Resumen

VALERO MEDINA, José Antonio; LIZARAZO SALCEDO, Iván Alberto  y  ELSNER, Pau. Topological challenges in multispectral image segmentation. Tecnura [online]. 2014, vol.18, n.spe, pp.136-149. ISSN 0123-921X.  https://doi.org/10.14483/udistrital.jour.tecnura.2014.DSE1.a12.

Land cover classification from remote sensing multispectral images has been traditionally conducted by using mainly spectral information associated with discrete spatial units (i.e. pixels). Geometric and topological characteristics of the spatial context close to every pixel have been either not fully treated or completely ignored. This article provides a review of the strategies used by a number of researchers in order to include spatial and topological properties in image segmentation. It is shown how most of researchers have proposed to perform -previous to classification- a grouping or segmentation of nearby pixels by modeling neighborhood relationships as 4-connected, 8-connected and (a, b) - connected graphs. In this object-oriented approach, however, topological concepts such as neighborhood, contiguity, connectivity and boundary suffer from ambiguity since image elements (pixels) are two-dimensional entities composing a spatially uniform grid cell (i.e. there are not uni-dimensional nor zero-dimensional elements to build boundaries). In order to solve such topological paradoxes, a number of approaches are proposed. This review discusses how the alternative of digital images representation based on Cartesian complexes suggested by Kovalevsky (2008) for image segmentation in computer vision, does not present topological flaws that are typical for conventional solutions based on grid cells. However, such approaches have not yet been applied to multispectral image segmentation in remote sensing. This review concludes suggesting the need to research on the potential of using Cartesian complexes for multispectral image segmentation.

Palabras clave : multispectral images; segmentation; topologic space.

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