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commit 49ece86c32e613fb032970b8a46b8624087893be
Author: Bas Couwenberg <sebas...@xs4all.nl>
Date: Fri Dec 12 14:58:15 2014 +0100
Add man page for pksvm.
---
debian/changelog | 2 +-
debian/man/pksvm.1.xml | 560 +++++++++++++++++++++++++++++++++++++++++++++++++
2 files changed, 561 insertions(+), 1 deletion(-)
diff --git a/debian/changelog b/debian/changelog
index ab5ad98..33729c5 100644
--- a/debian/changelog
+++ b/debian/changelog
@@ -7,7 +7,7 @@ pktools (2.6.1-1) UNRELEASED; urgency=medium
pkcrop, pkdiff, pkdsm2shadow, pkdumpimg, pkdumpogr, pkegcs, pkextract,
pkfillnodata, pkfilter, pkfilterascii, pkfilterdem, pkfsann, pkfssvm,
pkgetmask, pkinfo, pklas2img, pkoptsvm, pkpolygonize, pkregann, pksetmask,
- pksieve, pkstatascii, pkstatogr.
+ pksieve, pkstatascii, pkstatogr & pksvm.
-- Bas Couwenberg <sebas...@xs4all.nl> Wed, 03 Dec 2014 21:16:31 +0100
diff --git a/debian/man/pksvm.1.xml b/debian/man/pksvm.1.xml
new file mode 100644
index 0000000..958a009
--- /dev/null
+++ b/debian/man/pksvm.1.xml
@@ -0,0 +1,560 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<!DOCTYPE refentry PUBLIC "-//OASIS//DTD DocBook XML V4.4//EN"
"http://www.oasis-open.org/docbook/xml/4.4/docbookx.dtd";>
+<refentry id='pksvm'>
+
+ <refmeta>
+ <refentrytitle>pksvm</refentrytitle>
+ <manvolnum>1</manvolnum>
+ </refmeta>
+
+ <refnamediv>
+ <refname>pksvm</refname>
+ <refpurpose>classify raster image using Support Vector Machine</refpurpose>
+ </refnamediv>
+
+ <refsynopsisdiv id='synopsis'>
+ <cmdsynopsis>
+ <command>pksvm</command>
+ <arg choice='plain'><option>-t</option>
<replaceable>training</replaceable></arg>
+ <arg choice='opt'><option>-i</option>
<replaceable>input</replaceable></arg>
+ <arg choice='opt'><option>-o</option>
<replaceable>output</replaceable></arg>
+ <arg choice='opt'><option>-cv</option>
<replaceable>value</replaceable></arg>
+ <arg choice='opt'><replaceable>options</replaceable></arg>
+ <arg choice='opt'><replaceable>advanced options</replaceable></arg>
+ </cmdsynopsis>
+ </refsynopsisdiv>
+
+ <refsect1 id='description'>
+ <title>DESCRIPTION</title>
+ <para>
+ <command>pksvm</command> implements a support vector machine (SVM) to
+ solve a supervised classification problem.
+ The implementation is based on the open source C++ library libSVM
+ (http://www.csie.ntu.edu.tw/~cjlin/libsvm).
+ 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 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
+ <option>-ct</option>.
+ </para>
+ </refsect1>
+
+ <refsect1 id='options'>
+ <title>OPTIONS</title>
+ <variablelist>
+
+ <varlistentry>
+ <term><option>-t</option> <replaceable>filename</replaceable></term>
+ <term><option>--training</option>
<replaceable>filename</replaceable></term>
+ <listitem>
+ <para>
+ 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 <option>--bag</option> and
+ <option>--bagsize</option> options, where a random subset
+ is taken from a single training file)
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-i</option> <replaceable>filename</replaceable></term>
+ <term><option>--input</option>
<replaceable>filename</replaceable></term>
+ <listitem>
+ <para>
+ input image
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-o</option> <replaceable>filename</replaceable></term>
+ <term><option>--output</option>
<replaceable>filename</replaceable></term>
+ <listitem>
+ <para>
+ Output classification image
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-cv</option> <replaceable>value</replaceable></term>
+ <term><option>--cv</option> <replaceable>value</replaceable></term>
+ <listitem>
+ <para>
+ N-fold cross validation mode (default: 0)
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-tln</option> <replaceable>layer</replaceable></term>
+ <term><option>--tln</option> <replaceable>layer</replaceable></term>
+ <listitem>
+ <para>
+ Training layer name(s)
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-c</option> <replaceable>name</replaceable></term>
+ <term><option>--class</option> <replaceable>name</replaceable></term>
+ <listitem>
+ <para>
+ List of class names.
