pktools
2.6.7
Processing Kernel for geospatial data
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classify vector dataset using Support Vector Machine
Usage: pksvmogr -t training [-i input -o output] [-cv value]
Options: [-tln layer]* [-c name -r value]* [-of GDALformat|-f OGRformat] [-co NAME=VALUE]* [-ct filename] [-label attribute] [-prior value]* [-g gamma] [-cc cost] [-m filename ] [-nodata value]
Advanced options: [-b band] [-sband band -eband band]* [-bal size]* [-min] [-bag value] [-bs value] [-comb rule] [-cb filename] [-pim priorimage] [–offset value] [–scale value] [-svmt type] [-kt type] [-kd value] [-c0 value] [-nu value] [-eloss value] [-cache value] [-etol value] [-shrink]
The utility pksvmogr 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 -ct.
-short
or --long
options (both --long=value
and --long value
are supported)-h
shows basic options only, long option --help
shows all options short | long | type | default | description |
---|---|---|---|---|
t | training | std::string | 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 bag and bsize options, where a random subset is taken from a single training file) | |
i | input | std::string | input vector dataset | |
o | output | std::string | Output classification vector dataset | |
cv | cv | unsigned short | 0 | N-fold cross validation mode |
cmf | cmf | std::string | ascii | Format for confusion matrix (ascii or latex) |
tln | tln | std::string | Training layer name(s) | |
c | class | std::string | List of class names. | |
r | reclass | short | List of class values (use same order as in class opt). | |
f | f | std::string | SQLite | Output ogr format for active training sample |
co | co | std::string | Creation option for output file. Multiple options can be specified. | |
ct | ct | std::string | Color table in ASCII format having 5 columns: id R G B ALFA (0: transparent, 255: solid) | |
label | label | std::string | label | Attribute name for class label in training vector file. |
prior | prior | double | 0 | Prior probabilities for each class (e.g., -p 0.3 -p 0.3 -p 0.2 ). Used for input only (ignored for cross validation) |
g | gamma | float | 1 | Gamma in kernel function |
cc | ccost | float | 1000 | The parameter C of C_SVC, epsilon_SVR, and nu_SVR |
m | mask | std::string | Only classify within specified mask (vector or raster). | |
nodata | nodata | unsigned short | 0 | Nodata value to put where vector dataset is masked as nodata |
b | band | short | Band index (starting from 0, either use band option or use start to end) | |
sband | startband | unsigned short | Start band sequence number | |
eband | endband | unsigned short | End band sequence number | |
bal | balance | unsigned int | 0 | Balance the input data to this number of samples for each class |
min | min | int | 0 | If number of training pixels is less then min, do not take this class into account (0: consider all classes) |
bag | bag | unsigned short | 1 | Number of bootstrap aggregations |
bagsize | bagsize | int | 100 | 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 |
comb | comb | unsigned short | 0 | How to combine bootstrap aggregation classifiers (0: sum rule, 1: product rule, 2: max rule). Also used to aggregate classes with rc option. |
cb | classbag | std::string | Output for each individual bootstrap aggregation | |
pim | priorimg | std::string | Prior probability image (multi-band img with band for each class | |
offset | offset | double | 0 | Offset value for each spectral band input features: refl[band]=(DN[band]-offset[band])/scale[band] |
scale | scale | double | 0 | Scale value for each spectral band input features: refl=(DN[band]-offset[band])/scaleband |
svmt | svmtype | std::string | C_SVC | Type of SVM (C_SVC, nu_SVC,one_class, epsilon_SVR, nu_SVR) |
kt | kerneltype | std::string | radial | Type of kernel function (linear,polynomial,radial,sigmoid) |
kd | kd | unsigned short | 3 | Degree in kernel function |
c0 | coef0 | float | 0 | Coef0 in kernel function |
nu | nu | float | 0.5 | The parameter nu of nu_SVC, one_class SVM, and nu_SVR |
eloss | eloss | float | 0.1 | The epsilon in loss function of epsilon_SVR |
cache | cache | int | 100 | Cache memory size in MB |
etol | etol | float | 0.001 | The tolerance of termination criterion |
shrink | shrink | bool | false | Whether to use the shrinking heuristics |
pe | probest | bool | true | Whether to train a SVC or SVR model for probability estimates |
entropy | entropy | std::string | Entropy image (measure for uncertainty of classifier output | |
active | active | std::string | Ogr output for active training sample. | |
na | nactive | unsigned int | 1 | Number of active training points |
random | random | bool | true | Randomize training data for balancing and bagging |