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)