A Hybrid Model of Bidirectional Long-Short Term Memory and CNN for Multivariate Time Series Classification of Remote Sensing Data

Classification of multivariate time series has got massive attention in the last decade. The traditional modeling classifiers are complicated patterns and are incompetent to capture the dependencies of multivariate time series data. To include both of the effective features and the embedded relationships in the multivariate time series, a new hybrid model which incorporates both Convolutional Neural Network (CNN) and Bidirectional long-short term memory (BiLSTM) named Conv-BiLSTM is proposed in this study. The proposed Conv-BiLSTM is carried out for classifying the land cover multivariate time series of Landsat 8 satellite images. The efficacy of the proposed network is verified through its comparison with the-state-of-the-art methods using different cases of training dataset. The suggested network outperforms the classification techniques as Random Forest (RF), BiLSTM and the CNN and it has classification accuracy on average 6.5, 8 and 8.7% over that of those classifiers respectively. Moreover, the classification accuracy of the proposed Conv-BiLSTM network in F-Score metric is larger than that value of the state-of-the-art WEASEL+MUSE technique in average by 1.38%.