Identifying crops in smallholder farms using time series of WorldView-2 images

A high heterogeneity in farming factors (soils, weather, inputs, practice) characterizes the typical smallholder farm landscapes of sub-Saharan Africa. This complicates automatic classification to crop when using only spectral information of very high spatial resolution image time series. This work addresses the crop identification problem in smallholder landscapes through three steps: features extraction, feature selection and classification. Feature extraction is used to exted the spatial-spectral information of the farm fields, with a substantial number of features considered through cloud computing. Feature selection is based on correlation between the features and the labels of the field's crops and it is applied to reduce the dimensionality of the data without lose information. Finally, a random forest classifier is applied to identify a crop class per field. Good preliminary results were obtained reducing the number of features from 1638 to 66. The overall accuracy achieves 80% in the test set using a random forest classifier.