Exploring Temporal Context at Multiple Scales for Crop Mapping with Fully Convolutional Recurrent Nets and Fully Connected CRFS.

This paper introduces a novel hybrid N-to-N bidirectional ConvLSTM for multi-temporal crop recognition in areas characterized by highly complex crop dynamics as in tropical regions. The proposed method seeks to explore both spatial and temporal context of the data by adding a bidirectional ConvLSTM to every skip connection in the U-Net and applying the fully connected conditional random fields (CRF) methodology as a post-processing of the predictions. We evaluated our method on a publicly available tropical region dataset, from Sentinel-1 data, obtaining an average improvement of almost 4% in the Average F1 Score and 1.7% in Overall Accuracy, when comparing with a state-of-the art method.