Integration of the OpenCV Machine Learning library for Artificial Neural Network 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. | - |
Number of Layers | Integer | ANN_LAYERS | You can specify the number of layers in the network (not including input and output layer). | Minimum: 1 Default: 3 |
Number of Neurons | Integer | ANN_NEURONS | You can specify the number of neurons in each layer of the network. | Minimum: 1 Default: 3 |
Maximum Number of Iterations | Integer | ANN_MAXITER | - | Minimum: 1 Default: 300 |
Error Change (Epsilon) | Floating point | ANN_EPSILON | Termination criteria of the training algorithm. You can specify how much the error could change between the iterations to make the algorithm continue (epsilon). | Minimum: 0.000000 Default: 0.000000 |
Activation Function | Choice | ANN_ACTIVATION | - | Available Choices: [0] Identity [1] Sigmoid [2] Gaussian Default: 1 |
Function's Alpha | Floating point | ANN_ACT_ALPHA | - | Default: 1.000000 |
Function's Beta | Floating point | ANN_ACT_BETA | - | Default: 1.000000 |
Training Method | Choice | ANN_PROPAGATION | - | Available Choices: [0] resilient propagation [1] back propagation Default: 1 |
Initial Update Value | Floating point | ANN_RP_DW0 | - | Default: 0.000000 |
Increase Factor | Floating point | ANN_RP_DW_PLUS | - | Minimum: 1.010000 Default: 1.200000 |
Decrease Factor | Floating point | ANN_RP_DW_MINUS | - | Minimum: 0.010000 Maximum: 0.990000 Default: 0.500000 |
Lower Value Update Limit | Floating point | ANN_RP_DW_MIN | - | Minimum: 0.010000 Default: 0.100000 |
Upper Value Update Limit | Floating point | ANN_RP_DW_MAX | - | Minimum: 1.010000 Default: 1.100000 |
Weight Gradient Term | Floating point | ANN_BP_DW | - | Default: 0.100000 |
Moment Term | Floating point | ANN_BP_MOMENT | - | Default: 0.100000 |
(*) optional |