pkregann1

Usage

Overview

pkregann performs a regression based on an artificial neural network. The regression is trained from the input (FC-icF[0]) and output (FC-ocF[0]) 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., FC-icF[0] 0 FC-icF[0] 1 FC-icF[0] 2 for three dimensional features).

Options

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

input ASCII file

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

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

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

output ASCII file for result

FC-icF[0] col, FC--inputColsF[0] col

input columns (e.g., for three dimensional input data in first three columns use: FC-icF[0] 0 FC-icF[0] 1 FC-icF[0] 2

FC-ocF[0] col, FC--outputColsF[0] col

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

FC-fromF[0] row, FC--fromF[0] row

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

FC-toF[0] row, FC--toF[0] row

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

FC-cvF[0] size, FC--cvF[0] size

n-fold cross validation mode

FC-nnF[0] number, FC--nneuronF[0] number

number of neurons in hidden layers in neural network (multiple hidden layers are set by defining multiple number of neurons: FC-nF[0] 15 FC-nF[0] 1, default is one hidden layer with 5 neurons)

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

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

Advanced options

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

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--connectionF[0] rate

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

FC-lF[0] rate, FC--learningF[0] rate

learning rate (default: 0.7)

FC--maxitF[0] number

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