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| [[interimage:|InterIMAGE wiki]] | [[interimage:|InterIMAGE wiki]] | ||
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| ====== TerraAIDA Operators Documentation ====== | ====== TerraAIDA Operators Documentation ====== | ||
| + | Operators reference version: 1.0.5 | ||
| + | |||
| + | InterIMAGE is a multi-platform framework, written in C++, currently with implementations for LINUX and Windows operational systems. Moreover, InterIMAGE provides support for the integration of image processing operators in the interpretation process and, as such operators are treated as external programs by its control mechanism, they can be coded in any computer language, and even in proprietary programs. The InterIMAGE framework offers, nonetheless, a repository of operators, called TerraAIDA ([[http://www.dpi.inpe.br/terraaida]]), assembled with software classes and functions supplied by the [[http://www.terralib.org/|TerraLib]] library. | ||
| ===== TerraAIDA Arithmetic ===== | ===== TerraAIDA Arithmetic ===== | ||
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| **Description** | **Description** | ||
| - | Generate segments based on the threshold image resultant from an arithmetic operation from the given input images. | + | Generate segments based on the threshold image. It is the result of an arithmetic operation from the given input images. |
| **Type** | **Type** | ||
| Line 25: | Line 27: | ||
| **Multi-Class support** | **Multi-Class support** | ||
| - | Yes. If you select //true// in the "Top Down Multi-Class" option, you must create another node in the same level with a corresponding opposite name, with prefix "n-". For example, if this class name is "MyClass" the opposite class name will be "n-MyClass". Hypothesis discarded by this operator will be classified as "n-MyClass". | + | Yes. If you select //true// in the "Top Down Multi-Class" option, you must create another node in the same level with a corresponding opposite name, with prefix "n-". For example, if this class name is "MyClass" the opposite class name will be "n-MyClass". Hypothesis discarded by this operator will be classified as "n-MyClass". Alternatively, you can define a new node name in the input parameter named **Non Class Name**. |
| [[interimage:example_supervised_c45|Example of using Multi-Class support.]] | [[interimage:example_supervised_c45|Example of using Multi-Class support.]] | ||
| Line 49: | Line 51: | ||
| | Arithmetic Expression | String | One valid arithmetic expression, formed by a combination of the following elements, separated by spaces. | Operators: +, -, *, /, Real Numbers, Raster indexes and bands R0:1, R1:2 | Example: Quickbird-2 image with bands (0=B,1=G,2=R,3=NIR), the NDVI is calculated by ( R0:3 - R0:2 ) / ( R0:3 + R0:2 ). | | | Arithmetic Expression | String | One valid arithmetic expression, formed by a combination of the following elements, separated by spaces. | Operators: +, -, *, /, Real Numbers, Raster indexes and bands R0:1, R1:2 | Example: Quickbird-2 image with bands (0=B,1=G,2=R,3=NIR), the NDVI is calculated by ( R0:3 - R0:2 ) / ( R0:3 + R0:2 ). | | ||
| | Morphological Filter Iterations | Integer | Number of morphological (mode) iterations applied to the result. | Greater or equal to 0. | The use of a value greater than or equal to 1 is advised to avoid the creation of to small hypothesis | | | Morphological Filter Iterations | Integer | Number of morphological (mode) iterations applied to the result. | Greater or equal to 0. | The use of a value greater than or equal to 1 is advised to avoid the creation of to small hypothesis | | ||
| - | | Training Set File | *.shp | A file with training samples for supervised classification. | The file must be in the InterIMAGE regions format. | | | + | | Training Set File | *.shp | A file with training samples for supervised classification. | The file must be in the InterIMAGE region format. | | |
| | Decision Rule | Decision Rule | Decision rule applied to the generated hypothesis. | | Leave blank to accept all generated hypothesis. | | | Decision Rule | Decision Rule | Decision rule applied to the generated hypothesis. | | Leave blank to accept all generated hypothesis. | | ||
| - | | Node Weight | Floating point | This node weight (higher priority will be given to nodes with higher weights in cases where there are geographic overlays). | [0.0, 1.0] | | | + | | Reliability | Floating point | The reliability (higher priority will be given to nodes with higher weights in cases where there are geographic overlays). | [0.0, 1.0] | | |
| + | | Non Class Name | String | If Multi-Class support is active, the discarded hypothesis will fall in the node named by this parameter. | One string with the name of some node in the tree, in the same level. | | | ||
| ===== TerraAIDA Baatz Segmenter ===== | ===== TerraAIDA Baatz Segmenter ===== | ||
| Line 62: | Line 65: | ||
| **Description** | **Description** | ||
