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Proceedings Paper

Increasing the UAV data value by an OBIA methodology
Author(s): Angel García-Pedrero; Mario Lillo-Saavedra; Dionisio Rodriguez-Esparragon; Alejandro Rodriguez-Gonzalez; Consuelo Gonzalo-Martin
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Paper Abstract

Recently, there has been a noteworthy increment of using images registered by unmanned aerial vehicles (UAV) in different remote sensing applications. Sensors boarded on UAVs has lower operational costs and complexity than other remote sensing platforms, quicker turnaround times as well as higher spatial resolution. Concerning this last aspect, particular attention has to be paid on the limitations of classical algorithms based on pixels when they are applied to high resolution images. The objective of this study is to investigate the capability of an OBIA methodology developed for the automatic generation of a digital terrain model of an agricultural area from Digital Elevation Model (DEM) and multispectral images registered by a Parrot Sequoia multispectral sensor board on a eBee SQ agricultural drone. The proposed methodology uses a superpixel approach for obtaining context and elevation information used for merging superpixels and at the same time eliminating objects such as trees in order to generate a Digital Terrain Model (DTM) of the analyzed area. Obtained results show the potential of the approach, in terms of accuracy, when it is compared with a DTM generated by manually eliminating objects.

Paper Details

Date Published: 4 October 2017
PDF: 8 pages
Proc. SPIE 10427, Image and Signal Processing for Remote Sensing XXIII, 104270U (4 October 2017); doi: 10.1117/12.2277891
Show Author Affiliations
Angel García-Pedrero, Univ. Politécnica de Madrid (Spain)
Mario Lillo-Saavedra, Univ. de Concepción (Chile)
Dionisio Rodriguez-Esparragon, Univ. de Las Palmas de Gran Canaria (Spain)
Alejandro Rodriguez-Gonzalez, Univ. Politécnica de Madrid (Spain)
Consuelo Gonzalo-Martin, Univ. Politécnica de Madrid (Spain)

Published in SPIE Proceedings Vol. 10427:
Image and Signal Processing for Remote Sensing XXIII
Lorenzo Bruzzone, Editor(s)

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