Tool Supervised Image Classification
Standard methods for supervised image classification, including minimum distance, maximum likelihood, spectral angle mapping. Classifiers can be trained by areas defined through shapes, samples supplied as table records, or statistics previously stored to file.
- Author: O.Conrad (c) 2005
- Menu: Imagery|Classification
Parameters
| Name | Type | Identifier | Description | Constraints | |
|---|---|---|---|---|---|
| Input | Features | Grid list, input | GRIDS | - | - |
| Training Areas | Shapes, input | TRAINING | - | - | |
| Training Samples | Table, input | TRAIN_SAMPLES | Provide a class identifier in the first field followed by sample data corresponding to the input feature grids. | - | |
| 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. | - | |
| Quality (*) | Grid, output, optional | QUALITY | Dependent on chosen method, these are distances or probabilities. | - | |
| Options | Grid System | Grid system | PARAMETERS_GRID_SYSTEM | - | - |
| Normalize | Boolean | NORMALISE | - | 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 | |
| Training | Choice | TRAIN_WITH | - | Available Choices: [0] training areas [1] training samples [2] load from file Default: 0 | |
| Class Identifier | Table field | TRAINING_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 | |
| Load Statistics from File... | File path | FILE_LOAD | - | - | |
| Save Statistics to File... | File path | FILE_SAVE | - | - | |
| Method | Choice | METHOD | - | Available Choices: [0] Binary Encoding [1] Parallelepiped [2] Minimum Distance [3] Mahalanobis Distance [4] Maximum Likelihood [5] Spectral Angle Mapping Default: 2 | |
| Distance Threshold | Floating point | THRESHOLD_DIST | Let pixel stay unclassified, if minimum euclidian or mahalanobis distance is greater than threshold. | Minimum: 0.000000 Default: 0.000000 | |
| Spectral Angle Threshold (Degree) | Floating point | THRESHOLD_ANGLE | Let pixel stay unclassified, if spectral angle distance is greater than threshold. | Minimum: 0.000000 Maximum: 90.000000 Default: 0.000000 | |
| Probability Threshold | Floating point | THRESHOLD_PROB | Let pixel stay unclassified, if maximum likelihood probability value is less than threshold. | Minimum: 0.000000 Maximum: 100.000000 Default: 0.000000 | |
| Probability Reference | Choice | RELATIVE_PROB | - | Available Choices: [0] absolute [1] relative Default: 1 | |
| Binary Encoding | Boolean | WTA_0 | - | Default: 0 | |
| Parallelepiped | Boolean | WTA_1 | - | Default: 0 | |
| Minimum Distance | Boolean | WTA_2 | - | Default: 0 | |
| Mahalanobis Distance | Boolean | WTA_3 | - | Default: 0 | |
| Maximum Likelihood | Boolean | WTA_4 | - | Default: 0 | |
| Spectral Angle Mapping | Boolean | WTA_5 | - | Default: 0 | |
| (*) optional | |||||
Command-line
Usage: saga_cmd imagery_classification 0 [-GRIDS <str>] [-NORMALISE <str>] [-CLASSES <str>] [-CLASSES_LUT <str>] [-QUALITY <str>] [-TRAIN_WITH <str>] [-TRAINING <str>] [-TRAINING_CLASS <str>] [-TRAIN_BUFFER <double>] [-TRAIN_SAMPLES <str>] [-FILE_LOAD <str>] [-FILE_SAVE <str>] [-METHOD <str>] [-THRESHOLD_DIST <double>] [-THRESHOLD_ANGLE <double>] [-THRESHOLD_PROB <double>] [-RELATIVE_PROB <str>] [-WTA_0 <str>] [-WTA_1 <str>] [-WTA_2 <str>] [-WTA_3 <str>] [-WTA_4 <str>] [-WTA_5 <str>] -GRIDS:<str> Features Grid list, input -NORMALISE:<str> Normalize Boolean Default: 0 -CLASSES:<str> Classification Grid, output -CLASSES_LUT:<str> Look-up Table Table, output, optional -QUALITY:<str> Quality Grid, output, optional -TRAIN_WITH:<str> Training Choice Available Choices: [0] training areas [1] training samples [2] load from file Default: 0 -TRAINING:<str> Training Areas Shapes, input -TRAINING_CLASS:<str> Class Identifier Table field -TRAIN_BUFFER:<double> Buffer Size Floating point Minimum: 0.000000 Default: 1.000000 -TRAIN_SAMPLES:<str> Training Samples Table, input -FILE_LOAD:<str> Load Statistics from File... File path -FILE_SAVE:<str> Save Statistics to File... File path -METHOD:<str> Method Choice Available Choices: [0] Binary Encoding [1] Parallelepiped [2] Minimum Distance [3] Mahalanobis Distance [4] Maximum Likelihood [5] Spectral Angle Mapping Default: 2 -THRESHOLD_DIST:<double> Distance Threshold Floating point Minimum: 0.000000 Default: 0.000000 -THRESHOLD_ANGLE:<double> Spectral Angle Threshold (Degree) Floating point Minimum: 0.000000 Maximum: 90.000000 Default: 0.000000 -THRESHOLD_PROB:<double> Probability Threshold Floating point Minimum: 0.000000 Maximum: 100.000000 Default: 0.000000 -RELATIVE_PROB:<str> Probability Reference Choice Available Choices: [0] absolute [1] relative Default: 1 -WTA_0:<str> Binary Encoding Boolean Default: 0 -WTA_1:<str> Parallelepiped Boolean Default: 0 -WTA_2:<str> Minimum Distance Boolean Default: 0 -WTA_3:<str> Mahalanobis Distance Boolean Default: 0 -WTA_4:<str> Maximum Likelihood Boolean Default: 0 -WTA_5:<str> Spectral Angle Mapping Boolean Default: 0