pksvmogr(1)

NAME

pksvmogr - classify vector dataset using Support Vector Machine

SYNOPSIS

FCpksvmogrF[0] FC-tF[0] training [FC-iF[0] input] [FC-oF[0] output] [FC-cvF[0] value] [options] [advanced options]

DESCRIPTION

pksvmogr implements a support vector machine (SVM) to solve a supervised classification problem. The implementation is based on the open source C++ library libSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm). Both raster and vector files are supported as input. The output will contain the classification result, either in raster or vector format, corresponding to the format of the input. A training sample must be provided as an OGR vector dataset that contains the class labels and the features for each training point. The point locations are not considered in the training step. You can use the same training sample for classifying different images, provided the number of bands of the images are identical. Use the utility pkextract to create a suitable training sample, based on a sample of points or polygons. For raster output maps you can attach a color table using the option FC-ctF[0].

OPTIONS

FC-tF[0] filename, FC--trainingF[0] filename

Training vector file. A single vector file contains all training features (must be set as: b0, b1, b2,...) for all classes (class numbers identified by label option). Use multiple training files for bootstrap aggregation (alternative to the FC--bagF[0] and FC--bagsizeF[0] options, where a random subset is taken from a single training file)

FC-iF[0] filename, FC--inputF[0] filename

input image

FC-oF[0] filename, FC--outputF[0] filename

Output classification image

FC-cvF[0] value, FC--cvF[0] value

N-fold cross validation mode (default: 0)

FC-tlnF[0] layer, FC--tlnF[0] layer

Training layer name(s)

FC-cF[0] name, FC--classF[0] name

List of class names.

FC-rF[0] value, FC--reclassF[0] value

List of class values (use same order as in FC--classF[0] option).

FC-ofF[0] GDALformat, FC--oformatF[0] GDALformat

Output image format (see also gdal_translate(1)).

FC-fF[0] format, FC--fF[0] format

Output ogr format for active training sample

FC-coF[0] NAME=VALUE, FC--coF[0] NAME=VALUE

Creation option for output file. Multiple options can be specified.

FC-ctF[0] filename, FC--ctF[0] filename

Color table in ASCII format having 5 columns: id R G B ALFA (0: transparent, 255: solid)

FC-labelF[0] attribute, FC--labelF[0] attribute

Identifier for class label in training vector file. (default: label)

FC-priorF[0] value, FC--priorF[0] value

Prior probabilities for each class (e.g., FC-priorF[0] 0.3 FC-priorF[0] 0.3 FC-priorF[0] 0.2) Used for input only (ignored for cross validation)

FC-gF[0] gamma, FC--gammaF[0] gamma

Gamma in kernel function

FC-ccF[0] cost, FC--ccostF[0] cost

The parameter C of C_SVC, epsilon_SVR, and nu_SVR

FC-mF[0] filename, FC--maskF[0] filename

Only classify within specified mask (vector or raster). For raster mask, set nodata values with the option FC--msknodataF[0].

FC-msknodataF[0] value, FC--msknodataF[0] value

Mask value(s) not to consider for classification. Values will be taken over in classification image.

FC-nodataF[0] value, FC--nodataF[0] value

Nodata value to put where image is masked as nodata

FC-vF[0] level, FC--verboseF[0] level

Verbose level

Advanced options

FC-bF[0] band, FC--bandF[0] band

Band index (starting from 0, either use FC--bandF[0] option or use FC--startbandF[0] to FC--endbandF[0])

FC-sbandF[0] band, FC--startbandF[0] band

Start band sequence number

FC-ebandF[0] band, FC--endbandF[0] band

End band sequence number

FC-balF[0] size, FC--balanceF[0] size

Balance the input data to this number of samples for each class

FC-minF[0] number, FC--minF[0] number

If number of training pixels is less then min, do not take this class into account (0: consider all classes)

FC-bagF[0] value, FC--bagF[0] value

Number of bootstrap aggregations (default is no bagging: 1)

FC-bagsizeF[0] value, FC--bagsizeF[0] value

Percentage of features used from available training features for each bootstrap aggregation (one size for all classes, or a different size for each class respectively

FC-combF[0] rule, FC--combF[0] rule

How to combine bootstrap aggregation classifiers (0: sum rule, 1: product rule, 2: max rule). Also used to aggregate classes with rc option.

FC-cbF[0] filename, FC--classbagF[0] filename

Output for each individual bootstrap aggregation

FC-probF[0] filename, FC--probF[0] filename

Probability image.

FC-offsetF[0] value, FC--offsetF[0] value

Offset value for each spectral band input features: FCrefl[band]=(DN[band]-offset[band])/scale[band]F[0]

FC-scaleF[0] value, FC--scaleF[0] value

Scale value for each spectral band input features: FCrefl=(DN[band]-offset[band])/scale[band]F[0] (use FC0F[0] if scale min and max in each band to FC-1.0F[0] and FC1.0F[0])

FC-svmtF[0] type, FC--svmtypeF[0] type

Type of SVM (C_SVC, nu_SVC,one_class, epsilon_SVR, nu_SVR)

FC-ktF[0] type, FC--kerneltypeF[0] type

Type of kernel function (linear,polynomial,radial,sigmoid)

FC-kdF[0] value, FC--kdF[0] value

Degree in kernel function

FC-c0F[0] value, FC--coef0F[0] value

Coef0 in kernel function

FC-nuF[0] value, FC--nuF[0] value

The parameter nu of nu-SVC, one-class SVM, and nu-SVR

FC-elossF[0] value, FC--elossF[0] value

The epsilon in loss function of epsilon-SVR

FC-cacheF[0] number, FC--cacheF[0] number

Cache ⟨http://pktools.nongnu.org/html/classCache.html⟩ memory size in MB (default: 100)

FC-etolF[0] value, FC--etolF[0] value

the tolerance of termination criterion (default: 0.001)

FC-shrinkF[0], FC--shrinkF[0]

Whether to use the shrinking heuristics

FC-naF[0] number, FC--nactiveF[0] number

Number of active training points