ASSESSING THE CONTRIBUTION OF SPECTRAL AND TEMPORAL FEATURES FOR ANNUAL LAND COVER AND CROP TYPE MAPPING
Keywords: Classification, Mapping, Sentinel-2, Datacubes, Time-Series, Evaluation
Abstract. Freely available satellite image time-series are currently the most exploited data towards land cover mapping. In this work we assess the contribution of spectral and temporal features for the detailed, i.e., with more than thirty classes, land cover and crop type mapping based on annual Sentinel-2 data. As a baseline we employed a datacube consisting of spectral features, i.e., spectral bands and indices from one tile of Sentinel-2A data for the year 2016. Then we formed two different datacubes of reduced dimensions, containing either spectrotemporal or temporal features and performed the same experiments in order to assess their contribution. For the second dataset only spectral features that fulfilled certain temporal conditions were retained, reducing by 40% the initial datacube dimensionality. The third dataset was formed only of temporal features resulting to a reduction of 50%. A random forest classifier was employed for the classification procedure and standard accuracy metrics for the validation. All experiments resulted into high overall accuracy rates of over 90% while rates for average F-score metric exceeded 78% in all cases. The quantitative and qualitative validation indicated that the baseline dataset modestly outperformed the other two of spectrotemporal and temporal features. Insights regarding the influence of spectral differentiation among classes and the impact of their sample size, on the per-class performance are further discussed. The importance of spatial independency for training and testing sets was also demonstrated highlighting the need of following best practises during validation in order to deliver a realistic estimation of the produced map accuracy.