| - | This operator performs a Baatz based segmentation (see the reference article for more information). Each generated segment represents an hypothesis to be analyzed by the next semantic network node. | + | This operator performs a segmentation based on Baatz (see the reference article for more information). Each generated segment represents an hypothesis to be analyzed by the next semantic network node. |
| **Type** | **Type** | ||
| Line 78: | Line 81: | ||
| ** Reference** | ** Reference** | ||
| - | * Baatz, M.; Schape, A. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. In: XII Angewandte Geographische Informationsverarbeitung, Wichmann-Verlag, Heidelberg, 2000. | + | * Baatz, M.; Schäpe, A. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. In: XII Angewandte Geographische Informationsverarbeitung, Wichmann Verlag, Heidelberg, 2000. |
| **Processing sequence** | **Processing sequence** | ||
| Line 99: | Line 102: | ||
| | Training Set File | *.shp | A file with training samples for supervised classification. | The file must be in the InterIMAGE regions format. | | | | Training Set File | *.shp | A file with training samples for supervised classification. | The file must be in the InterIMAGE regions format. | | | ||
| | Decision Rule | Decision Rule | Decision rule applied to the generated hypothesis. | | Leave blank to accept all generated hypothesis. | | | Decision Rule | Decision Rule | Decision rule applied to the generated hypothesis. | | Leave blank to accept all generated hypothesis. | | ||
| - | | Node Weight | Floating point | This node weight (higher priority will be given to nodes with higher weights in cases where there are geographic overlays). | [0.0, 1.0] | | | + | | Reliability | Floating point | The reliability (higher priority will be given to nodes with higher weights in cases where there are geographic overlays). | [0.0, 1.0] | | |
| - | | Euclidean Distance Threshold | Floating point | The minimum euclidean distance between each segment feature. | Greater than 0. | This parameter is required to merge adjacent blocks segments when the optimization option is enabled | | + | | Euclidean Distance Threshold | Floating point | The minimum Euclidean Distance between each segment feature. | Greater than 0. | This parameter is required to merge adjacent block segments when the optimization option is enabled | |
| ===== TerraAIDA Bottom-Up Export ===== | ===== TerraAIDA Bottom-Up Export ===== | ||
| Line 110: | Line 113: | ||
| **Description** | **Description** | ||
| - | Allows to export the bottom-up hypothesis (including their attributes) to a shape file. This operator acts like the generic bottom-up operator by making a copy of every hypothesis to the shape file. No spatial conflict resolution is performed when there is no decision rule. | + | Allows to export the bottom-up hypothesis (including its attributes) to a shape file. This operator acts like the generic bottom-up operator, making a copy of every hypothesis to the shape file. No spatial conflict resolution is performed when there is no decision rule. |
| **Type** | **Type** | ||
| Line 147: | Line 150: | ||
| **Description** | **Description** | ||
| - | Use C4.5 Decision Tree Algorithm to perform supervised classification into shapefiles with attributes and classes description. | + | Use C4.5 Decision Tree Algorithm to perform supervised classification into shapefiles with attributes and class description. |
| **Type** | **Type** | ||
| Line 159: | Line 162: | ||
| **Multi-Class support** | **Multi-Class support** | ||
| - | Yes. You must create the same number of classes as the training file. The classes must have the same name of the training file also. One of the nodes must have this operator, and all others should have "No Operator" for Top-Down and Bottom-Up options, and must have the option "Multi-Class" selected as well. | + | Yes. You must create the same number of classes as the training file. The classes must have also the same name of the training file. One of the nodes must have this operator, and all others should have "No Operator" for Top-Down and Bottom-Up options, and must have the option "Multi-Class" selected as well. |
| **Input parameters** | **Input parameters** | ||
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| ^ Parameter Name ^ Type ^ Description ^ Valid Values ^ Note ^ | ^ Parameter Name ^ Type ^ Description ^ Valid Values ^ Note ^ | ||
| | a) Training Set File | * .shp | Training shape file name. | A valid shape file name. | The shapefile must be composed by polygons, their respective attributes and particularly one attribute called "class". | | | a) Training Set File | * .shp | Training shape file name. | A valid shape file name. | The shapefile must be composed by polygons, their respective attributes and particularly one attribute called "class". | | ||
| - | | b) Input Shape File | *.shp | Shape file name of the file to be classified. | A valid shape file name. | The shapefile must be composed by polygons, and the *same set of attributes* in the training shape file. | | + | | b) Input Shape File | *.shp | Shape file name of the file to be classified. | A valid shape file name. | The shapefile must be composed by polygons, and the **same set of attributes** in the training shape file. | |
