Fully convolutional recurrent networks for multidate crop recognition from multitemporal image sequences

Abstract

Abstract Crop recognition in tropical regions is a challenging task because of the highly complex crop dynamics, with multiple crops per year. Nevertheless, most automatic methods proposed thus far are devoted to temperate areas where normally a single crop is cultivated along the crop year. This paper introduces convolutional recurrent networks for crop recognition in areas characterized by complex spatiotemporal dynamics typical of tropical agriculture, where a per date classification is required. The proposed networks consist of two sequential steps. First, a deep network simultaneously models spatial and temporal contexts. Second, a post-processing algorithm enforces prior knowledge about the crop dynamics in the target area based on the posterior probabilities computed in the first step. The paper proposes deep network architectures that join a fully convolutional network (FCN) for modeling spatial context at multiple levels and a bidirectional recurrent neural network to explore the temporal context. The recurrent network is configured as N-to-N, where N is the sequence length. This allows it to produce classification outcomes for the entire sequence of multi-temporal images using a single network. Different network designs are proposed based on three FCN architectures: U-Net, dense network, and Atrous Spatial Pyramid Pooling. A convolutional Long-Short-Term-Memory (ConvLSTM) accounts for sequence modeling, whereas the Most Likely Class Sequence (MLCS) algorithm is adopted for enforcing prior knowledge. The paper finally reports experiments conducted on Sentinel-1 data of two publicly available datasets from different tropical regions. The experimental results indicated that the proposed architectures outperformed state-of-the-art methods based on recurrent networks in terms of Overall Accuracy and per-class F1 score.