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Remote Sens. 2015, 7(1), 767-787;

A GEOBIA Methodology for Fragmented Agricultural Landscapes

Centro de Tecnología Biomédica, Universidad Politécnica de Madrid, Campus de Montegancedo, Pozuelo de Alarcón 28233, Spain
Facultad de Ingeniería Agrícola, Universidad de Concepción, Av. Vicente Méndez 595, Casilla 537, Chillán, Octava Región, Chile
Author to whom correspondence should be addressed.
Academic Editors: Ioannis Gitas and Prasad S. Thenkabail
Received: 10 October 2014 / Accepted: 25 December 2014 / Published: 13 January 2015
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Very high resolution remotely sensed images are an important tool for monitoring fragmented agricultural landscapes, which allows farmers and policy makers to make better decisions regarding management practices. An object-based methodology is proposed for automatic generation of thematic maps of the available classes in the scene, which combines edge-based and superpixel processing for small agricultural parcels. The methodology employs superpixels instead of pixels as minimal processing units, and provides a link between them and meaningful objects (obtained by the edge-based method) in order to facilitate the analysis of parcels. Performance analysis on a scene dominated by agricultural small parcels indicates that the combination of both superpixel and edge-based methods achieves a classification accuracy slightly better than when those methods are performed separately and comparable to the accuracy of traditional object-based analysis, with automatic approach. View Full-Text
Keywords: remote sensing; image analysis; GEOBIA; superpixels remote sensing; image analysis; GEOBIA; superpixels

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Garcia-Pedrero, A.; Gonzalo-Martin, C.; Fonseca-Luengo, D.; Lillo-Saavedra, M. A GEOBIA Methodology for Fragmented Agricultural Landscapes. Remote Sens. 2015, 7, 767-787.

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