Enhancing Remote Sensing Image Scene Classification With Satellite-Terrestrial Collaboration and Attention-Aware Transmission Policy

Abstract

Advancements in Earth observation sensors on low Earth orbit (LEO) satellites have significantly increased the volume of remote sensing images. This growth has led to challenges such as higher storage demands, downlink bandwidth stress, and transmission delays, particularly for real-time remote sensing image scene classification (RSISC). To address this, we propose a novel Satellite-Terrestrial Collaborative Scene Classification (STCSC) framework that integrates transmission and computation. The framework employs an attention-aware policy on the satellite, which adaptively determines the sequence of images and selection of image blocks for transmission, as well as these blocks’ sampling rates. This policy is based on image complexity and the real-time data transmission rate, prioritizing blocks crucial for downstream tasks. On the ground, a classification model processes the received image blocks, balancing classification accuracy and transmission delay. Moreover, we have developed a comprehensive simulation system to validate the performance of our framework, including simulations of the satellite, transmission, and ground modules. Simulation results demonstrate that our STCSC framework can reduce transmission delay by 76.6% while enhancing classification accuracy on the ground by 0.6%. Additionally, our attention-aware policy is compatible with any ground classification model.