SAGA.imagery_opencv.8: Decision Tree Classification (OpenCV)

Integration of the OpenCV Machine Learning library for Decision Tree classification of gridded features.

Inputs

Features

format
href
Please set a value for FEATURES.

Normalize

boolean

Use the first three features in list to obtain blue, green, red components for class colour in look-up table.

boolean

Use a model previously stored to file.

format
href
Please set a value for MODEL_LOAD.

Training Areas

format
href
Please set a value for TRAIN_AREAS.

Class Identifier

format
href
Please set a value for TRAIN_CLASS.

For non-polygon type training areas, creates a buffer with a diameter of specified size.

number

Stores model to file to be used for subsequent classifications instead of training areas.

format
href
Please set a value for MODEL_SAVE.

The maximum possible depth of the tree. That is the training algorithms attempts to split a node while its depth is less than maxDepth. The root node has zero depth.

integer

If the number of samples in a node is less than this parameter then the node will not be split.

integer

Cluster possible values of a categorical variable into K<=maxCategories clusters to find a suboptimal split.

integer

If true then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate.

boolean

If true then pruned branches are physically removed from the tree. Otherwise they are retained and it is possible to get results from the original unpruned (or pruned less aggressively) tree.

boolean

Termination criteria for regression trees. If all absolute differences between an estimated value in a node and values of train samples in this node are less than this parameter then the node will not be split further.

number

Outputs

Classification

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.

