Compute pointwise geometry features based on local neighborhood. Each feature is added into an
extrabyte attribute. The names of the extrabytes attributes (if recorded) are coeff00, coeff01,
coeff02 and so on, lambda1, lambda2, lambda3, anisotropy, planarity, sphericity, linearity,
omnivariance, curvature, eigensum, angle, normalX, normalY, normalZ (recorded in this order).
There is a total of 23 attributes that can be added. It is strongly discouraged to use them all.
All the features are recorded with single precision floating points yet computing them all will triple
the size of the point cloud. This stage modifies the point cloud in the pipeline but does not produce
any output.
Arguments
- k, r
integer and numeric respectively for k-nearest neighbours and radius of the neighborhood sphere. If k is given and r is missing, computes with the knn, if r is given and k is missing computes with a sphere neighborhood, if k and r are given computes with the knn and a limit on the search distance.
- features
String. Geometric feature to export. Each feature is added into an extrabyte attribute. Use 'C' for the 9 principal component coefficients, 'E' for the 3 eigenvalues of the covariance matrix, 'a' for anisotropy, 'p' for planarity, 's' for sphericity, 'l' for linearity, 'o' for omnivariance, 'c' for curvature, 'e' for the sum of eigenvalues, 'i' for the angle (inclination in degrees relative to the azimuth), and 'n' for the 3 components of the normal vector. Notice that the uppercase labeled components allow computing all the lowercase labeled components. Default is "". In this case, the singular value decomposition is computed but serves no purpose. The order of the flags does not matter and the features are recorded in the order mentioned above.
References
Hackel, T., Wegner, J. D., & Schindler, K. (2016). Contour detection in unstructured 3D point clouds. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1610-1618).
Examples
f <- system.file("extdata", "Example.las", package = "lasR")
pipeline <- geometry_features(8, features = "pi") + write_las()
ans <- exec(pipeline, on = f)