| - | One example of this operator can be found [[interimage:example_supervised_c45|here]]. | + | Note: The generated decision tree will be placed in a //.txt// file, located in the **same place as your input shape file**, with the same name as your input file. |
| + | |||
| + | One example of this operator can be found [[interimage:examples:supervised_c45|here]]. | ||
| ===== TerraAIDA Checkerboard Segmenter ===== | ===== TerraAIDA Checkerboard Segmenter ===== | ||
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| This operator creates a rectangular grid according to user defined resolution. | This operator creates a rectangular grid according to user defined resolution. | ||
| - | Every rectangle will be a segment for the next nodes. | + | Every rectangle will be a segment for the child nodes. |
| **Type** | **Type** | ||
| Line 195: | Line 200: | ||
| ^ Parameter Name ^ Type ^ Description ^ Valid Values ^ Note ^ | ^ Parameter Name ^ Type ^ Description ^ Valid Values ^ Note ^ | ||
| - | | Size of Cells | Integer | Size of cells in pixels. | Greater than zero. | | | + | | Number of grid lines | Integer | Number of lines to divide the image. | Greater than zero. | | |
| - | | Training Set File | *.shp | A file with training samples for supervised classification. | The file must be in the InterIMAGE regions format. | | | + | | Number of grid columns | Integer | Number of columns to divide the image. | Greater than zero. | | |
| - | | Decision Rule | Decision rule | Decision rule applied in the generated hypothesis. | | Leave blank to accept all generated hypothesis. | | + | | Reliability | Floating point | The reliability (higher priority will be given to nodes with higher weights in cases where there are geographic overlays). | [0.0, 1.0] | | |
| - | | Node Weight | Floating point | This node weight (higher priority will be given to nodes with higher weights in cases where there are geographic overlays). | [0.0, 1.0] | | | + | |
| ===== TerraAIDA NDVI Segmenter ===== | ===== TerraAIDA NDVI Segmenter ===== | ||
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| **Multi-Class support** | **Multi-Class support** | ||
| - | Yes. If you select //true// in the "Top Down Multi-Class" option, you must create another node in the same level with a corresponding opposite name, with prefix "n-". For example, if this class name is "MyClass" the opposite class name will be "n-MyClass". Hypothesis discarded by this operator will be classified as "n-MyClass". | + | Yes. If you select //true// in the "Top Down Multi-Class" option, you must create another node in the same level with a corresponding opposite name, with prefix "n-". For example, if this class name is "MyClass" the opposite class name will be "n-MyClass". Hypothesis discarded by this operator will be classified as "n-MyClass". Alternatively, you can define a new node name in the input parameter named **Non Class Name**. |
| [[interimage:example_supervised_c45|Example of using Multi-Class support.]] | [[interimage:example_supervised_c45|Example of using Multi-Class support.]] | ||
| Line 249: | Line 253: | ||
| | Training Set File | *.shp | A file with training samples for supervised classification. | The file must be in the InterIMAGE regions format. | | | | Training Set File | *.shp | A file with training samples for supervised classification. | The file must be in the InterIMAGE regions format. | | | ||
| | Decision Rule | Decision Rule | Decision rule applied to the generated hypothesis. | | Leave blank to accept all generated hypothesis. | | | Decision Rule | Decision Rule | Decision rule applied to the generated hypothesis. | | Leave blank to accept all generated hypothesis. | | ||
| - | | Node Weight | Floating point | This node weight (higher priority will be given to nodes with higher weights in cases where there are geographic overlays). | [0.0, 1.0] | | | + | | Reliability | Floating point | The reliability (higher priority will be given to nodes with higher weights in cases where there are geographic overlays). | [0.0, 1.0] | | |
| + | | Non Class Name | String | If Multi-Class support is active, the discarded hypothesis will fall in the node named by this parameter. | One string with the name of some node in the tree, in the same level. | | | ||
| ===== TerraAIDA Region Growing Segmenter ===== | ===== TerraAIDA Region Growing Segmenter ===== | ||
| Line 276: | Line 281: | ||
| ** Reference** | ** Reference** | ||
| - | * [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.93.4555&rep=rep1&type=pdf|Bins, L.S.; Fonseca, L.M.G.; Erthal, G.J.; Ii, F.M. Satellite imagery segmentation: a region growing approach. VIII Simpósio Brasileiro de Sensoriamento Remoto, Salvador, BA. Pages 677-680. 1996.]] | + | * [[http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.93.4555&rep=rep1&type=pdf|Bins, L.S.; Fonseca, L.M.G.; Erthal, G.J.; Ii, F.M. Satellite imagery segmentation: a region growing approach. VIII Simpósio Brasileiro de Sensoriamento Remoto, Salvador, BA. p. 677-680. 1996.]] |
| **Processing sequence** | **Processing sequence** | ||
