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SAR Image Compression Based on Multi-Resblock and Global Context | IEEE Journals & Magazine | IEEE Xplore

SAR Image Compression Based on Multi-Resblock and Global Context


Abstract:

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 ...Show More

Abstract:

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.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 20)
Article Sequence Number: 4002105
Date of Publication: 08 February 2023

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I. Introduction

Synthetic aperture radar (SAR) images are becoming more popular because they are less affected by bad weather and clouds. However, the rapid growth of SAR data places a significant burden on its storage and transmission. A more efficient image compression scheme is desperately needed to solve this quandary.

References

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