pkregann

NAME

pkregann - regression with artificial neural network (multi-layer perceptron)

SYNOPSIS

pkregann -i input -t training [-ic col] [-oc col] -o output [options] [advanced options]

DESCRIPTION

pkregann performs a regression based on an artificial neural network. The regression is trained from the input (-ic) and output (-oc) columns in a training text file. Each row in the training file represents one sampling unit. Multi-dimensional input features can be defined with multiple input options (e.g., -ic 0 -ic 1 -ic 2 for three dimensional features).

OPTIONS

-i filename, --input filename

input ASCII file

-t filename, --training filename

training ASCII file (each row represents one sampling unit. Input features should be provided as columns, followed by output)

-o filename, --output filename

output ASCII file for result

-ic col, --inputCols col

input columns (e.g., for three dimensional input data in first three columns use: -ic 0 -ic 1 -ic 2

-oc col, --outputCols col

output columns (e.g., for two dimensional output in columns 3 and 4 (starting from 0) use: -oc 3 -oc 4

-from row, --from row

start from this row in training file (start from 0)

-to row, --to row

read until this row in training file (start from 0 or set leave 0 as default to read until end of file)

-cv size, --cv size

n-fold cross validation mode

-nn number, --nneuron number

number of neurons in hidden layers in neural network (multiple hidden layers are set by defining multiple number of neurons: -n 15 -n 1, default is one hidden layer with 5 neurons)

-v level, --verbose level

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

Advanced options
--offset value

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

--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)

--connection rate

connection rate (default: 1.0 for a fully connected network)

-l rate, --learning rate

learning rate (default: 0.7)

--maxit number

number of maximum iterations (epoch) (default: 500)