Pixelwise remote sensing image classification based on recurrence plot deep features

Dias Danielle, Dias Ulisses, Menini Nathalia, Lamparelli Rubens Augusto Camargo, Le Maire Guerric, Torres Ricardo da S.. 2019. Pixelwise remote sensing image classification based on recurrence plot deep features. In : IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium proceedings. IEEE, GRSS. Yokohama : IEEE-GRSS, 1310-1313. ISBN 978-1-5386-9154-0 Annual IGARSS symposium. 39, Yokohama, Japon, 28 Juillet 2019/2 Août 2019.

Résumé : Pixelwise remote sensing image classification has benefited from temporal contextual information encoded in time series. In this paper, we investigate the use of data-driven features extracted from time series representations based on recurrence plots, with the goal of improving the effectiveness of classification systems. Performed experiments considered the classification of eucalyptus plantations based on time series profiles. Achieved results demonstrate that the combination of recurrence plot representations with deep-learning features are a promising research venue for addressing pixelwise classification problems.

Mots-clés libres : Pixelwise classification, Time series, Recurrence plot, Deep features, Eucalyptus, NDVI index, MODIS

Auteurs et affiliations

• Dias Danielle, UNICAMP (BRA)
• Dias Ulisses, UNICAMP (BRA)
• Menini Nathalia, UNICAMP (BRA)
• Lamparelli Rubens Augusto Camargo, UNICAMP (BRA)
• Le Maire Guerric, CIRAD-PERSYST-UMR Eco&Sols (FRA) ORCID: 0000-0002-5227-958X
• Torres Ricardo da S., UNICAMP (BRA)

Source : Cirad-Agritrop (https://agritrop.cirad.fr/594543/)

Métriques

[ Page générée et mise en cache le 2021-06-04 ]