Local optimal scale in a hierarchical segmentation method for satellite image: an OBIA approach for the agricultural landscape

Gonzalo Martín, Consuelo; Lillo Saavedra, Mario; Menasalvas Ruiz, Ernestina; Fonseca Luengo, David; García Pedrero, Ángel Mario y Costumero Moreno, Roberto (2015). Local optimal scale in a hierarchical segmentation method for satellite image: an OBIA approach for the agricultural landscape. "Journal of Intelligent Information Systems", v. 46 (n. 3); pp. 517-529. ISSN 0925-9902. https://doi.org/10.1007/s10844-015-0365-4.

Descripción

Título: Local optimal scale in a hierarchical segmentation method for satellite image: an OBIA approach for the agricultural landscape
Autor/es:
  • Gonzalo Martín, Consuelo
  • Lillo Saavedra, Mario
  • Menasalvas Ruiz, Ernestina
  • Fonseca Luengo, David
  • García Pedrero, Ángel Mario
  • Costumero Moreno, Roberto
Tipo de Documento: Artículo
Título de Revista/Publicación: Journal of Intelligent Information Systems
Fecha: Junio 2015
Volumen: 46
Materias:
Escuela: E.T.S. de Ingenieros Informáticos (UPM)
Departamento: Arquitectura y Tecnología de Sistemas Informáticos
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 40800
Identificador DC: http://oa.upm.es/40800/
Identificador OAI: oai:oa.upm.es:40800
Identificador DOI: 10.1007/s10844-015-0365-4
URL Oficial: http://link.springer.com/article/10.1007/s10844-015-0365-4
Depositado por: Memoria Investigacion
Depositado el: 20 Oct 2016 07:31
Ultima Modificación: 20 Oct 2016 07:31
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