26 #ifndef __TERRALIB_INTERNAL_RP_H 27 #define __TERRALIB_INTERNAL_RP_H 48 #include "rp/Config.h" 119 #endif // __TERRALIB_INTERNAL_RP_H Raster Processing functions.
Raster segmenter strategy factory base class.
Raster decomposition using mixture model.
Creation of skeleton imagems.
ISOData strategy for image classification.
Dummy strategy (just for testing purposes).
A structure to hold the set of GLCM metrics.
Maximum a posteriori probability strategy.
KMeans strategy for image classification.
Performs raster data registering into a SRS using a set of tie points.
Blended pixel value calculation for two overlaped rasters.
Extraction of attributes from Raster, Bands, and Polygons.
Spectral Response Functions.
EM (Expectation-Maximization) strategy for pixel-based classification.
Tie-Pointsr locator SURF strategy.
Raster Processing algorithm base interface class.
Tie-Pointsr locator strategy.
Performs arithmetic operation over raster data.
Raster mixture model strategy factory base class.
Raster Processing algorithm output parameters base interface.
Tie-Pointsr locator Moravec strategy.
A maximum likelihood estimation strategy for classification (a.k.a. MaxVer in portuguese).
This singleton defines the TerraLib Raster Processing module entry.
Raster region growing segmenter Baatz strategy.
Raster strategy parameters base class.
Create mosaics from a sequence of overlapped rasters using an automatic tie-points detection method...
Create a mosaic from a set of geo-referenced rasters.
Raster linear strategy for mixture model classification.
Spectral Angle Mapper classification strategy.
Raster classifier strategy factory base class.
Raster region growing segmenter Mean strategy.
Raster segmenter strategy base class.
A series of well-known filtering algorithms for images, linear and non-linear.
Raster segmenter strategy parameters base class.
Raster mixture model strategy base class.
Euclidean Distance Classifier strategy.
TiePointsLocator locator.
Radar Raster Processing functions.
Raster classifier strategy base class.
Create a mosaic from a set of rasters using tie-points.
PCA (Principal Component Analysis) strategy for mixture model.
ISOSeg strategy for segmentation-based classification.
Creation of skeleton imagems.
Dummy strategy (just for testing purposes).
Creation of skeleton imagems.