An end-to-end deep learning image compression method for satellite images based on entropy model.

With the extensive applications of satellite image data in environmental monitoring and geographic surveying and mapping, the amount of data has increased rapidly, which brings great challenges for transmitting and storing these images. However, when processing high-resolution and multi-spectral satellite data, existing image compression methods often lead to low compression efficiency or poor reconstruction image quality due to its insufficient generalization ability. In this paper, we propose an end-to-end deep learning image compression framework for visible infrared imaging radiometer suite (VIIRS) satellite imagery. The framework consists of an analysis transform encoder, a synthesis transform decoder, a hybrid training-testing quantizer and a probability model for entropy coding. A cumulative distribution function (CDF) is constructed to compute the discrete likelihood of quantized latent symbols under a Gaussian-mixture entropy model. It is integrated with a checkerboard context structure and a VIIRS-oriented block-processing pipeline. Finally, we conduct systematic experiments based on NASA VIIRS multi-spectral datasets. Experimental results show that the proposed method achieves 0.51 ± 0.04 bpp, 38.39 ± 0.72 dB PSNR and 0.973 ± 0.007 SSIM. Relative to ELIC and the Transformer-CNN baseline, it reduced bpp by 13.6% and 10.5% and improved PSNR by 0.78 dB and 0.61 dB, respectively. The framework can compress the data volume to approximately 1.5%-4% of the original size, corresponding to an average compression ratio of about 30:1. In order to meet the processing requirements of high-resolution satellite images, we further propose a block compression strategy, which divides large-size images into sub-blocks of 256 × 256 pixels for independent compression, and realizes complete image reconstruction through decompression and splicing technology.