Logistic Classification
Classify surface features using logistic regression on engineered height-derived features. Optionally accepts a training mask; otherwise an Otsu-based threshold generates pseudo-labels automatically.
Inputs
| Name |
Type |
Required |
Description |
| field |
DATA_FIELD |
Yes |
Input topographic surface to classify |
| training_mask |
IMAGE |
No |
Optional training labels — masked pixels are treated as the positive class |
Outputs
| Name |
Type |
Description |
| mask |
IMAGE |
Binary classification result (0 or 255) |
| probability |
DATA_FIELD |
Per-pixel probability from the logistic model (values in [0, 1]) |
Controls
| Name |
Type |
Default |
Description |
| use_gaussians |
BOOLEAN |
True |
Include Gaussian blur features at multiple scales |
| n_gaussians |
INT |
4 |
Number of Gaussian scales (1–10). Only shown when use_gaussians is True |
| use_sobel |
BOOLEAN |
True |
Include Sobel gradient features (horizontal and vertical) |
| use_laplacian |
BOOLEAN |
True |
Include Laplacian (sum of second differences) feature |
| regularization |
FLOAT |
1.0 |
L2 regularization strength lambda (0.0–10.0) |
| max_iter |
INT |
500 |
Maximum gradient descent iterations (10–5000) |
| seed |
INT |
42 |
Random seed for reproducibility (0–999999) |
Notes
- Feature engineering: The classifier always uses normalized raw height as a feature. Gaussian blurs at scales 2^0, 2^1, ..., 2^(n-1) capture multi-scale smoothness. Sobel gradients detect edges, and the Laplacian highlights curvature. All features are standardized to zero mean and unit variance before training.
- L2 regularization: The regularization parameter controls overfitting by penalizing large weights. Higher values produce smoother, more generalizable decision boundaries. The bias term is not regularized.
- Logistic regression vs neural networks: Logistic regression is a linear classifier — it learns a single hyperplane in feature space. For complex, highly non-linear boundaries a neural network may be more appropriate, but logistic regression is fast, interpretable, and often sufficient when combined with good feature engineering.
- Unsupervised mode: When no training mask is provided, the node uses an Otsu-like threshold on the raw height to generate pseudo-labels, then trains the classifier on those labels. This can improve on simple thresholding because the classifier leverages multi-scale and gradient features.
- Equivalent to Gwyddion's logistic.c classification functionality.
