Distributed Deep Learning for Remote Sensing Data Interpretation

As a newly emerging technology, deep learning (DL) is very promising field in big data applications. Remote sensing often involves huge volumes obtained daily by numerous in-orbit satellites. This makes it perfect target area for data-driven Nowadays, technological advances terms of software and hardware have noticeable impact on Earth observation applications, more specifically remote techniques procedures, allowing the acquisition sets with greater quality at higher ratios. results collection amounts remotely sensed data, characterized their large spatial resolution (in number pixels per scene), high spectral dimensionality, hundreds or even thousands bands. result, instruments spaceborne airborne platforms are now generating cubes extremely imposing several restrictions both processing runtimes storage capacity. In this article, we provide comprehensive review state art DL interpretation, analyzing strengths weaknesses most widely used literature, as well an exhaustive description parallel distributed implementations (with particular focus those conducted using cloud computing systems). We also quantitative results, offering assessment technique specific case study (source code available: https://github.com/mhaut/cloud-dnn-HSI). article concludes some remarks hints about future challenges application to interpretation problems. emphasize role providing powerful architecture that able manage vast due its implementation simplicity, low cost, efficiency compared other architectures, such grid dedicated clusters.