Parameters for NDS
| Flag | Description | Mandatory |
|---|---|---|
-ti |
Training images (one or more images). If multivariate, the last dimension should match the number of variables and the size of -dt. NaN values in the training image are ignored. |
✔ |
-di |
Destination image (simulation grid). NaN values are simulated and non-NaN values are treated as conditioning data. | ✔ |
-dt |
Data type. 0 for continuous and 1 for categorical. This also defines the number of variables. |
✔ |
-k |
Number of best candidates used to evaluate the narrowness. | ✔ |
-ki |
Weighting kernel. This defines the search neighborhood and can also be used to normalize variables. If omitted, NDS generates a default kernel internally. | |
-nw |
Narrowness range. 0 corresponds to max-min, 1 approaches the median, default is 0.5 (interquartile range). |
|
-nwv |
Number of variables used to compute the narrowness, counted from the end of the variable list. Default: 1. |
|
-cs |
Chunk size: number of pixels simulated together at each iteration. Default: 1. |
|
-uds |
Update radius: number of nearby pixels updated around each simulated pixel. Default: 10. |
|
-mp |
Partial simulation ratio. 0 means empty and 1 means a full simulation. Default: 1. |
|
-s |
Random seed. | |
-j |
Parallel execution settings. Use -j, N1, N2, N3, where all three values are optional but N3 requires N2, and N2 requires N1. Decimal values in ]0,1[ represent a fraction of the available logical cores. |
|
-wd |
Use the kernel distance. | |
-ed |
Use the Euclidean distance. This is the default. | |
-md |
Use the Manhattan distance. | |
-W_GPU |
Use an integrated GPU if available. | |
-nV |
No Verbatim mode (experimental). |
Examples
NDS is primarily intended for spectrally guided simulation and colorization tasks. The active implementation is controlled by the narrowness parameters -nw and -nwv, together with the chunking and update controls -cs, -uds, and -mp.
Publication
Gravey, M., Rasera, L. G., & Mariethoz, G. (2019). Analogue-based colorization of remote sensing images using textural information. ISPRS Journal of Photogrammetry and Remote Sensing, 147, 242–254. https://doi.org/10.1016/j.isprsjprs.2018.11.003