Sentinel-1 Synthetic Aperture Radar (SAR) data has provided an unprecedented opportunity for crop monitoring due to its high revisit frequency and wide spatial coverage. The dual-pol Sentinel-1 SAR data is being utilized for the European Common Agricultural Policy (CAP) as well as for other national projects, which aim to provide Sentinel derived information to support crop monitoring networks. Among the several earth observation products identified for agriculture monitoring, the vegetation status indicator is one of the critical elements that require minimum end-user expertise. In literature, several experiments usually utilize the backscatter intensities to characterize crops. In this work, we jointly use both the scattered and received wave information to derive a new vegetation index (DpRVI) for Sentinel-1 dual-pol (VV-VH) SAR data. The DpRVI is derived using the degree of polarization

No abstract

Feature selection techniques intent to select a subset of features that minimizes redundancy and maximizes relevancy for classification problems in machine learning. Standard methods for feature selection in machine learning seldom take into account the domain knowledge associated with the data. Multi-temporal crop classification studies with full-polarimetric Synthetic Aperture Radar (PolSAR) data ought to consider the changes in the scattering mechanisms with their phenological growth stages. Hence, it is desirable to incorporate these changes while determining a feature subset for classification. In this study, a Random Forest (RF) based feature selection technique is proposed which takes into account the changes in the physical scattering mechanism with crop phenological stages for multitemporal PolSAR classification. The partial probability plot, which is an attribute of RF, provides information about the marginal effect of a polarimetric parameter on the desired crop class. Moreover, it is used to identify the specific range of a parameter where the probability of the presence of a particular crop class is high. The proposed technique identifies features which change significantly with crop phenology. The selected features are the ones whose ranges show maximum separation amongst crop classes. Additionally, the feature subset is refined by eliminating correlated features. The E-SAR L-band dataset of the AgriSAR-2006 campaign over the Demmin test site in Germany is used in this study. The classification accuracy using the novel feature selection technique is 99.12%. This is nominally better than using the features obtained from a standard feature selection method used in RF such as Mean Decrease Gini (98.73%) and Mean Decrease Accuracy (98.68%) which do not take into account the information based on crop phenology.

No abstract

No abstract