OTB.ImageClassifier: Performs a classification of the input image according to a model file.

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. By default, the remaining pixels will be given the label 0 in the output image.

Inputs

The input image to classify.

format
href
Please set a value for in.

The mask restricts the classification of the input image to the area where mask pixel values are greater than 0.

format
href
Please set a value for mask.

A model file (produced by TrainImagesClassifier application, maximal class label = 65535).

format
href
Please set a value for model.

An XML file containing mean and standard deviation to center and reduce samples before classification (produced by ComputeImagesStatistics application).

format
href
Please set a value for imstat.

By default, hidden pixels will have the assigned label 0 in the output image. It is possible to define the label mask by another value, but be careful not to use a label from another class (max. 65535).

integer

Output image containing class labels

string
Please set a value for out.

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)* Boost: sum of votes* DecisionTree: (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)

string

Probability of each class for each pixel. This is an image having a number of bands equal to the number of classes in the model. This is only implemented for the Shark Random Forest classifier at this point.

string

Available memory for processing (in MB).

integer

The number of classes is required by the output of the probability map in order to set the number of output bands.

integer
Please set a value for nbclasses.

Outputs

Output image containing class labels

format
transmission

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)* Boost: sum of votes* DecisionTree: (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)

format
transmission

Probability of each class for each pixel. This is an image having a number of bands equal to the number of classes in the model. This is only implemented for the Shark Random Forest classifier at this point.

format
transmission

Execution options

successUri
inProgressUri
failedUri

format

mode

Execute End Point

View the execution endpoint of a process.

View the alternative version in HTML.

