Reduced-Complexity Multirate Remote Sensing Data Compression With Neural Networks

被引:2
作者
Verdu, Sebastia Mijares i [1 ]
Chabert, Marie [2 ]
Oberlin, Thomas [3 ]
Serra-Sagrista, Joan [1 ]
机构
[1] Univ Autonoma Barcelona, Dept Informat & Commun Technol, Cerdanyola Del Valles 08193, Spain
[2] Univ Toulouse, IRIT INP ENSEEIHT, F-31071 Toulouse, France
[3] Univ Toulouse, ISAE SUPAERO, F-31055 Toulouse, France
关键词
Image coding; Bit rate; Computer architecture; Remote sensing; Modulation; Transform coding; Complexity theory; Data compression; deep learning; lossy compression; multirate; remote sensing;
D O I
10.1109/LGRS.2023.3325477
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
One of the main limitations to the adoption of deep learning for image compression is the need to train multiple models to compress at multiple rates. In the case of onboard remote sensing data compression, another limitation is the computational cost of the neural networks. Addressing both limitations, this letter presents a new reduced-complexity architecture for multirate compression of remote sensing images. The proposed architecture enables compressing at a precise user-selected rate while keeping a competitive performance in lossy compression on different sets of remote sensing data. The proposed approach is amenable for onboard deployment.
引用
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页数:5
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