High-resolution image compression algorithms in remote sensing imaging

Digital image processing (DIP) has great application values in many fields, especially in remote sensing image processing, which represents the acquisition, enhancement, analysis, encoding, transmission, and storage of remote sensing images. With the development of chip technology and parallel compu...

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Bibliographic Details
Published in:Displays Vol. 79; p. 102462
Main Author: Ma, Xianghe
Format: Journal Article
Language:English
Published: Elsevier B.V 01.09.2023
Subjects:
Image compression
Remote sensing
Video compression
Video compression
Image compression
Remote sensing
ISSN:0141-9382, 1872-7387
Online Access:Get full text
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Summary:Digital image processing (DIP) has great application values in many fields, especially in remote sensing image processing, which represents the acquisition, enhancement, analysis, encoding, transmission, and storage of remote sensing images. With the development of chip technology and parallel computing technology, various digital image processing technologies have been successfully applied to satellite applications to help researchers exploit reliable information from remote-sensing images. However, the huge amount of images generated by ultra-high resolution optical remote sensing satellites put great pressure on existing transmission, storage, and processing technologies. Therefore, this paper proposes a spatio-temporal compression pipeline for remote sensing images based on lossy compression methods with ultra-high compression ratios to reduce the overhead required for the transmission and storage of remote sensing images while maintaining the quality of the compressed images. The experimental results show that the proposed method outperforms the classical image compression such as JPEG-2000. [Display omitted]
ISSN:0141-9382
1872-7387
DOI:10.1016/j.displa.2023.102462