Task-Oriented Compression Framework for Remote Sensing Satellite Data Transmission

被引：12

作者：

Xiang, Shao ^{[1
]}

Liang, Qiaokang ^{[1
]}

Tang, Peng ^{[2
]}

机构：

[1] Hunan Univ, Coll Elect & Informat Engn, Changsha, Peoples R China

[2] Tech Univ Munich, Dept Informat, D-80333 Munich, Germany

来源：

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | 2024年 / 20卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Image compression; latent feature selection (LFS); massive remote sensing data; region-of-interest (ROI); IMAGE COMPRESSION;

D O I：

10.1109/TII.2023.3309030

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

High-ratio image compression has always been a hotspot for remote sensing satellite image transmission. Especially for a resource-limited environment on board, image compression plays an important role in data storage and transmission. This article proposes a novel method for integrating information extraction network and image compression network into a comprehensive compression framework in order to achieve high-ratio image codec. To reconstruct region-of-interest (ROI) latent representations, we propose a latent feature selection (LFS) module. Some of the channel representations are removed according to the spatial location of the background, but the channel representations of ROI are entirely retained. To effectively validate the performance of our method, we conduct extensive experiments on multiple datasets. The experimental results show that the proposed framework is better at satellite data compression than traditional codecs.

引用

页码：3487 / 3496

页数：10