Crop Phenology Classification Using a Representation Learning Network from Sentinel-1 SAR Data
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http://hdl.handle.net/10045/100127
Title: | Crop Phenology Classification Using a Representation Learning Network from Sentinel-1 SAR Data |
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Authors: | Dey, Subhadip | Mandal, Dipankar | Kumar, Vineet | Banerjee, Biplab | Lopez-Sanchez, Juan M. | McNairn, Heather | Bhattacharya, Avik |
Research Group/s: | Señales, Sistemas y Telecomunicación |
Center, Department or Service: | Universidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señal | Universidad de Alicante. Instituto Universitario de Investigación Informática |
Keywords: | Wheat | Phenology Classification | Sentinel-1 | Neural network | SMAPVEX16 |
Knowledge Area: | Teoría de la Señal y Comunicaciones |
Date Created: | Jul-2019 |
Issue Date: | Aug-2019 |
Publisher: | IEEE |
Citation: | S. Dey et al., "Crop Phenology Classification Using A Representation Learning Network From Sentinel-1 SAR Data," IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 2019, pp. 7184-7187. doi: 10.1109/IGARSS.2019.8900389 |
Abstract: | This work deals with the classification of wheat phenology by regressing the synthetic aperture radar (SAR) backscatter coefficients (VV, VH) to vegetation water content (VWC) and plant area index (PAI) through a representation learning network. The representation network architecture consists of a pair (VV, VH) of two regression layers (VWC, PAI) which finally converge to a classification (crop phenology) layer. The study was conducted with the Sentinel-1 C-band SAR data acquired during the SMAPVEX16 campaign in Manitoba, Canada. Using this framework, the wheat phenology was classified to an accuracy of 86.67%. However, in comparison, the classification accuracy reduced by ~ 20% while using only the backscatter coefficients of (VV, VH) polarization channels. The results obtained from this study justifies the potential of using a representation learning scheme for crop phenology classification with SAR data. |
Sponsor: | Ministerio de Ciencia, Innovación y Universidades |
URI: | http://hdl.handle.net/10045/100127 |
ISBN: | 978-1-5386-9154-0 |
DOI: | 10.1109/IGARSS.2019.8900389 |
Language: | eng |
Type: | info:eu-repo/semantics/conferenceObject |
Rights: | © 2019 IEEE |
Peer Review: | si |
Publisher version: | https://doi.org/10.1109/IGARSS.2019.8900389 |
Appears in Collections: | INV - SST - Comunicaciones a Congresos, Conferencias, etc. |
Files in This Item:
File | Description | Size | Format | |
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Dey_etal_IGARSS2019_preprint.pdf | Preprint (acceso abierto) | 1,54 MB | Adobe PDF | Open Preview |
Dey_etal_IGARSS2019_final.pdf | Versión final (acceso restringido) | 1,55 MB | Adobe PDF | Open Request a copy |
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