Decentralized Federated GAN for Hyperspectral Change Detection in Edge Computing

Abstract

Change detection on hyperspectral images (HSIs) is an essential task for Earth observation. Due to the vast amounts of remote sensing (RS) Big Data resulting from the ongoing advancements in RS hardware, employing centralized learning through cloud computing emerges as a logical and convenient solution. Nevertheless, this approach overlooks the influence of the isolation and heterogeneity of RS data on the reliability of change detection outcomes. In contrast, federated learning (FL) enables collaborative change detection on nonindependent identical distributed RS data without the need to transfer the original data. Simultaneously, it is important to acknowledge that the dependency of FL on the central node may pose potential data security risks. To address this issue, this article proposes a decentralized FL generative adversarial network (GAN) network. This ensures that raw data remains stationary during participation in unsupervised learning. In addition, the network employs edge devices to implement FL, allowing adaptation to diverse devices in practical scenarios. Lastly, blockchain is integrated into a decentralized architecture for the dynamic selection of leader nodes, effectively enhancing the robustness and security of the framework. Decentralized federated generative adversarial network (DFGAN) is a novel approach for cooperative privacy preservation introduced into hyperspectral change detection systems. The method outperforms most of existing methods on three datasets, achieving more than 90% detection accuracy on all of them. In addition, by simulating in a real on-orbit environment using a satellite constellation simulator on a Jeston TX2, FedGAN demonstrates superior accuracy in HSI change compared to existing algorithms while significantly reducing training time by 17.3%.