Algorithms to detect patterns in raster regions using different methods. More...

Classes

class  te::rp::Classifier
 Raster classification. More...
 
class  te::rp::ClassifierDummyStrategy
 Dummy strategy (just for testing purposes). More...
 
class  te::rp::ClassifierEDStrategy
 Euclidean Distance Classifier strategy. More...
 
class  te::rp::ClassifierEMStrategy
 EM strategy for pixel-based classification. This is an unsupervised and pixel-based classification algorithm. Expectation-Maximization (EM) works iteratively by applying two steps: the E-step (Expectation) and the M-step (Maximization). The method aims to approximate the parameter estimates to real data distribution, along the iterations: More...
 
class  te::rp::ClassifierISOSegStrategy
 ISOSeg strategy for OBIA classification. The algorithm orders regions by area (larger first), and classify the largest region as Cluster 1. All regions similar to this cluster are inserted in Cluster 1, otherwise new Clusters are created. After all regions belong to a cluster, the algorithm merges similar clusters. The acceptance threshold is the only parameter given by the user, and it indicates the maximum distance between two regions to be clustered togheter. More...
 
class  te::rp::ClassifierKMeansStrategy
 KMeans strategy for image classification. Step-by-step: More...
 
class  te::rp::ClassifierMAPStrategy
 Maximum a posteriori probability strategy. More...
 
class  te::rp::ClassifierSAMStrategy
 Spectral Angle Mapper classification strategy. More...
 
class  te::rp::MixtureModel
 Raster decomposition using mixture model. More...
 
class  te::rp::MixtureModelLinearStrategy
 
class  te::rp::MixtureModelPCAStrategy
 

Detailed Description

Algorithms to detect patterns in raster regions using different methods.