A fuzzy topology-based maximum likelihood classification

Improving the potential of pixel-based supervised classification in the absence of quality ground truth data

“…Tu et al (2012) compared various modelling approaches towards predicting potential habitat and species distribution. SPOT 5 imagery were also used for vegetation patch detection in China (Liu et al, 2011) and vegetation change detection within three coarse vegetation classes in the Brazilian Amazon (Lu et al, 2008). However, these studies were all applied in forested areas and cannot be readily adapted to the savanna biome.…”

Fuzzy Modeling of Geospatial Patterns

“…Other classification approaches attempt to take advantage of the strengths of each algorithm. For example, in the combination of two techniques-fuzzy topology and the Maximum Likelihood Classifier (MLC) (Liu et al, 2011), known as FTMLC-one membership function is created for each pixel using FTMLC and the pixels with greater membership are assigned a certain class, while those with less membership are left at the boundaries for a later process. Connectivity is sought for pixels at the boundaries with respect to their 8 neighbors, in such a manner that the one with the higher number of connected pixels belongs to that class.…”

Logic-based topological representation of vague moving regions: computational models for well-behaved GIS solutions

“…For implementation, many studies have theoretical natures that are not directly applicable in a GIS or must be wellbehaved. Conversely, according to the literature, the works related to fuzzy topology models in the GIS domain are based on point-set topology often applicable to data stored in raster construction (Shi & Liu 2007;Liu & Shi 2009;Liu et al 2011). These models are intrinsically inappropriate for moving object problems in which data are usually stored in a vector model and a different fuzzy definition is required.…”