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| ^ Parameter Name ^ Type ^ Description ^ Valid Values ^ Note ^ | ^ Parameter Name ^ Type ^ Description ^ Valid Values ^ Note ^ | ||
| | Input Image | * .tif | Input image file name. | A valid image file name. | The image type must be supported by TerraLib. | | | Input Image | * .tif | Input image file name. | A valid image file name. | The image type must be supported by TerraLib. | | ||
| - | | Euclidean Distance Threshold | Floating point | The minimum euclidean distance between each segment feature. | Greater than 0. | | | + | | Euclidean Distance Threshold | Floating point | The minimum Euclidean Distance between each segment feature. | Greater than 0. | | |
| | Polygons Min Area | Integer | The generated polygons minimum area. | Greater than 0. | | | | Polygons Min Area | Integer | The generated polygons minimum area. | Greater than 0. | | | ||
| | Use Optimization | Boolean | Divides, or not, the image into pieces and segment each piece individually. | yes/no | | | | Use Optimization | Boolean | Divides, or not, the image into pieces and segment each piece individually. | yes/no | | | ||
| | Training Set File | *.shp | A file with training samples for supervised classification. | The file must be in the InterIMAGE regions format. | | | | Training Set File | *.shp | A file with training samples for supervised classification. | The file must be in the InterIMAGE regions format. | | | ||
| | Decision Rule | Decision Rule | Decision rule applied to the generated hypothesis. | | Leave blank to accept all generated hypothesis. | | | Decision Rule | Decision Rule | Decision rule applied to the generated hypothesis. | | Leave blank to accept all generated hypothesis. | | ||
| - | | Node Weight | Floating point | This node weight (higher priority will be given to nodes with higher weights in cases where there are geographic overlays). | [0.0, 1.0] | | | + | | Reliability | Floating point | The reliability (higher priority will be given to nodes with higher weights in cases where there are geographic overlays). | [0.0, 1.0] | | |
| ** Segmentation Example** | ** Segmentation Example** | ||
| Line 327: | Line 332: | ||
| **Notes** | **Notes** | ||
| - | Shape files attributes with the the folowing names will not be imported: REGION, CLASS, ID, GEOWEST, GEONORTH, GEOEAST, GEOSOUTH, FILE_GEOWEST, FILE_GEONORTH, FILE_GEOEAST, FILE_GEOSOUTH, P, FILE, LLX, LLY, URX, URY. | + | Shape file attributes with the the following names will not be imported: REGION, CLASS, ID, GEOWEST, GEONORTH, GEOEAST, GEOSOUTH, FILE_GEOWEST, FILE_GEONORTH, FILE_GEOEAST, FILE_GEOSOUTH, P, FILE, LLX, LLY, URX, URY. |
| + | |||
| + | **Processing sequence** | ||
| + | |||
| + | -Read user input shape file. | ||
| + | -Generates the intersection between the loaded shape file polygons and the operator execution area. | ||
| + | -Apply the user decision rule (using the supplied training set) over the intersection polygons. | ||
| + | -Generate the output hypothesis. | ||
| **Input parameters** | **Input parameters** | ||
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| | Shape File Name | * .shp | Shape file name. | A valid shape file name. | The shapefile must be composed only by polygons, neither lines nor polylines. | | | Shape File Name | * .shp | Shape file name. | A valid shape file name. | The shapefile must be composed only by polygons, neither lines nor polylines. | | ||
| | Shape File Attributes | String | A comma-separated list of shape file attributes names to be imported. | A valid attribute ID or empty field. | Leave blank to use all shape file attributes. | | | Shape File Attributes | String | A comma-separated list of shape file attributes names to be imported. | A valid attribute ID or empty field. | Leave blank to use all shape file attributes. | | ||
| - | | Label Image Resolution | Floating point | The output label image resolution. | Positive real numbers or zero for automatic. | Choose a value good enough matching the resolution of the best used image. Leave blank and the system will automatically choose the resolution. The automatic method only can be used if the operator are not in the semantic network node right above the "scene" node. | | + | | Label Image Resolution | Floating point | The output label image resolution. | Positive real numbers or zero for automatic. | Choose a value good enough matching the resolution of the best used image. Leave blank and the system will automatically choose the resolution. The automatic method can be used only if the operator is not in the semantic network node right above the "scene" node. | |
| - | | Training Set File | *.shp | A file with training samples for supervised classification. | The file must be in the InterIMAGE regions format. | | | + | | Training Set File | *.shp | A file with training samples for supervised classification. | The file must be in the InterIMAGE region format. | | |
| | Decision Rule | Decision rule | Decision rule applied to the generated hypothesis. | | Leave blank to accept all generated hypothesis. | | | Decision Rule | Decision rule | Decision rule applied to the generated hypothesis. | | Leave blank to accept all generated hypothesis. | | ||
