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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Communication dans un congrès
IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Jul 2018, Valencia, Spain. IEEE, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 〈10.1109/IGARSS.2018.8518243〉
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https://hal.archives-ouvertes.fr/hal-01931765
Contributeur : Enguerran Grandchamp <>
Soumis le : vendredi 23 novembre 2018 - 02:29:50
Dernière modification le : jeudi 28 février 2019 - 01:15:39

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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. IEEE, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium. 〈10.1109/IGARSS.2018.8518243〉. 〈hal-01931765〉

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