Two-Stream Translating LSTM Network for Mangroves Mapping Using Sentinel-2 Multivariate Time Series

Monitoring mangroves is critical to protect the coastal ecosystems, and deep learning has gained great popularity in mapping mangroves using remote sensing. However, mangroves are usually submerged by cyclical tide since they are grown in land–sea interface places, resulting in some drawbacks of existing mangroves mapping models. On one side, the correlations between the vegetation index (VI) and the water index (WI) time series of mangroves are not fully considered. On another side, existing models rarely explored the local differences between mangroves and other land covers. Considering the above two aspects, we propose a novel two-stream translating long short-term memory network (TSTLN) for mangroves mapping. First, we construct multivariate time series (MTS) by compositing VI and WI based on Sentinel-2 time-series data. Second, we build a two-stream architecture and design a Siamese translating (ST) module in both streams. In the global stream, MTS is embedded into the ST module directly to get global features, whereas, in the local stream, a depthwise convolutional self-attention (DCA) module is conceived to capture local information first, and then, local features are further learned by the ST module. Finally, a fully connected layer and softmax are used to classify the representations extracted from the two streams. Experiments conducted over the Maowei Sea, the Dongzhai Port, and the Quanzhou Bay in 2019 demonstrate that: 1) TSTLN outperforms other methods, with improved OA of 0.49%–3.89%, 1.35%–6.85%, and 0.73%–3.65% in the three areas, respectively; 2) two-stream architecture, ST module, and DCA module all contribute to the good performance of TSTLN; and 3) TSTLN maintains higher accuracy with few parameters and less running time compared to other counterparts.