ONLINE PREDICTION OF DERIVED REMOTE SENSING IMAGE TIME SERIES: AN AUTONOMOUS MACHINE LEARNING APPROACH

Neural network models are quite popular among the various machine learning approaches for prediction of derived remote sensing image time series. However, the existing models are mostly based on multi-pass parameter learning strategy and these use fixed network architectures which need to be determined through rigorous empirical study. Eventually, their performance deteriorates during online prediction of such data, as commonly encountered in various real-life scenarios. In order to address this issue, this paper proposes OPAL, an online prediction model based on autonomous learning approach. The autonomous learning of OPAL is achieved by employing a self-evolutionary recurrent neural network, whereas its single-pass learning makes it fit for online prediction environment. Experimentation with normalized difference vegetation index (NDVI) data, derived from MODIS Terra satellite imagery, shows that proposed OPAL is able to attain state-of-the-art accuracy even with single-pass learning and without requiring empirical adjustment of network architecture.