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-r</option> <replaceable>value</replaceable></term>
+ <term><option>--reclass</option>
<replaceable>value</replaceable></term>
+ <listitem>
+ <para>
+ List of class values (use same order as in
+ <option>--class</option> option).
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-of</option> <replaceable>GDALformat</replaceable></term>
+ <term><option>--oformat</option>
<replaceable>GDALformat</replaceable></term>
+ <listitem>
+ <para>
+ Output image format (see also
+ <citerefentry>
+ <refentrytitle>gdal_translate</refentrytitle>
+ <manvolnum>1</manvolnum>
+ </citerefentry>).
+ Empty string: inherit from input image
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-f</option> <replaceable>format</replaceable></term>
+ <term><option>--f</option> <replaceable>format</replaceable></term>
+ <listitem>
+ <para>
+ Output ogr format for active training sample
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-co</option> <replaceable>NAME=VALUE</replaceable></term>
+ <term><option>--co</option>
<replaceable>NAME=VALUE</replaceable></term>
+ <listitem>
+ <para>
+ Creation option for output file.
+ Multiple options can be specified.
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-ct</option> <replaceable>filename</replaceable></term>
+ <term><option>--ct</option> <replaceable>filename</replaceable></term>
+ <listitem>
+ <para>
+ Color table in ASCII format having 5 columns:
+ id R G B ALFA (0: transparent, 255: solid)
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-label</option>
<replaceable>attribute</replaceable></term>
+ <term><option>--label</option>
<replaceable>attribute</replaceable></term>
+ <listitem>
+ <para>
+ Identifier for class label in training vector file.
+ (default: label)
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-prior</option> <replaceable>value</replaceable></term>
+ <term><option>--prior</option> <replaceable>value</replaceable></term>
+ <listitem>
+ <para>
+ Prior probabilities for each class (e.g.,
+ <option>-prior</option> 0.3 <option>-prior</option> 0.3
+ <option>-prior</option> 0.2)
+ Used for input only (ignored for cross validation)
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-g</option> <replaceable>gamma</replaceable></term>
+ <term><option>--gamma</option> <replaceable>gamma</replaceable></term>
+ <listitem>
+ <para>
+ Gamma in kernel function
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-cc</option> <replaceable>cost</replaceable></term>
+ <term><option>--ccost</option> <replaceable>cost</replaceable></term>
+ <listitem>
+ <para>
+ The parameter C of C_SVC, epsilon_SVR, and nu_SVR
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-m</option> <replaceable>filename</replaceable></term>
+ <term><option>--mask</option>
<replaceable>filename</replaceable></term>
+ <listitem>
+ <para>
+ Use the first band of the specified file as a validity mask.
+ Nodata values can be set with the option
+ <option>--msknodata</option>.
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-msknodata</option>
<replaceable>value</replaceable></term>
+ <term><option>--msknodata</option>
<replaceable>value</replaceable></term>
+ <listitem>
+ <para>
+ Mask value(s) not to consider for classification (use negative
+ values if only these values should be taken into account).
+ Values will be taken over in classification image.
