Distributed Deep Learning With Gradient Compression for Big Remote Sensing Image Interpretation.

Fast and reliable interpretation of high-dimensional hyperspectral images (HSIs) can provide great support to remote sensing-based Earth observations. Targets of interest in HSI can be detected using deep neural networks (DNNs) for background learning on an acquired image where the occurrence probability of background samples is much greater than that of targets, accounting for more than 95% of the whole scene. However, there is an increasing gap between theory and feasible application, because of the contradiction between massive hyperspectral data and resource-limited Internet of Things (IoT)/edge device hardware like satellite.