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::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 |
Algorithms to detect patterns in raster regions using different methods.