| Session Title | WE2.R5: Analysis of Time Series |
|---|---|
| Presentation Mode | Oral |
| Session Time | Wednesday, 31 July, 10:40 - 12:20 |
| Location | Room 315 |
| Session Chairs | Lorenzo Bruzzone, University of Trento and Qian Du, Mississippi State University |
| WE2.R5.1: A SEMI-SUPERVISED CROP-TYPE CLASSIFICATION BASED ON SENTINEL-2 NDVI SATELLITE IMAGE TIME SERIES AND PHENOLOGICAL PARAMETERS |
| Yady Tatiana Solano-Correa; Fondazione Bruno Kessler |
| Francesca Bovolo; Fondazione Bruno Kessler |
| Lorenzo Bruzzone; University of Trento |
| WE2.R5.2: DEEP LEARNING FOR THE CLASSIFICATION OF SENTINEL-2 IMAGE TIME SERIES |
| Charlotte Pelletier; Monash University |
| Geoffrey I Webb; Monash University |
| François Petitjean; Monash University |
| WE2.R5.3: IMPROVING HYPERSPECTRAL IMAGE CLASSIFICATION BY COMBINING SPECTRAL AND MULTIBAND COMPACT TEXTURE FEATURES |
| Khelifa Djerriri; Centre des Techniques Spatiales |
| Abdelmounaime Safia; Centre d’applications et de Recherches en Télédétection (CARTEL) |
| Reda Adjoudj; Djillali Liabes University |
| Moussa Sofiane Karoui; Centre des Techniques Spatiales |
| WE2.R5.4: COMPARING PHENOMETRICS EXTRACTED FROM DENSE LANDSAT-LIKE IMAGE TIME SERIES FOR CROP CLASSIFICATION |
| Hugo Bendini; National Institute for Space Research (INPE) |
| Leila Fonseca; National Institute for Space Research (INPE) |
| Marcel Schwieder; Humboldt-Universität zu Berlin |
| Thales Körting; National Institute for Space Research (INPE) |
| Philippe Rufin; Humboldt-Universität zu Berlin |
| Ieda Sanches; National Institute for Space Research (INPE) |
| Pedro Leitão; Humboldt-Universität zu Berlin |
| Patrick Hostert; Humboldt-Universität zu Berlin |
| WE2.R5.5: DEEP RECURRENT NEURAL NETWORKS FOR LAND-COVER CLASSIFICATION USING SENTINEL-1 INSAR TIME SERIES |
| Shaojia Ge; Nanjing University of Science and Technology |
| Oleg Antropov; VTT Technical Research Centre of Finland |
| Weimin Su; Nanjing University of Science and Technology |
| Hong Gu; Nanjing University of Science and Technology |
| Jaan Praks; Aalto University |