a small percentage of the total data e.g. 1 percent.

Termination Criteria for regression tree -classifier.rf.ra float Default value: 0
If all absolute differences between an estimated value in a node and the values of the train samples in this node are smaller than this regression accuracy parameter, then the node will not be split.

Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split -classifier.rf.cat int Default value: 10
Cluster possible values of a categorical variable into K <= cat clusters to find a suboptimal split.

Size of the randomly selected subset of features at each tree node -classifier.rf.var int Default value: 0
The size of the subset of features, randomly selected at each tree node, 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.

Maximum number of trees in the forest -classifier.rf.nbtrees int Default value: 100
The maximum number of trees in the forest. Typically, the more trees you have, the better the accuracy. However, the improvement in accuracy generally diminishes and reaches an asymptote for a certain number of trees. Also to keep in mind, increasing the number of trees increases the prediction time linearly.

Sufficient accuracy (OOB error) -classifier.rf.acc float Default value: 0.01
Sufficient accuracy (OOB error).

KNN分类器选项

Number of Neighbors -classifier.knn.k int Default value: 32
The number of neighbors to use.

Decision rule -classifier.knn.rule [mean|median] Default value: mean
Decision rule for regression output

  • Mean of neighbors values
    Returns the mean of neighbors values
  • Median of neighbors values
    Returns the median of neighbors values

鲨鱼随机森林分类器选项

Maximum number of trees in the forest -classifier.sharkrf.nbtrees int Default value: 100
The maximum number of trees in the forest. Typically, the more trees you have, the better the accuracy. However, the improvement in accuracy generally diminishes and reaches an asymptote for a certain number of trees. Also to keep in mind, increasing the number of trees increases the prediction time linearly.

Min size of the node for a split -classifier.sharkrf.nodesize int Default value: 25
If the number of samples in a node is smaller than this parameter, then the node will not be split. A reasonable value is a small percentage of the total data e.g. 1 percent.

Number of features tested at each node -classifier.sharkrf.mtry int Default value: 0
The number of features (variables) which will be tested at each node in order to compute the split. If set to zero, the square root of the number of features is used.

Out of bound ratio -classifier.sharkrf.oobr float Default value: 0.66
Set the fraction of the original training dataset to use as the out of bag sample.A good default value is 0.66.

Temporary files cleaning -cleanup bool Default value: true
If activated, the application will try to clean all temporary files it created

实例

从命令行执行以下操作：

otbcli_TrainImagesRegression -io.il inputPredictorImage.tif -io.ip inputLabelImage.tif -io.vd trainingData.shp -io.valid validationData.shp -sample.nt 500 -sample.nv 100 -io.imstat imageStats.xml -classifier rf -io.out model.txt

来自Python的评论：

import otbApplication

app = otbApplication.Registry.CreateApplication("TrainImagesRegression")

app.SetParameterStringList("io.il", ['inputPredictorImage.tif'])
app.SetParameterStringList("io.ip", ['inputLabelImage.tif'])
app.SetParameterStringList("io.vd", ['trainingData.shp'])
app.SetParameterStringList("io.valid", ['validationData.shp'])
app.SetParameterInt("sample.nt", 500)
app.SetParameterInt("sample.nv", 100)
app.SetParameterString("io.imstat", "imageStats.xml")
app.SetParameterString("classifier","rf")
app.SetParameterString("io.out", "model.txt")

app.ExecuteAndWriteOutput()