As part of the G4AW SMARTSeeds project, this study aims to investigate the potential of dense time series of Sentinel-1A dual polarization data for the classification of vegetables that are common in East Java, Indonesia. We first analyzed the temporal behavior of three main types of vegetables (i.e.chili, tomato and cucumber) in terms of backscatter (VH andVV) intensity, and of polarimetric features (i.e. entropy,al-pha and anisotropy) derived from polarization decomposition. We then applied a support vector machine with an intersection kernel to the time series data of vegetable samples collected infield. Our results showed that dense time series Sentinel-1Aimages are of high potential for vegetable classification. Be-sides using backscatter intensity, the polarimetric information can further improve the discrimination between three specific vegetable types.
|Title of host publication||IGARSS 2018 : IEEE International Geoscience and Remote Sensing Symposium : Observing, Understanding and Forecasting the Dynamics of Our Planet|
|Number of pages||4|
|Edition||IEEE Catalog Number: CFP18IGA-ART|
|Publication status||Published - 2018|
|Event||38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018: Observing, Understanding and Forcasting the Dynamics of Our Planet - Feria Valencia Convention & Exhibition Center, Valencia, Spain|
Duration: 22 Jul 2018 → 27 Jul 2018
Conference number: 38
|Conference||38th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018|
|Period||22/07/18 → 27/07/18|
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Potential of multi-temporal SENTINEL-1A dual polarization SAR images for vegetable classification in Indonesia : SMARTSeeds project - ITC part: vegetable classification
Bijker, W. (Creator) & Li, M. (Data Collector), DANS , 14 Nov 2019
DOI: 10.17026/dans-xxt-tqcr, https://www.persistent-identifier.nl/urn:nbn:nl:ui:13-tj-zqiw