DistMat3D-class | R Documentation |

Class to store effectively (large) distance matrices (up to 3D), which can be interpreted as a stack of traditional 2-D distance matrices. Therefore, the first two dimensions are of equal length and usually describe the wavelength in hsdar. This third dimension is normally the number of samples or pixels. In hsdar, objects of class `DistMat3D`

are used e.g., to store `nri`

-values. In this case, the first and second dimensions store the information which band #1 is substraced by which band #2, respectively. The third dimension is the sample. Since it usually does not matter if band #1 is substracted from band #2 or vice versa, the `nri`

-matrix would contain the same absolute values on both triangles (as 2-D distance matrices would do). Therefore, hsdar defines and uses the class `DistMat3D`

in which only one triangle is stored and memory demand is considerably reduced.

S4-class with 3 slots:

values: Numerical vector containing distance values

ncol: Number of columns in the 3D-matrix. Number of columns equals always the number of rows

nlyr: Number of layers in the 3D-matrix

The data in the values slot is organized as follows: The first value is the distance at band #1 and band #2 for sample number #1, the second one is for band #1 and band #3 (sample #1) and so forth. Methods to create objects of class `DistMat3D`

for matrix and array objects exist. Additionally, methods to apply functions to the values exist.

See figure in `hsdar-package`

for an overview of classes in hsdar.

Lukas Lehnert

`distMat3D`

, `apply.DistMat3D`

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