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-nodata</option> <replaceable>value</replaceable></term>
+ <term><option>--nodata</option> <replaceable>value</replaceable></term>
+ <listitem>
+ <para>
+ Nodata value to put where image is masked as nodata
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-v</option> <replaceable>level</replaceable></term>
+ <term><option>--verbose</option>
<replaceable>level</replaceable></term>
+ <listitem>
+ <para>
+ Verbose level
+ </para>
+ </listitem>
+ </varlistentry>
+
+ </variablelist>
+
+ <para>Advanced options</para>
+ <variablelist>
+
+ <varlistentry>
+ <term><option>-b</option> <replaceable>band</replaceable></term>
+ <term><option>--band</option> <replaceable>band</replaceable></term>
+ <listitem>
+ <para>
+ Band index (starting from 0, either use <option>--band</option>
+ option or use <option>--start</option> to <option>--end</option>)
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-s</option> <replaceable>band</replaceable></term>
+ <term><option>--start</option> <replaceable>band</replaceable></term>
+ <listitem>
+ <para>
+ Start band sequence number
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-e</option> <replaceable>band</replaceable></term>
+ <term><option>--end</option> <replaceable>band</replaceable></term>
+ <listitem>
+ <para>
+ End band sequence number (set to 0 to include all bands)
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-bal</option> <replaceable>size</replaceable></term>
+ <term><option>--balance</option> <replaceable>size</replaceable></term>
+ <listitem>
+ <para>
+ Balance the input data to this number of samples for each class
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-min</option> <replaceable>number</replaceable></term>
+ <term><option>--min</option> <replaceable>number</replaceable></term>
+ <listitem>
+ <para>
+ If number of training pixels is less then min, do not take this
+ class into account (0: consider all classes)
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-bag</option> <replaceable>value</replaceable></term>
+ <term><option>--bag</option> <replaceable>value</replaceable></term>
+ <listitem>
+ <para>
+ Number of bootstrap aggregations (default is no bagging: 1)
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-bagsize</option> <replaceable>value</replaceable></term>
+ <term><option>--bagsize</option>
<replaceable>value</replaceable></term>
+ <listitem>
+ <para>
+ 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
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-comb</option> <replaceable>rule</replaceable></term>
+ <term><option>--comb</option> <replaceable>rule</replaceable></term>
+ <listitem>
+ <para>
+ How to combine bootstrap aggregation classifiers
+ (0: sum rule, 1: product rule, 2: max rule).
+ Also used to aggregate classes with rc option.
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-cb</option> <replaceable>filename</replaceable></term>
+ <term><option>--classbag</option>
<replaceable>filename</replaceable></term>
+ <listitem>
+ <para>
+ Output for each individual bootstrap aggregation
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-prob</option> <replaceable>filename</replaceable></term>
+ <term><option>--prob</option>
<replaceable>filename</replaceable></term>
+ <listitem>
+ <para>
+ Probability image.
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>--offset</option> <replaceable>value</replaceable></term>
+ <listitem>
+ <para>
+ Offset value for each spectral band input features:
+ refl[band]=(DN[band]-offset[band])/scale[band]
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>--scale</option> <replaceable>value</replaceable></term>
+ <listitem>
+ <para>
+ 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)
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-svmt</option> <replaceable>type</replaceable></term>
+ <term><option>--svmtype</option> <replaceable>type</replaceable></term>
+ <listitem>
+ <para>
+ Type of SVM (C_SVC, nu_SVC,one_class, epsilon_SVR, nu_SVR)
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-kt</option> <replaceable>type</replaceable></term>
+ <term><option>--kerneltype</option>
<replaceable>type</replaceable></term>
+ <listitem>
+ <para>
+ Type of kernel function (linear,polynomial,radial,sigmoid)
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-kd</option> <replaceable>value</replaceable></term>
+ <term><option>--kd</option> <replaceable>value</replaceable></term>
+ <listitem>
+ <para>
+ Degree in kernel function
