MultiF‐RSNet: Distributed interpretation for multi‐platform collaborative in remote sensing

Abstract

Advances in remote sensing technology have significantly improved the on‐orbit processing capabilities of satellites. A distributed collaborative inference approach for multi‐platform remote sensing is introduced. This method leverages the synergy of multiple platforms to enhance inference precision. Initially, a feature compression module is implemented to reduce data transmission demands. Subsequently, a multi‐layer feature fusion module is employed to integrate features from various platforms. Simulation experiments on public datasets confirm that our multi‐platform collaborative object detection algorithm outperforms single‐platform independent inference, validating its effectiveness.