pkannogr(1)

NAME
pkannogr - classify vector dataset using Artificial Neural Network

SYNOPSIS
\FCpkannogr\F[]
\FC-t\F[] training [\FC-i\F[] input] [\FC-o\F[] output] [\FC-cv\F[] value] [options] [advanced options]

DESCRIPTION
pkannogr implements an artificial neural network (ANN) to solve a supervised classification problem. The implementation is based on the open source C++ library ( ). 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(1) 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-ct\F[].

OPTIONS

\FC-i\F[] filename, \FC--input\F[] filename
    input image

\FC-t\F[] filename, \FC--training\F[] 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--bag\F[] and \FC--bsize\F[] options, where a random subset is taken from a single training file)

\FC-tln\F[] layer, \FC--tln\F[] layer
    training layer name(s)

\FC-label\F[] attribute, \FC--label\F[] attribute
    identifier for class label in training vector file. (default: label)

\FC-prior\F[] value, \FC--prior\F[] value
    prior probabilities for each class (e.g., \FC-prior\F[] 0.3 \FC-prior\F[] 0.3 \FC-prior\F[] 0.2 )

\FC-cv\F[] value, \FC--cv\F[] value
    n-fold cross validation mode (default: 0)

\FC-nn\F[] number, \FC--nneuron\F[] number
    number of neurons in hidden layers in neural network (multiple hidden layers are set by defining multiple number of neurons: \FC-nn\F[] 15 \FC-nn\F[] 1, default is one hidden layer with 5 neurons)

\FC-m\F[] filename, \FC--mask\F[] filename
    Only classify within specified mask (vector or raster). For raster mask, set nodata values with the option \FC--msknodata\F[].

\FC-msknodata\F[] value, \FC--msknodata\F[] value
    mask value(s) not to consider for classification. Values will be taken over in classification image. Default is 0.

\FC-nodata\F[] value, \FC--nodata\F[] value
    nodata value to put where image is masked as nodata (default: 0)

\FC-o\F[] filename, \FC--output\F[] filename
    output classification image

\FC-ot\F[] type, \FC--otype\F[] type
    Data type for output image ({Byte / Int16 / UInt16 / UInt32 / Int32 / Float32 / Float64 / CInt16 / CInt32 / CFloat32 / CFloat64}). Empty string: inherit type from input image

\FC-of\F[] GDALformat, \FC--oformat\F[] GDALformat
    Output image format (see also gdal_translate(1)). Empty string: inherit from input image

\FC-f\F[] OGRformat, \FC--f\F[] OGRformat
    Output ogr format for active training sample (default: SQLite)

\FC-ct\F[] filename, \FC--ct\F[] filename
    colour table in ASCII format having 5 columns: id R G B ALFA (0: transparent, 255: solid)

\FC-co\F[] NAME=VALUE, \FC--co\F[] NAME=VALUE
    Creation option for output file. Multiple options can be specified.

\FC-c\F[] name, \FC--class\F[] name
    list of class names.

\FC-r\F[] value, \FC--reclass\F[] value
    list of class values (use same order as in \FC--class\F[] option).

\FC-v\F[] 0|1|2, \FC--verbose\F[] 0|1|2
    set to: 0 (results only), 1 (confusion matrix), 2 (debug)

Advanced options

\FC-bal\F[] size, \FC--balance\F[] size
    balance the input data to this number of samples for each class (default: 0)

\FC-min\F[] number, \FC--min\F[] number
    if number of training pixels is less then min, do not take this class into account (0: consider all classes)

\FC-b\F[] band, \FC--band\F[] band
    band index (starting from 0, either use \FC--band\F[] option or use \FC--start\F[] to \FC--end\F[])

\FC-sband\F[] band, \FC--startband\F[] band
    start band sequence number (default: 0)

\FC-eband\F[] band, \FC--endband\F[] band
    end band sequence number

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

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

\FC-a\F[] 1|2, \FC--aggreg\F[] 1|2
    how to combine aggregated classifiers, see also \FC--rc\F[] option (1: sum rule, 2: max rule).

\FC--connection\F[] 0|1
    connection rate (default: 1.0 for a fully connected network)

\FC-w\F[] weights, \FC--weights\F[] weights
    weights for neural network. Apply to fully connected network only, starting from first input neuron to last output neuron, including the bias neurons (last neuron in each but last layer)

\FC-l\F[] rate, \FC--learning\F[] rate
    learning rate (default: 0.7)

\FC--maxit\F[] number
    number of maximum iterations (epoch) (default: 500)

\FC-comb\F[] rule, \FC--comb\F[] rule
    how to combine bootstrap aggregation classifiers (0: sum rule, 1: product rule, 2: max rule). Also used to aggregate classes with \FC--rc\F[] option. Default is sum rule (0)

\FC-bag\F[] value, \FC--bag\F[] value
    Number of bootstrap aggregations (default is no bagging: 1)

\FC-bs\F[] value, \FC--bsize\F[] 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. default: 100)

\FC-cb\F[] filename, \FC--classbag\F[] filename
    output for each individual bootstrap aggregation (default is blank)

\FC--prob\F[] filename
    probability image. Default is no probability image

\FC-na\F[] number, \FC--na\F[] number
    number of active training points (default: 1)