| - | | Node Weight | Floating point | This node weight (higher priority will be given to nodes with higher weights in cases where there are geographic overlays). | [0.0, 1.0] | | | + | | Reliability | Floating point | The reliability (higher priority will be given to nodes with higher weights in cases where there are geographic overlays). | [0.0, 1.0] | | |
| {{ :interimage:shape_file_import_example.png }} | {{ :interimage:shape_file_import_example.png }} | ||
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| **Multi-Class support** | **Multi-Class support** | ||
| - | Yes. If you select //true// in the "Top Down Multi-Class" option, you must create another node in the same level with a corresponding opposite name, with prefix "n-". For example, if this class name is "MyClass" the opposite class name will be "n-MyClass". Hypothesis discarded by this operator will be classified as "n-MyClass". | + | Yes. If you select //true// in the "Top Down Multi-Class" option, you must create another node in the same level with a corresponding opposite name, with prefix "n-". For example, if this class name is "MyClass" the opposite class name will be "n-MyClass". Hypothesis discarded by this operator will be classified as "n-MyClass". Alternatively, you can define a new node name in the input parameter named **Non Class Name**. |
| [[interimage:example_supervised_c45|Example of using Multi-Class support.]] | [[interimage:example_supervised_c45|Example of using Multi-Class support.]] | ||
| + | |||
| + | **Processing sequence** | ||
| + | |||
| + | -Read user input shape file. | ||
| + | -Generates the intersection between the loaded shape file polygons and the operator execution area. | ||
| + | -Using the intersection result generates a set of intermediate hypothesis of "MyClass" and the opposite class "n-MyClass", as describe above. | ||
| + | -Apply the user decision rule (using the supplied training set) over the intermediate hypothesis set. | ||
| + | -Generate the output hypothesis from the decision rule result. | ||
| **Input parameters** | **Input parameters** | ||
| Line 372: | Line 392: | ||
| | Shape Target Attribute ID | String | The name of the shape file attribute column used to filter what shape files polygons will be used. | A valid attribute ID. | Leave blank to use all shape file polygons. | | | Shape Target Attribute ID | String | The name of the shape file attribute column used to filter what shape files polygons will be used. | A valid attribute ID. | Leave blank to use all shape file polygons. | | ||
| | Shape Target Attribute Value | String | The value, from the shape target attribute ID, which will define what polygons will be selected. | | Leave blank to use all shape file polygons. | | | Shape Target Attribute Value | String | The value, from the shape target attribute ID, which will define what polygons will be selected. | | Leave blank to use all shape file polygons. | | ||
| - | | Label Image Resolution | Floating point | The output label image resolution. | Positive real numbers or zero for automatic. | Choose a value good enough matching the the resolution of the best used image. Leave blank and the system will automatically choose the resolution. The automatic method only can be used if the operator are not in the semantic network node right above the "scene" node. | | + | | Label Image Resolution | Floating point | The output label image resolution. | Positive real numbers or zero for automatic. | Choose a value good enough matching the the resolution of the best used image. Leave blank and the system will automatically choose the resolution. The automatic method can be used only if the operator is not in the semantic network node right above the "scene" node. | |
| | Training Set File | *.shp | A file with training samples for supervised classification. | The file must be in the InterIMAGE regions format. | | | | Training Set File | *.shp | A file with training samples for supervised classification. | The file must be in the InterIMAGE regions format. | | | ||
| | Decision Rule | Decision Rule | Decision rule applied to the generated hypothesis. | | Leave blank to accept all generated hypothesis. | | | Decision Rule | Decision Rule | Decision rule applied to the generated hypothesis. | | Leave blank to accept all generated hypothesis. | | ||
| - | | Node Weight | Floating point | This node weight (higher priority will be given to nodes with higher weights in cases where there are geographic overlays). | [0.0, 1.0] | | | + | | Reliability | Floating point | The reliability (higher priority will be given to nodes with higher weights in cases where there are geographic overlays). | [0.0, 1.0] | | |
| + | | Non Class Name | String | If Multi-Class support is active, the discarded hypothesis will fall in the node named by this parameter. | One string with the name of some node in the tree, in the same level. | | | ||
| {{ :interimage:shape_file_intersection_example.png }} | {{ :interimage:shape_file_intersection_example.png }} | ||
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