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"Normalize", "schema": {"type": "boolean", "default": false, "enum": ["true", "false"], "nullable": true}, "id": "NORMALIZE"}, "RGB_COLORS": {"title": "Update Colors from Features", "description": "Use the first three features in list to obtain blue, green, red components for class colour in look-up table.", "schema": {"type": "boolean", "default": false, "enum": ["true", "false"], "nullable": true}, "id": "RGB_COLORS"}, "MODEL_LOAD": {"title": "Load Model", "description": "Use a model previously stored to file.", "extended-schema": {"oneOf": [{"allOf": [{"$ref": "http://zoo-project.org/dl/link.json"}, {"type": "object", "properties": {"type": {"enum": ["text/plain"]}}}]}, {"type": "object", "required": ["value"], "properties": {"value": {"oneOf": [{"type": "string", "contentEncoding": "utf-8", "contentMediaType": "text/plain"}]}}}], "nullable": true}, "schema": {"oneOf": [{"type": "string", "contentEncoding": "utf-8", "contentMediaType": "text/plain"}]}, "id": "MODEL_LOAD"}, "TRAIN_AREAS": {"title": "Training Areas", "description": "Training Areas", "extended-schema": {"oneOf": [{"allOf": [{"$ref": "http://zoo-project.org/dl/link.json"}, {"type": "object", "properties": {"type": {"enum": ["text/xml", "application/vnd.google-earth.kml+xml", "application/json"]}}}]}, {"type": "object", "required": ["value"], "properties": {"value": {"oneOf": [{"type": "string", "contentEncoding": "utf-8", "contentMediaType": "text/xml"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "application/vnd.google-earth.kml+xml"}, {"type": "object"}]}}}]}, "schema": {"oneOf": [{"type": "string", "contentEncoding": "utf-8", "contentMediaType": "text/xml"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "application/vnd.google-earth.kml+xml"}, {"type": "object"}]}, "id": "TRAIN_AREAS"}, "TRAIN_CLASS": {"title": "Class Identifier", "description": "Class Identifier", "extended-schema": {"oneOf": [{"allOf": [{"$ref": "http://zoo-project.org/dl/link.json"}, {"type": "object", "properties": {"type": {"enum": ["text/csv", "text/csv"]}}}]}, {"type": "object", "required": ["value"], "properties": {"value": {"oneOf": [{"type": "string", "contentEncoding": "utf-8", "contentMediaType": "text/csv"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "text/csv"}]}}}], "nullable": true}, "schema": {"oneOf": [{"type": "string", "contentEncoding": "utf-8", "contentMediaType": "text/csv"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "text/csv"}]}, "id": "TRAIN_CLASS"}, "TRAIN_BUFFER": {"title": "Buffer Size", "description": "For non-polygon type training areas, creates a buffer with a diameter of specified size.", "schema": {"type": "number", "default": 1, "format": "double", "nullable": true}, "id": "TRAIN_BUFFER"}, "MODEL_SAVE": {"title": "Save Model", "description": "Stores model to file to be used for subsequent classifications instead of training areas.", "extended-schema": {"oneOf": [{"allOf": [{"$ref": "http://zoo-project.org/dl/link.json"}, {"type": "object", "properties": {"type": {"enum": ["text/plain"]}}}]}, {"type": "object", "required": ["value"], "properties": {"value": {"oneOf": [{"type": "string", "contentEncoding": "utf-8", "contentMediaType": "text/plain"}]}}}], "nullable": true}, "schema": {"oneOf": [{"type": "string", "contentEncoding": "utf-8", "contentMediaType": "text/plain"}]}, "id": "MODEL_SAVE"}, "MAX_DEPTH": {"title": "Maximum Tree Depth", "description": "The maximum possible depth of the tree. That is the training algorithms attempts to split a node while its depth is less than maxDepth. The root node has zero depth.", "schema": {"type": "integer", "default": 10, "nullable": true}, "id": "MAX_DEPTH"}, "MIN_SAMPLES": {"title": "Minimum Sample Count", "description": "If the number of samples in a node is less than this parameter then the node will not be split.", "schema": {"type": "integer", "default": 2, "nullable": true}, "id": "MIN_SAMPLES"}, "MAX_CATEGRS": {"title": "Maximum Categories", "description": "Cluster possible values of a categorical variable into K<=maxCategories clusters to find a suboptimal split.", "schema": {"type": "integer", "default": 10, "nullable": true}, "id": "MAX_CATEGRS"}, "1SE_RULE": {"title": "Use 1SE Rule", "description": "If true then a pruning will be harsher. This will make a tree more compact and more resistant to the training data noise but a bit less accurate.", "schema": {"type": "boolean", "default": false, "enum": ["true", "false"], "nullable": true}, "id": "1SE_RULE"}, "TRUNC_PRUNED": {"title": "Truncate Pruned Trees", "description": "If true then pruned branches are physically removed from the tree. Otherwise they are retained and it is possible to get results from the original unpruned (or pruned less aggressively) tree.", "schema": {"type": "boolean", "default": false, "enum": ["true", "false"], "nullable": true}, "id": "TRUNC_PRUNED"}, "REG_ACCURACY": {"title": "Regression Accuracy", "description": "Termination criteria for regression trees. If all absolute differences between an estimated value in a node and values of train samples in this node are less than this parameter then the node will not be split further.", "schema": {"type": "number", "default": 0.01, "format": "double", "nullable": true}, "id": "REG_ACCURACY"}}, "outputs": {"CLASSES": {"title": "Classification", "description": "Classification", "extended-schema": {"oneOf": [{"allOf": [{"$ref": "http://zoo-project.org/dl/link.json"}, {"type": "object", "properties": {"type": {"enum": ["image/tiff", "application/x-ogc-envi", "application/x-ogc-aaigrid", "image/png"]}}}]}, {"type": "object", "required": ["value"], "properties": {"value": {"oneOf": [{"type": "string", "contentEncoding": "base64", "contentMediaType": "image/tiff"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "application/x-ogc-envi"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "application/x-ogc-aaigrid"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/png"}]}}}]}, "schema": {"oneOf": [{"type": "string", "contentEncoding": "base64", "contentMediaType": "image/tiff"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "application/x-ogc-envi"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "application/x-ogc-aaigrid"}, {"type": "string", "contentEncoding": "base64", "contentMediaType": "image/png"}]}, "id": "CLASSES"}}}

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Last modified: Sat Feb 19 15:43:34 CET 2022