On the added value of quad-pol data in a multi-temporal crop classification framework based on RADARSAT-2 imagery
Fecha
2016Versión
Acceso abierto / Sarbide irekia
Tipo
Artículo / Artikulua
Versión
Versión publicada / Argitaratu den bertsioa
Impacto
|
10.3390/rs8040335
Resumen
Polarimetric SAR images are a rich data source for crop mapping. However, quad-pol
sensors have some limitations due to their complexity, increased data rate, and reduced coverage
and revisit time. The main objective of this study was to evaluate the added value of quad-pol
data in a multi-temporal crop classification framework based on SAR imagery. With this aim, three
RADARSAT-2 scenes were ...
[++]
Polarimetric SAR images are a rich data source for crop mapping. However, quad-pol
sensors have some limitations due to their complexity, increased data rate, and reduced coverage
and revisit time. The main objective of this study was to evaluate the added value of quad-pol
data in a multi-temporal crop classification framework based on SAR imagery. With this aim, three
RADARSAT-2 scenes were acquired between May and June 2010. Once we analyzed the separability
and the descriptive analysis of the features, an object-based supervised classification was performed
using the Random Forests classification algorithm. Classification results obtained with dual-pol
(VV-VH) data as input were compared to those using quad-pol data in different polarization bases
(linear H-V, circular, and linear 45º), and also to configurations where several polarimetric features
(Pauli and Cloude–Pottier decomposition features and co-pol coherence and phase difference) were
added. Dual-pol data obtained satisfactory results, equal to those obtained with quad-pol data
(in H-V basis) in terms of overall accuracy (0.79) and Kappa values (0.69). Quad-pol data in circular
and linear 45º bases resulted in lower accuracies. The inclusion of polarimetric features, particularly
co-pol coherence and phase difference, resulted in enhanced classification accuracies with an overall
accuracy of 0.86 and Kappa of 0.79 in the best case, when all the polarimetric features were added.
Improvements were also observed in the identification of some particular crops, but major crops like
cereals, rapeseed, and sunflower already achieved a satisfactory accuracy with the VV-VH dual-pol
configuration and obtained only minor improvements. Therefore, it can be concluded that C-band
VV-VH dual-pol data is almost ready to be used operationally for crop mapping as long as at least
three acquisitions in dates reflecting key growth stages representing typical phenology differences of
the present crops are available. In the near future, issues regarding the classification of crops with
small field sizes and heterogeneous cover (i.e., fallow and grasslands) need to be tackled to make this
application fully operational. [--]
Materias
RADARSAT-2,
Polarimetric features,
Separability,
Random forests,
Crop classification
Editor
MDPI
Publicado en
Remote Sensing 2016, 8, 335
Departamento
Universidad Pública de Navarra. Departamento de Proyectos e Ingeniería Rural /
Nafarroako Unibertsitate Publikoa. Landa Ingeniaritza eta Proiektuak Saila