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Memorias de investigación
Artículos en revistas:
Local optimal scale in a hierarchical segmentation method for satellite images
Áreas de investigación
  • Ingenierías,
  • Ciencias de la computación y tecnología informática
Overrecentdecades,remotesensinghasemergedasaneffectivetoolforimprov- ing agriculture productivity. In particular, many works have dealt with the problem of identifying characteristics or phenomena of crops and orchards on different scales using remote sensed images. Since the natural processes are scale dependent and most of them are hierarchically structured, the determination of optimal study scales is mandatory in understanding these processes and their interactions. The concept of multi-scale/multi- resolution inherent to OBIA methodologies allows the scale problem to be dealt with. But for that multi-scale and hierarchical segmentation algorithms are required. The question that remains unsolved is to determine the suitable scale segmentation that allows different objects and phenomena to be characterized in a single image. In this work, an adaptation of the Simple Linear Iterative Clustering (SLIC) algorithm to perform a multi-scale hierarchi- cal segmentation of satellite images is proposed. The selection of the optimal multi-scale segmentation for different regions of the image is carried out by evaluating the intra- variability and inter-heterogeneity of the regions obtained on each scale with respect to the parent-regions defined by the coarsest scale. To achieve this goal, an objective function, that combines weighted variance and the global Moran index, has been used. Two different kinds of experiment have been carried out, generating the number of regions on each scale through linear and dyadic approaches. This methodology has allowed, on the one hand, the detection of objects on different scales and, on the other hand, to represent them all in a sin- gle image. Altogether, the procedure provides the user with a better comprehension of the land cover, the objects on it and the phenomena occurring.
Título de la revista
Journal of Intelligent Information Systems
Factor de impacto JCR
Información de impacto
Datos JCR del año 2013
Número de revista
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Esta actividad pertenece a memorias de investigación
  • Autor: Consuelo Gonzalo Martin (UPM)
  • Autor: Mario Lillo Saavedra (Universidad de Concepción)
  • Autor: Ernestina Menasalvas Ruiz (UPM)
  • Autor: David Fonseca Luengo (Universidad de Concepción)
  • Autor: Angel Mario García Pedrero (UPM)
  • Autor: Roberto Costumero Moreno (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Minería de Datos y Simulación (MIDAS)
  • Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB
  • Departamento: Arquitectura y Tecnología de Sistemas Informáticos
  • Departamento: Lenguajes y Sistemas Informáticos e Ingeniería de Software
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