A procedure for semi-automatic segmentation in OBIA based on the maximization of a comparison index


Abstract:

In an Object Based Image Analysis Classification (OBIA) process, the quality of the classification results are highly dependent on segmentation. However, a high number of the studies that make use of an OBIA process find the segmentation parameters by making use of trial-and-error methods. It is clear that a lack of a structured procedure to determine the segmentation parameters produces unquantified errors in the classification. This paper aims to quantify the effects of using a semi-automatic approach to determine optimal segmentation parameters. To this end, an OBIA process is performed to classify land cover types produced by both a manual and an automatic segmentation. Even though the classification using the manual segmentation outperforms the automatic segmentation, the difference is only 2%. Since the automatic segmentation is performed with optimal parameters, a procedure to accurately determine those parameters must be performed to minimize the error produced by a misjudgment in the segmentation step. © 2014 Springer International Publishing.

Año de publicación:

2014

Keywords:

  • segmentation parameters
  • OBIA
  • comparison index
  • segmentation
  • SUPPORT VECTOR MACHINES
  • classification

Fuente:

googlegoogle
scopusscopus
rraaerraae

Tipo de documento:

Conference Object

Estado:

Acceso restringido

Áreas de conocimiento:

  • Visión por computadora
  • Algoritmo

Áreas temáticas:

  • Sistemas