4.8.5 Image Classification
Performs a classification of the input image according to a model file.
Detailed description
This application performs an image classification based on a model file produced by the TrainImagesClassifier
application. Pixels of the output image will contain the class labels decided by the classifier (maximal
class label = 65535). The input pixels can be optionally centered and reduced according to the
statistics file produced by the ComputeImagesStatistics application. An optional input mask
can be provided, in which case only input image pixels whose corresponding mask value is
greater than 0 will be classified. The remaining of pixels will be given the label 0 in the output
image.
Parameters
This section describes in details the parameters available for this application. Table 4.122, page 715
presents a summary of these parameters and the parameters keys to be used in command-line and
programming languages. Application key is ImageClassifier.
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Parameter key | Parameter type |
Parameter description |
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in | Input image |
Input Image |
mask | Input image |
Input Mask |
model | Input File name |
Model file |
imstat | Input File name |
Statistics file |
out | Output image |
Output Image |
confmap | Output image |
Confidence map |
ram | Int |
Available RAM (Mb) |
inxml | XML input parameters file |
Load otb application from xml file |
outxml | XML output parameters file |
Save otb application to xml file |
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Table 4.122: Parameters table for Image Classification.
- Input Image: The input image to classify.
- Input Mask: The mask allows restricting classification of the input image to the area where
mask pixel values are greater than 0.
- Model file: A model file (produced by TrainImagesClassifier application, maximal class label
= 65535).
- Statistics file: A XML file containing mean and standard deviation to center and reduce
samples before classification (produced by ComputeImagesStatistics application).
- Output Image: Output image containing class labels
- Confidence map: Confidence map of the produced classification. The confidence index
depends on the model :
- LibSVM : difference between the two highest probabilities (needs a model with probability
estimates, so that classes probabilities can be computed for each sample)
- OpenCV
* Boost : sum of votes
* DecisionTree : (not supported)
* GradientBoostedTree : (not supported)
* KNearestNeighbors : number of neighbors with the same label
* NeuralNetwork : difference between the two highest responses
* NormalBayes : (not supported)
* RandomForest : Confidence (proportion of votes for the majority class). Margin (normalized
difference of the votes of the 2 majority classes) is not available for now.
* SVM : distance to margin (only works for 2-class models)
- Available RAM (Mb): Available memory for processing (in MB)
- Load otb application from xml file: Load otb application from xml file
- Save otb application to xml file: Save otb application to xml file
Example
To run this example in command-line, use the following:
otbcli_ImageClassifier -in QB_1_ortho.tif -imstat EstimateImageStatisticsQB1.xml -model clsvmModelQB1.svm -out clLabeledImageQB1.tif
To run this example from Python, use the following code snippet:
#!/usr/bin/python # Import the otb applications package import otbApplication # The following line creates an instance of the ImageClassifier application ImageClassifier = otbApplication.Registry.CreateApplication("ImageClassifier") # The following lines set all the application parameters: ImageClassifier.SetParameterString("in", "QB_1_ortho.tif") ImageClassifier.SetParameterString("imstat", "EstimateImageStatisticsQB1.xml") ImageClassifier.SetParameterString("model", "clsvmModelQB1.svm") ImageClassifier.SetParameterString("out", "clLabeledImageQB1.tif") # The following line execute the application ImageClassifier.ExecuteAndWriteOutput()
Limitations
The input image must have the same type, order and number of bands than the images used to produce
the statistics file and the SVM model file. If a statistics file was used during training by the
TrainImagesClassifier, it is mandatory to use the same statistics file for classification. If an input mask is
used, its size must match the input image size.
Authors
This application has been written by OTB-Team.
See also
These additional ressources can be useful for further information:
- TrainImagesClassifier, ValidateImagesClassifier, ComputeImagesStatistics