Back to Search
Start Over
Crop Phenology Classification Using a Representation Learning Network from Sentinel-1 SAR Data
- Source :
- RUA. Repositorio Institucional de la Universidad de Alicante, Universidad de Alicante (UA), IGARSS
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
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. Ministerio de Ciencia, Innovación y Universidades
- Subjects :
- Synthetic aperture radar
Network architecture
Crop phenology
010504 meteorology & atmospheric sciences
Backscatter
Artificial neural network
Phenology
0211 other engineering and technologies
02 engineering and technology
01 natural sciences
Regression
Neural network
SMAPVEX16
Phenology Classification
Wheat
Teoría de la Señal y Comunicaciones
Sentinel-1
Feature learning
021101 geological & geomatics engineering
0105 earth and related environmental sciences
Mathematics
Remote sensing
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- RUA. Repositorio Institucional de la Universidad de Alicante, Universidad de Alicante (UA), IGARSS
- Accession number :
- edsair.doi.dedup.....b91fb16272af4efef0a7fab49f38cc07