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A Comprehensive Review on Hyperspectral Image Lossless Compression Algorithms.

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Title: A Comprehensive Review on Hyperspectral Image Lossless Compression Algorithms.
Authors: Liu, Shumin1 (AUTHOR) liu_shumin@ieee.org, Saeed, Fahad1,2 (AUTHOR), Yang, Zhenghui1,2 (AUTHOR), Chen, Jie1,2 (AUTHOR)
Source: Remote Sensing. Dec2025, Vol. 17 Issue 24, p3966. 48p.
Subjects: Lossless data compression, Remote sensing, Hyperspectral imaging systems, Data compression, Deep learning
Abstract: Highlights: What are the main findings? The review provides a focused and systematic analysis of lossless hyperspectral image compression, categorizing existing algorithms into transform-based, prediction-based, and deep learning-based methods. It uniquely emphasizes the second stage of the compression pipeline—scanning and encoding order optimization—an aspect often overlooked in previous reviews but crucial for improving compression efficiency. What is the implication of the main findings? By distinguishing the principles and performance characteristics of different algorithm classes, the review offers a comprehensive framework that helps researchers and practitioners select suitable lossless compression schemes for diverse remote-sensing applications. The analysis highlights future research directions, including the integration of deep learning with reversible transforms and the exploration of adaptive scanning strategies to enhance compression ratio and computational efficiency. The rapid advancement of imaging sensors and optical filters has significantly increased the number of spectral bands captured in hyperspectral images, leading to a substantial rise in data volume. This creates major challenges for data transmission and storage, making hyperspectral image compression a crucial area of research. Compression techniques can be either lossy or lossless, each employing distinct strategies to maximize efficiency. To provide a more focused and comprehensive analysis, this review concentrates exclusively on lossless compression, which is categorized into transform, prediction, and deep learning-based methods. Each category is systematically examined, with particular emphasis on the underlying principles and the strategies adopted to enhance compression performance. In addition to the core algorithms, encoding and scanning orders are also discussed, which is an essential aspect that is often overlooked in other reviews. By integrating these aspects into a unified framework, this paper offers an up-to-date and in-depth overview of the methodologies, trends, and challenges in lossless hyperspectral image compression. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: A Comprehensive Review on Hyperspectral Image Lossless Compression Algorithms.
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  Data: <searchLink fieldCode="JN" term="%22Remote+Sensing%22">Remote Sensing</searchLink>. Dec2025, Vol. 17 Issue 24, p3966. 48p.
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  Label: Abstract
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  Data: Highlights: What are the main findings? The review provides a focused and systematic analysis of lossless hyperspectral image compression, categorizing existing algorithms into transform-based, prediction-based, and deep learning-based methods. It uniquely emphasizes the second stage of the compression pipeline—scanning and encoding order optimization—an aspect often overlooked in previous reviews but crucial for improving compression efficiency. What is the implication of the main findings? By distinguishing the principles and performance characteristics of different algorithm classes, the review offers a comprehensive framework that helps researchers and practitioners select suitable lossless compression schemes for diverse remote-sensing applications. The analysis highlights future research directions, including the integration of deep learning with reversible transforms and the exploration of adaptive scanning strategies to enhance compression ratio and computational efficiency. The rapid advancement of imaging sensors and optical filters has significantly increased the number of spectral bands captured in hyperspectral images, leading to a substantial rise in data volume. This creates major challenges for data transmission and storage, making hyperspectral image compression a crucial area of research. Compression techniques can be either lossy or lossless, each employing distinct strategies to maximize efficiency. To provide a more focused and comprehensive analysis, this review concentrates exclusively on lossless compression, which is categorized into transform, prediction, and deep learning-based methods. Each category is systematically examined, with particular emphasis on the underlying principles and the strategies adopted to enhance compression performance. In addition to the core algorithms, encoding and scanning orders are also discussed, which is an essential aspect that is often overlooked in other reviews. By integrating these aspects into a unified framework, this paper offers an up-to-date and in-depth overview of the methodologies, trends, and challenges in lossless hyperspectral image compression. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Type: general
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      – SubjectFull: Hyperspectral imaging systems
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              Text: Dec2025
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