Provided by: pktools_2.6.6-1_amd64 
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)
24 January 2016 pkregann(1)