Deep-Learning-Based System for Change Detection Onboard Earth Observation Small Satellites

In recent years, the important evolution in the number, potentiality, and diversity of Earth observation (EO) satellites has resulted in dramatic increases in the payload data volume and rate. However, these exponential increases in the generated data volume are creating a significant bottleneck onboard EO satellites due to transmission bandwidth limits and communication delays. Onboard imaging payload data processing can provide an appropriate solution to alleviate the induced data bottleneck. It can also facilitate rapid response for decision-making operations. Change detection is one of the most significant functions in onboard payload data processing systems that enable a real-time reaction to natural disasters, such as flooding, earthquakes, and volcanic eruptions. In this article, we address the problem of automatic change detection onboard EO satellites. This article aims to design an automatic onboard change detection system (OCDS) that can run on existing flight-proven hardware by taking advantage of the attractive features of a leading model in deep learning (DL) called convolutional neural network. The contribution of this article is twofold. An efficient algorithmic solution for change detection based on DL that fulfills space environment-induced constraints is first proposed. Second, a preliminary hardware architecture of the proposed OCDS is designed based on payload data processing flight-proven hardware. The experimental results demonstrate the efficiency of the proposed DL-based change detection approach and the suitability of the designed OCDS for onboarding on EO small satellites.