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 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 | Grid System | Grid system | PARAMETERS_GRID_SYSTEM | - | - |
Normalize | Boolean | NORMALIZE | - | Default: 0 |
Update 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 |
Load Model | File path | MODEL_LOAD | Use a model previously stored to file. | - |
Class Identifier | Table field | TRAIN_CLASS | - | - |
Buffer Size | Floating point | TRAIN_BUFFER | For non-polygon type training areas, creates a buffer with a diameter of specified size. | Minimum: 0.000000 Default: 1.000000 |
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 | 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 | EMAX | - | Minimum: 1 Default: 1000 |
(*) optional |