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Open AccessArticle

Fusion of High Resolution Multispectral Imagery in Vulnerable Coastal and Land Ecosystems

Instituto de Oceanografía y Cambio Global, IOCAG, Universidad Las Palmas de Gran Canaria (ULPGC), Parque Científico Tecnológico Marino de Taliarte , 35214 Telde, Spain
Centro de Tecnología Biomédica, Universidad Politécnica de Madrid (UPM), Campus de Montegancedo, 28223 Pozuelo de Alarcón, Spain
Author to whom correspondence should be addressed.
Academic Editor: Alistair M. S. Smith
Sensors 2017, 17(2), 228;
Received: 4 October 2016 / Revised: 14 December 2016 / Accepted: 17 January 2017 / Published: 25 January 2017
(This article belongs to the Section Remote Sensors, Control, and Telemetry)
Ecosystems provide a wide variety of useful resources that enhance human welfare, but these resources are declining due to climate change and anthropogenic pressure. In this work, three vulnerable ecosystems, including shrublands, coastal areas with dunes systems and areas of shallow water, are studied. As far as these resources’ reduction is concerned, remote sensing and image processing techniques could contribute to the management of these natural resources in a practical and cost-effective way, although some improvements are needed for obtaining a higher quality of the information available. An important quality improvement is the fusion at the pixel level. Hence, the objective of this work is to assess which pansharpening technique provides the best fused image for the different types of ecosystems. After a preliminary evaluation of twelve classic and novel fusion algorithms, a total of four pansharpening algorithms was analyzed using six quality indices. The quality assessment was implemented not only for the whole set of multispectral bands, but also for the subset of spectral bands covered by the wavelength range of the panchromatic image and outside of it. A better quality result is observed in the fused image using only the bands covered by the panchromatic band range. It is important to highlight the use of these techniques not only in land and urban areas, but a novel analysis in areas of shallow water ecosystems. Although the algorithms do not show a high difference in land and coastal areas, coastal ecosystems require simpler algorithms, such as fast intensity hue saturation, whereas more heterogeneous ecosystems need advanced algorithms, as weighted wavelet ‘à trous’ through fractal dimension maps for shrublands and mixed ecosystems. Moreover, quality map analysis was carried out in order to study the fusion result in each band at the local level. Finally, to demonstrate the performance of these pansharpening techniques, advanced Object-Based (OBIA) support vector machine classification was applied, and a thematic map for the shrubland ecosystem was obtained, which corroborates wavelet ‘à trous’ through fractal dimension maps as the best fusion algorithm for this ecosystem. View Full-Text
Keywords: pansharpening; high resolution satellite images; WorldView-2; ecosystem management; classification pansharpening; high resolution satellite images; WorldView-2; ecosystem management; classification
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Ibarrola-Ulzurrun, E.; Gonzalo-Martin, C.; Marcello-Ruiz, J.; Garcia-Pedrero, A.; Rodriguez-Esparragon, D. Fusion of High Resolution Multispectral Imagery in Vulnerable Coastal and Land Ecosystems. Sensors 2017, 17, 228.

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