A Multilayered Adaptive Recurrent Incremental Network Model for Heterogeneity-Aware Prediction of Derived Remote Sensing Image Time Series

Catastrophic forgetting of previously acquired knowledge is a major setback suffered by the neural networks (NNs) when these are trained on tasks in sequential fashion. The NN variants, including the deep network models, as commonly used in remote sensing data prediction are also not free from this limitation. The issue becomes more prominent when the prediction is performed over data collected from spatial zones with a large degree of subregional variations or heterogeneity. In order to tackle this problem, in this article, we propose a multilayered adaptive recurrent incremental network (MARINE) model, offering a spatial heterogeneity-aware self-adaptation scheme that resolves the catastrophic forgetting issue in an autonomous manner. Typically, the proposed MARINE is equipped with an intrinsic mechanism of clustering the spatial subregions as per their heterogeneity levels and auto-constructing the recurrent network layers in an ensemble fashion so that the knowledge acquired about one group of heterogeneity level does not overwrite that acquired about other groups. With respect to spatio-temporal prediction of normalized difference vegetation index (NDVI) time series, as derived from MODIS Terra satellite remote sensing imagery, we demonstrate that our proposed MARINE achieves competitive results when compared with the state-of-the-arts. More significantly, in the presence of higher degree of spatial heterogeneity, MARINE outperforms others by better avoiding the catastrophic forgetting issue.