{"id": "OTB.ImageClassifier", "title": "Performs a classification of the input image according to a model file.", "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. By default, the remaining pixels will be given the label 0 in the output image.", "version": "1.0.0", "jobControlOptions": ["sync-execute", "async-execute", "dismiss"], "outputTransmission": ["value", "reference"], "links": [{"rel": "execute", "type": "application/json", "title": "Execute End Point", "href": "http://tb17.geolabs.fr:8090/ogc-api/processes/OTB.ImageClassifier/execution"}, {"rel": "alternate", "type": "text/html", "title": "Execute End Point", "href": "http://tb17.geolabs.fr:8090/ogc-api/processes/OTB.ImageClassifier/execution.html"}], "inputs": {"in": {"title": "The input image to classify.", "description": "The input image to classify.", "maxOccurs": 1, "extentded-schema": {"oneOf": [{"allOf": [{"$ref": "http://zoo-project.org/dl/link.json"}, {"type": "object", "properties": {"type": {"enum": ["image/tiff", "image/jpeg", "image/png"]}}}]}, {"type": "object", "required": ["value"], "properties": {"value": {"oneOf": [{"type": "string", "contentEncoding": "base64", "contentMediaType": "image/tiff"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/jpeg"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/png"}]}}}]}, "schema": {"oneOf": [{"type": "string", "contentEncoding": "base64", "contentMediaType": "image/tiff"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/jpeg"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/png"}]}, "id": "in"}, "mask": {"title": "The mask restricts the classification of the input image to the area where mask pixel values are greater than 0.", "description": "The mask restricts the classification of the input image to the area where mask pixel values are greater than 0.", "maxOccurs": 1, "extentded-schema": {"oneOf": [{"allOf": [{"$ref": "http://zoo-project.org/dl/link.json"}, {"type": "object", "properties": {"type": {"enum": ["image/tiff", "image/jpeg", "image/png"]}}}]}, {"type": "object", "required": ["value"], "properties": {"value": {"oneOf": [{"type": "string", "contentEncoding": "base64", "contentMediaType": "image/tiff"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/jpeg"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/png"}]}}}], "nullable": true}, "schema": {"oneOf": [{"type": "string", "contentEncoding": "base64", "contentMediaType": "image/tiff"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/jpeg"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/png"}]}, "id": "mask"}, "model": {"title": "A model file (produced by TrainImagesClassifier application, maximal class label = 65535).", "description": "A model file (produced by TrainImagesClassifier application, maximal class label = 65535).", "maxOccurs": 1, "extentded-schema": {"oneOf": [{"allOf": [{"$ref": "http://zoo-project.org/dl/link.json"}, {"type": "object", "properties": {"type": {"enum": ["image/tiff", "image/jpeg", "image/png"]}}}]}, {"type": "object", "required": ["value"], "properties": {"value": {"oneOf": [{"type": "string", "contentEncoding": "base64", "contentMediaType": "image/tiff"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/jpeg"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/png"}]}}}]}, "schema": {"oneOf": [{"type": "string", "contentEncoding": "base64", "contentMediaType": "image/tiff"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/jpeg"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/png"}]}, "id": "model"}, "imstat": {"title": "An XML file containing mean and standard deviation to center and reduce samples before classification (produced by ComputeImagesStatistics application).", "description": "An XML file containing mean and standard deviation to center and reduce samples before classification (produced by ComputeImagesStatistics application).", "maxOccurs": 1, "extentded-schema": {"oneOf": [{"allOf": [{"$ref": "http://zoo-project.org/dl/link.json"}, {"type": "object", "properties": {"type": {"enum": ["text/xml"]}}}]}, {"type": "object", "required": ["value"], "properties": {"value": {"oneOf": [{"type": "string", "contentEncoding": "utf-8", "contentMediaType": "text/xml"}]}}}], "nullable": true}, "schema": {"oneOf": [{"type": "string", "contentEncoding": "utf-8", "contentMediaType": "text/xml"}]}, "id": "imstat"}, "nodatalabel": {"title": "By default, hidden pixels will have the assigned label 0 in the output image. It is possible to define the label mask by another value, but be careful not to use a label from another class (max. 65535).", "description": "By default, hidden pixels will have the assigned label 0 in the output image. It is possible to define the label mask by another value, but be careful not to use a label from another class (max. 65535).", "maxOccurs": 1, "schema": {"type": "integer", "default": 0, "nullable": true}, "id": "nodatalabel"}, "out": {"title": "Output image containing class labels", "description": "Output image containing class labels", "maxOccurs": 1, "schema": {"type": "string", "default": "uint8", "enum": ["uint8", "uint16", "int16", "int32", "int32", "float", "double"]}, "id": "out"}, "confmap": {"title": "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)* Boost: sum of votes* DecisionTree: (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)", "description": "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)* Boost: sum of votes* DecisionTree: (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)", "maxOccurs": 1, "schema": {"type": "string", "default": "uint8", "enum": ["uint8", "uint16", "int16", "int32", "int32", "float", "double"], "nullable": true}, "id": "confmap"}, "probamap": {"title": "Probability of each class for each pixel. This is an image having a number of bands equal to the number of classes in the model. This is only implemented for the Shark Random Forest classifier at this point.", "description": "Probability of each class for each pixel. This is an image having a number of bands equal to the number of classes in the model. This is only implemented for the Shark Random Forest classifier at this point.", "maxOccurs": 1, "schema": {"type": "string", "default": "uint16", "enum": ["uint8", "uint16", "int16", "int32", "int32", "float", "double"], "nullable": true}, "id": "probamap"}, "ram": {"title": "Available memory for processing (in MB).", "description": "Available memory for processing (in MB).", "maxOccurs": 1, "schema": {"type": "integer", "default": 256, "nullable": true}, "id": "ram"}, "nbclasses": {"title": "The number of classes is required by the output of the probability map in order to set the number of output bands.", "description": "The number of classes is required by the output of the probability map in order to set the number of output bands.", "maxOccurs": 1, "schema": {"type": "integer", "default": 20}, "id": "nbclasses"}}, "outputs": {"out": {"title": "Output image containing class labels", "description": "Output image containing class labels", "extentded-schema": {"oneOf": [{"allOf": [{"$ref": "http://zoo-project.org/dl/link.json"}, {"type": "object", "properties": {"type": {"enum": ["image/tiff", "image/jpeg", "image/png"]}}}]}, {"type": "object", "required": ["value"], "properties": {"value": {"oneOf": [{"type": "string", "contentEncoding": "base64", "contentMediaType": "image/tiff"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/jpeg"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/png"}]}}}]}, "schema": {"oneOf": [{"type": "string", "contentEncoding": "base64", "contentMediaType": "image/tiff"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/jpeg"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/png"}]}, "id": "out"}, "confmap": {"title": "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)* Boost: sum of votes* DecisionTree: (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)", "description": "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)* Boost: sum of votes* DecisionTree: (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)", "extentded-schema": {"oneOf": [{"allOf": [{"$ref": "http://zoo-project.org/dl/link.json"}, {"type": "object", "properties": {"type": {"enum": ["image/tiff", "image/jpeg", "image/png"]}}}]}, {"type": "object", "required": ["value"], "properties": {"value": {"oneOf": [{"type": "string", "contentEncoding": "base64", "contentMediaType": "image/tiff"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/jpeg"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/png"}]}}}]}, "schema": {"oneOf": [{"type": "string", "contentEncoding": "base64", "contentMediaType": "image/tiff"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/jpeg"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/png"}]}, "id": "confmap"}, "probamap": {"title": "Probability of each class for each pixel. This is an image having a number of bands equal to the number of classes in the model. This is only implemented for the Shark Random Forest classifier at this point.", "description": "Probability of each class for each pixel. This is an image having a number of bands equal to the number of classes in the model. This is only implemented for the Shark Random Forest classifier at this point.", "extentded-schema": {"oneOf": [{"allOf": [{"$ref": "http://zoo-project.org/dl/link.json"}, {"type": "object", "properties": {"type": {"enum": ["image/tiff", "image/jpeg", "image/png"]}}}]}, {"type": "object", "required": ["value"], "properties": {"value": {"oneOf": [{"type": "string", "contentEncoding": "base64", "contentMediaType": "image/tiff"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/jpeg"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/png"}]}}}]}, "schema": {"oneOf": [{"type": "string", "contentEncoding": "base64", "contentMediaType": "image/tiff"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/jpeg"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/png"}]}, "id": "probamap"}}}

http://tb17.geolabs.fr:8090/ogc-api/processes/OTB.ImageClassifier.html
Last modified: Wed Jun 9 17:39:32 CEST 2021