SAR Image Compression Based on Multi-Resblock and Global Context
Chuan Yun Fu, Bo Du, Liangpei Zhang · IEEE Geoscience and Remote Sensing Letters · 2023
The synthetic aperture radar (SAR) image is widely used in many remote sensing applications. In order to store and transmit the increasing SAR image data, more efficient compression algorithms are needed. The purpose of this letter is to introduce a new framework for compressing SAR images. First, we propose a novel analysis and synthesis transform based on multi-Resblocks for transforming the original SAR image into a compact latent representation. Then, a Gaussian mixture model (GMM) is used to estimate the latent representation’s distribution. In order to explore the redundancy within the latent representation, the entropy model parameter is estimated by combining the local context, global context, and hyperprior information. In order to evaluate the performance of the proposed algorithm, we conduct experiments on a dataset of SAR images. The results show that the proposed algorithm outperforms JPEG2000 and some state-of-the-art learned image compression schemes in terms of compression performance.