Publications

Das, M (2021). Real-time prediction of spatial raster time series: a context-aware autonomous learning model. JOURNAL OF REAL-TIME IMAGE PROCESSING, 18(5), 1591-1605.

Abstract
Real-time prediction of spatial raster time series, such as those derived from satellite remote sensing imagery, is important for making emergency decisions on various geo-spatial processes/events. However, because of the scalability issue and large training time requirement, the neural network (NN)-based models often fail to perform real-time prediction, in spite of their tremendous potential. In this paper, we propose ContRast, a variant of recurrent NN-based context-aware raster time series prediction model that attempts to resolve these issues by: (1) eliminating the need for offline adjustment of network structure by employing self-evolving autonomous learning of recurrent neural network, (2) saving training time by adopting single-pass parameter learning mechanism, and (3) reducing redundant learning by skipping sub-regional data associated with similar spatio-temporal context and reusing already learned parameters to predict for the same. Experimental evaluations with respect to predicting normalized difference vegetation index (NDVI)-raster derived from MODIS Terra satellite remote sensing imagery show that ContRast is highly effective for real-time prediction of spatial raster time series, and it significantly outperforms the existing models. In addition, the theoretical analyses of model complexity and computational cost further justify our empirical observations.

DOI:
10.1007/s11554-021-01099-7

ISSN:
1861-8200