RS-LLIC: A Lightweight Learned Image Compression Model with Knowledge Distillation for Onboard Remote Sensing

With the rapid advancement of remote sensing satellites toward higher spatial resolution and revisit frequency, the explosive growth of image data has posed severe challenges to the efficiency of space-to-ground transmission. Traditional onboard compression standards, such as JPEG2000, often fail to maintain satisfactory reconstruction quality under high compression ratios, limiting their applicability in large-scale remote sensing scenarios. Although learned image compression (LIC) methods have achieved remarkable improvements in rate-distortion (RD) performance, their high computational complexity hinders deployment on resource-constrained onboard platforms. To address these challenges, this article proposes remote sensing-lightweight learned image compression (RS-LLIC), a lightweight LIC framework with knowledge distillation (KD) tailored for onboard remote sensing, following the 'onboard encoding and ground decoding' paradigm. Specifically, an efficient encoder architecture is designed to significantly reduce onboard computational costs, while a knowledge distillation-based training strategy is introduced to guide the lightweight encoder in feature learning using a teacher model, thereby improving RD performance without incurring additional inference overhead. Experimental results on multiple remote sensing datasets demonstrate that the proposed RS-LLIC achieves superior compression performance with extremely low encoder complexity, providing an effective solution for high-quality and efficient onboard remote sensing image compression. The code will be released on https://github.com/dyl96/RS-LLIC.