Integration of the OpenCV Machine Learning library for K-Nearest Neighbours classification of gridded features.
| Name | Type | Identifier | Description | Constraints |
Input | Features | grid list, input | FEATURES | - | - |
Training Samples | table, input | TRAIN_SAMPLES | Provide a class identifier in the first field followed by sample data corresponding to the input feature grids. | - |
Training Areas | shapes, input | TRAIN_AREAS | - | - |
Output | Classification | grid, output | CLASSES | - | - |
Look-up Table | table, output, optional | CLASSES_LUT | A reference list of the grid values that have been assigned to the training classes. | - |
Options | Normalize | boolean | NORMALIZE | - | Default: 0 |
Colors from Features | boolean, GUI | RGB_COLORS | Use the first three features in list to obtain blue, green, red components for class colour in look-up table. | Default: 0 |
Grid System | grid system | GRID_SYSTEM | - | - |
Training | choice | MODEL_TRAIN | - | Available Choices:
[0] training areas
[1] training samples
[2] load from file
Default: 0 |
Class Identifier | table field | TRAIN_CLASS | - | - |
Buffer Size | floating point number | TRAIN_BUFFER | For non-polygon type training areas, creates a buffer with a diameter of specified size. | Minimum: 0.000000
Default: 1.000000 |
Load Model | file path | MODEL_LOAD | Use a model previously stored to file. | - |
Save Model | file path | MODEL_SAVE | Stores model to file to be used for subsequent classifications instead of training areas. | - |
Default Number of Neighbours | integer number | NEIGHBOURS | - | Minimum: 1
Default: 3 |
Training Method | choice | TRAINING | - | Available Choices:
[0] classification
[1] regression model
Default: 0 |
Algorithm Type | choice | ALGORITHM | - | Available Choices:
[0] brute force
[1] KD Tree
Default: 0 |
Parameter for KD Tree implementation | integer number | EMAX | - | Minimum: 1
Default: 1000 |