Uplink-Assist Downlink Remote-Sensing Image Compression via Historical Referencing
Huiwen Wang, Liang Liao, Jing Xiao, Weisi Lin, Mi Wang · IEEE Transactions on Geoscience and Remote Sensing · 2023
The traditional strategy of acquiring satellite images involves transmitting compressed satellite data to ground stations solely via the downlink, without utilizing the uplink. In this paper, we propose an enhanced remote sensing (RS) image compression approach that utilizes uplink assistance to improve compression efficiency. By leveraging the uplink, historical images from ground stations can serve as reference images for on-orbit compression, effectively eliminating spatio-temporal redundancy in RS images. However, due to radiation variations among RS images captured on different dates, pixel-wise referencing as employed in the prior codec paradigm is insufficient. To address this, we propose a novel dual-end referencing downsampling-based coding (RefDBC) framework. At the encoder, relevance embedding evaluates reconstructability and records information to restore texture details from the reference prior to downsampling. At the decoder, relevance-based super-resolution uses the identical reference and recorded relevance information to reconstruct the decoded low-resolution image. By incorporating relevance referencing, RefDBC effectively mitigates fake texture generation caused by downsampling and compression, achieving significant bitrate savings ranging from 35%-70% compared to standard, learning-based, and DBC compression baselines in experiments on Spot-5 and Luojia3 images. Code, data, and pretrained models are available online at https://github.com/WHW1233/RefDBC.