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Remote Sens. 2014, 6(8), 6897-6928; doi:10.3390/rs6086897

Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping

School of Forestry and Natural Environment, Faculty of Agriculture, Forestry and Natural Environment, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
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Received: 4 April 2014 / Revised: 1 July 2014 / Accepted: 21 July 2014 / Published: 25 July 2014
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Abstract

This study investigates the effectiveness of combining multispectral very high resolution (VHR) and hyperspectral satellite imagery through a decision fusion approach, for accurate forest species mapping. Initially, two fuzzy classifications are conducted, one for each satellite image, using a fuzzy output support vector machine (SVM). The classification result from the hyperspectral image is then resampled to the multispectral’s spatial resolution and the two sources are combined using a simple yet efficient fusion operator. Thus, the complementary information provided from the two sources is effectively exploited, without having to resort to computationally demanding and time-consuming typical data fusion or vector stacking approaches. The effectiveness of the proposed methodology is validated in a complex Mediterranean forest landscape, comprising spectrally similar and spatially intermingled species. The decision fusion scheme resulted in an accuracy increase of 8% compared to the classification using only the multispectral imagery, whereas the increase was even higher compared to the classification using only the hyperspectral satellite image. Perhaps most importantly, its accuracy was significantly higher than alternative multisource fusion approaches, although the latter are characterized by much higher computation, storage, and time requirements. View Full-Text
Keywords: decision fusion; high spatial resolution; satellite hyperspectral imagery; forest species mapping; fuzzy output SVM; post-regularization decision fusion; high spatial resolution; satellite hyperspectral imagery; forest species mapping; fuzzy output SVM; post-regularization
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Stavrakoudis, D.G.; Dragozi, E.; Gitas, I.Z.; Karydas, C.G. Decision Fusion Based on Hyperspectral and Multispectral Satellite Imagery for Accurate Forest Species Mapping. Remote Sens. 2014, 6, 6897-6928.

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