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Effects of Pansharpening Methods on Discrimination of Tropical Crop and Forest Using Very High-Resolution Satellite Imagery

Abstract : This paper assesses the effect of pansharpening process in classification of tropical crop and forest areas. Supervised classifications based on Support Vector Machine were adopted. Different pansharpening methods using bilinear interpolation technique have been used to merge very high spatial resolution Quickbird multispectral and panchromatic imagery. To develop this study, seven sub-areas were extracted and human segmentations data were created. The quantitative results based on the mean of Probabilistic Rand Index, Variation of Information and Global Consistency Error, computed for all sub-areas, showed similar results by using (0.92, 0.87, 0.87, 1.23, 0,2 respectively) and by not applying (0.93, 0.89, 0.86, 1.23, 0.21 respectively) pansharpening methods.
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https://hal.archives-ouvertes.fr/hal-01931765
Contributor : Enguerran Grandchamp <>
Submitted on : Friday, November 23, 2018 - 2:29:50 AM
Last modification on : Thursday, February 28, 2019 - 1:15:39 AM

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Mohamed Abadi, Enguerran Grandchamp, Artur Gil. Effects of Pansharpening Methods on Discrimination of Tropical Crop and Forest Using Very High-Resolution Satellite Imagery. IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Jul 2018, Valencia, Spain. ⟨10.1109/IGARSS.2018.8518243⟩. ⟨hal-01931765⟩

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