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-c0</option> <replaceable>value</replaceable></term>
+ <term><option>--coef0</option> <replaceable>value</replaceable></term>
+ <listitem>
+ <para>
+ Coef0 in kernel function
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-nu</option> <replaceable>value</replaceable></term>
+ <term><option>--nu</option> <replaceable>value</replaceable></term>
+ <listitem>
+ <para>
+ The parameter nu of nu-SVC, one-class SVM, and nu-SVR
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-eloss</option> <replaceable>value</replaceable></term>
+ <term><option>--eloss</option> <replaceable>value</replaceable></term>
+ <listitem>
+ <para>
+ The epsilon in loss function of epsilon-SVR
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-cache</option> <replaceable>number</replaceable></term>
+ <term><option>--cache</option> <replaceable>number</replaceable></term>
+ <listitem>
+ <para>
+ <ulink
url="http://pktools.nongnu.org/html/classCache.html";>Cache</ulink>
+ memory size in MB
+ (default: 100)
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-etol</option> <replaceable>value</replaceable></term>
+ <term><option>--etol</option> <replaceable>value</replaceable></term>
+ <listitem>
+ <para>
+ the tolerance of termination criterion
+ (default: 0.001)
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-shrink</option></term>
+ <term><option>--shrink</option></term>
+ <listitem>
+ <para>
+ Whether to use the shrinking heuristics
+ </para>
+ </listitem>
+ </varlistentry>
+
+ <varlistentry>
+ <term><option>-na</option> <replaceable>number</replaceable></term>
+ <term><option>--nactive</option>
<replaceable>number</replaceable></term>
+ <listitem>
+ <para>
+ Number of active training points
+ </para>
+ </listitem>
+ </varlistentry>
+
+ </variablelist>
+
+ </refsect1>
+
+ <refsect1 id='example'>
+ <title>EXAMPLE</title>
+
+ <example>
+ <para>
+ Classify input image input.tif with a support vector machine.
+ A training sample that is provided as an OGR vector dataset.
+ It contains all features (same dimensionality as input.tif) in its
+ fields (please check
+ <citerefentry>
+ <refentrytitle>pkextract</refentrytitle>
+ <manvolnum>1</manvolnum>
+ </citerefentry>
+ on how to obtain such a file from a "clean" vector file containing
+ locations only).
+ A two-fold cross validation (cv) is performed (output on screen).
+ The parameters cost and gamma of the support vector machine are set
+ to 1000 and 0.1 respectively.
+ A colourtable (a five column text file: image value, RED, GREEN,
+ BLUE, ALPHA) has also been provided.
+ </para>
+
+ <screen>
+<command>pksvm</command> <option>-i</option>
<replaceable>input.tif</replaceable> <option>-t</option>
<replaceable>training.sqlite</replaceable> <option>-o</option>
<replaceable>output.tif</replaceable> <option>-cv</option>
<replaceable>2</replaceable> <option>-ct</option>
<replaceable>colourtable.txt</replaceable> <option>-cc</option>
<replaceable>1000</replaceable> <option>-g</option>
<replaceable>0.1</replaceable>
+ </screen>
+ </example>
+
+ <example>
+ <para>
+ Classification using bootstrap aggregation.
+ The training sample is randomly split in three subsamples
+ (33% of the original sample each).
+ </para>
+
+ <screen>
+<command>pksvm</command> <option>-i</option>
<replaceable>input.tif</replaceable> <option>-t</option>
<replaceable>training.sqlite</replaceable> <option>-o</option>
<replaceable>output.tif</replaceable> <option>-bs</option>
<replaceable>33</replaceable> <option>-bag</option> <replaceable>3</replaceable>
+ </screen>
+ </example>
+
+ <example>
+ <para>
+ Classification using prior probabilities for each class.
+ The priors are automatically normalized.
+ The order in which the options <option>-p</option> are provide should
+ respect the alphanumeric order of the class names (class 10 comes
+ before 2...)
+ </para>
+
+ <screen>
+<command>pksvm</command> <option>-i</option>
<replaceable>input.tif</replaceable> <option>-t</option>
<replaceable>training.sqlite</replaceable> <option>-o</option>
<replaceable>output.tif</replaceable> <option>-p</option>
<replaceable>1</replaceable> <option>-p</option> <replaceable>1</replaceable>
<option>-p</option> <replaceable>1</replaceable> <option>-p</option>
<replaceable>1</replaceable> <option>-p</option> <replaceable>1</replaceable>
<option>-p</option> <replaceable>1</replacea [...]
+ </screen>
+ </example>
+
+ </refsect1>
+
+</refentry>
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