Target First, Background Later: Region-Progressive Latent Compression for Satellite Image Transmission

Recent expansion of satellite constellations and onboard sensing brings a rapidly increasing volume of satellite images for earth observation collected onboard. Meanwhile, the satellite downlink can hardly fulfill the huge downloading needs due to the short ground-station contact windows and time-varying link conditions. Although neural image compression approaches achieve strong rate-distortion performance for terrestrial scenarios, they are hard to adopt for satellite image compression for two reasons. First, some approaches require fixed-rate codecs which cannot adapt to dynamic satellite downlink environments. Second, those progressive approaches refine the entire image without considering satellite images' explicit spatial priority. In this paper, our insight is that satellite images usually contain a few small but high-value objects with a large and sparse background. This highly nonuniform information distribution inspires us that the earliest transmitted bits should preserve the region of interest (ROI), whereas background information can be deferred and supplemented using historical observations available at the ground station. Thus, we propose Region-Progressive Latent Compression (RPLC), which partitions a quantized latent into spatially masked channel-slice units and serializes them once as an ROI-first bitstream that is progressively decodable at unit boundaries. At the ground-side decoder, a lightweight latent-completion network predicts unreceived ROI elements without additional coded bits, while an aligned historical observation guides background reconstruction through a spatially gated Reference ControlNet. Image-quality evaluation and downstream ROI classification and segmentation experiments on the fMoW dataset demonstrate that RPLC achieves state-of-the-art ROI performance against 24 baselines at ultra-low bit rates, attaining 66.0% reference-assisted top-1 ROI classification accuracy under a paired-gallery protocol and 81.48% Mask IoU at only 0.0089 bpp.