Haralick Texture Extraction

Brief Description

Computes Haralick textural features on the selected channel of the input image

Tags

Feature Extraction, Textures

Long Description

This application computes three sets of Haralick features [1][2].
* simple: a set of 8 local Haralick features: Energy (texture uniformity) , Entropy (measure of randomness of intensity image), Correlation (how correlated a pixel is to its neighborhood), Inverse Difference Moment (measures the texture homogeneity), Inertia (intensity contrast between a pixel and its neighborhood), Cluster Shade, Cluster Prominence, Haralick Correlation;
* advanced: a set of 10 advanced Haralick features : Mean, Variance (measures the texture heterogeneity), Dissimilarity, Sum Average, Sum Variance, Sum Entropy, Difference of Entropies, Difference of Variances, IC1, IC2;
* higher: a set of 11 higher Haralick features : Short Run Emphasis (measures the texture sharpness), Long Run Emphasis (measures the texture roughness), Grey-Level Nonuniformity, Run Length Nonuniformity, Run Percentage (measures the texture sharpness homogeneity), Low Grey-Level Run Emphasis, High Grey-Level Run Emphasis, Short Run Low Grey-Level Emphasis, Short Run High Grey-Level Emphasis, Long Run Low Grey-Level Emphasis and Long Run High Grey-Level Emphasis.

Parameters

Limitations

The computation of the features is based on a Gray Level Co-occurrence matrix (GLCM) from the quantized input image. Consequently the quantization parameters (min, max, nbbin) must be appropriate to the range of the pixel values.

Authors

OTB-Team

See also

[1] HARALICK, Robert M., SHANMUGAM, Karthikeyan, et al. Textural features for image classification. IEEE Transactions on systems, man, and cybernetics, 1973, no 6, p. 610-621.
[2] otbScalarImageToTexturesFilter, otbScalarImageToAdvancedTexturesFilter and otbScalarImageToHigherOrderTexturesFilter classes

Example of use