SYNOPSIS

pkfssvm -t training -n number [options] [advanced options]

DESCRIPTION

Classification problems dealing with high dimensional input data can be challenging due to the Hughes phenomenon. Hyperspectral data, for instance, can have hundreds of spectral bands and require special attention when being classified. In particular when limited training data are available, the classification of such data can be problematic without reducing the dimension.

The SVM classifier has been shown to be more robust to this type of problem than others. Nevertheless, classification accuracy can often be improved with feature selection methods. The utility pkfssvm implements a number of feature selection techniques, among which a sequential floating forward search (SFFS).

OPTIONS

-t filename, --training 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 bag and bsize options, where a random subset is taken from a single training file)

-n number, --nf number

number of features to select (0 to select optimal number, see also --ecost option)

-i filename, --input filename

input test set (leave empty to perform a cross validation based on training only)

-v level, --verbose level

set to: 0 (results only), 1 (confusion matrix), 2 (debug)

Advanced options

-tln layer, --tln layer

training layer name(s)

-label attribute, --label attribute

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

-bal size, --balance size

balance the input data to this number of samples for each class (default: 0)

-random, --random

in case of balance, randomize input data

-min number, --min number

if number of training pixels is less then min, do not take this class into account

-b band, --band band

band index (starting from 0, either use band option or use start to end)

-sband band, --startband band

start band sequence number

-eband band, --endband band

end band sequence number

-offset value, --offset value

offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band]

-scale value, --scale value

scale value for each spectral band input features: refl=(DN[band]-offset[band])/scale[band] (use 0 if scale min and max in each band to -1.0 and 1.0)

-svmt type, --svmtype type

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

-kt type, --kerneltype type

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

-kd value, --kd value

degree in kernel function

-g value, --gamma value

gamma in kernel function

-c0 value, --coef0 value

coef0 in kernel function

-cc value, --ccost value

the parameter C of C-SVC, epsilon-SVR, and nu-SVR

-nu value, --nu value

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

-eloss value, --eloss value

the epsilon in loss function of epsilon-SVR

-cache number, --cache number

cache memory size in MB (default: 100)

-etol value, --etol value

the tolerance of termination criterion (default: 0.001)

-shrink, --shrink

whether to use the shrinking heuristics

-sm method, --sm method

feature selection method (sffs=sequential floating forward search, sfs=sequential forward search, sbs, sequential backward search, bfs=brute force search)

-ecost value, --ecost value

epsilon for stopping criterion in cost function to determine optimal number of features

-cv value, --cv value

n-fold cross validation mode (default: 0)

-c name, --class name

list of class names.

-r value, --reclass value

list of class values (use same order as in --class option).