LAUSR

The problem of missing data in remote sensing analysis is manifold. The situation becomes more serious during multitemporal analysis when data at various a-periodic timestamps are missing. In this work, we have proposed a deep-learning-based framework (Deep-STEP_FE) for reconstructing the missing data to facilitate analysis with remote sensing time series. The idea is to utilize the available data from both earlier and subsequent timestamps, while maintaining the causality constraint in spatiotemporal analysis. The framework is based on an ensemble of multiple forecasting modules, built upon the observed data in the time-series sequence. The coupling between the forecasting modules is accomplished with the help of dummy data, initially predicted using the earlier part of the sequence. Then, the dummy data are progressively improved in an iterative manner so that it can best conform to the next part of the sequence. Each of the forecasting modules in the ensemble is based on Deep-STEP, a variant of the deep stacking network learning approach. The work has been validated using a case study on predicting the missing images in normalized difference vegetation index time series, derived from Landsat-7 TM-5 satellite imagery over two spatial zones in India. Comparative performance analysis demonstrates the effectiveness of the proposed forecasting ensemble.