Deep learning-based models for temporal satellite data processing: Classification of paddy transplanted fields

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Deep learning-based frameworks have not been much explored to incorporate the temporal dimension of the remote sensing data. In this research work, deep learning-based models have been developed which exploit the spectral-temporal domain of bi-sensor remote sensing data obtained from Sentinel-2 and Landsat-8 satellites. Application of proposed deep learning frameworks has been tested for mapping transplanted paddy fields using Class Based Sensor Independent - Normalised Difference Vegetation Index (CBSI-NDVI) spectral reduction approach. Two deep learning-based models viz., one-dimensional Convolutional Neural Network (1-D CNN) and hybrid 1-D Convolutional Neural Network – Long Short Term Memory (CNN-LSTM) consisting of 1-D CNN and LSTM layers respectively were developed. For both of the models the optimized model hyper-parameters were also deduced. Though the performance of both the models were above satisfactory level still the 1-D CNN model performed slightly better than the hybrid 1-D CNN-LSTM based model with average overall accuracies of 93.75% and 91.25% respectively. The average F-measure values were computed as 0.93 and 0.90 respectively for the 1-D CNN based and hybrid CNN-LSTM based models. This study indicates that 1-D CNN based deep learning models provide an effective solution to handle mono/bi-sensor temporal remote sensing data of medium spatial resolution with small size training datasets.