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Related Experiment Video

Updated: May 16, 2026

Applying Hyperspectral Reflectance Imaging to Investigate the Palettes and the Techniques of Painters

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[Lossless compression of hyperspectral image for space-borne application].

Jin Li¹, Long-xu Jin, Guo-ning Li

¹Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun. 1458312813@qq.com

Guang Pu Xue Yu Guang Pu Fen Xi = Guang Pu

|November 20, 2012

Summary

This study introduces a novel hyperspectral image lossless compression algorithm for space-borne applications, improving hardware implementation and compression ratios. The new method achieves an average compression ratio of 3.05 bpp, outperforming traditional techniques.

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Hyperspectral Imaging as a Tool to Study Optical Anisotropy in Lanthanide-Based Molecular Single Crystals

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Published on: April 14, 2020

Area of Science:

Remote Sensing
Image Processing
Data Compression

Context:

Hyperspectral imaging generates large datasets, posing challenges for storage and transmission in space-borne applications.
Existing lossless compression algorithms often struggle with hardware implementation complexity and efficiency.
Wavelet-based compression schemes can be computationally intensive and difficult to implement on hardware.

Purpose:

To develop a hyperspectral image lossless compression algorithm optimized for space-borne applications.
To address limitations in hardware implementation, compression ratio, and processing time of existing methods.
To enhance the efficiency and practicality of hyperspectral data compression for remote sensing.

Summary:

A novel hyperspectral image lossless compression algorithm is proposed, utilizing intra-band prediction for the first spectral image and a two-step, bidirectional inter-band prediction for subsequent images.
The inter-band prediction incorporates a bidirectional, second-order predictor and an improved Look-Up Table (LUT) prediction algorithm for enhanced prediction accuracy.
Verification experiments using dedicated test equipment demonstrated the algorithm's fast and stable operation, achieving an average compression ratio of 3.05 bits per pixel (bpp).

Impact:

The proposed algorithm significantly improves the average compression ratio by 0.14–2.94 bpp compared to traditional approaches.
It effectively enhances lossless compression performance for hyperspectral imagery.
The method successfully overcomes the hardware implementation challenges associated with complex compression schemes, making it suitable for space-borne systems.