[A new hyperspectral image compression method combined with subspace partition and multi-inter-spectral prediction].

Area of Science:

• Remote sensing and image processing.
• Data compression techniques.

Context:

• Hyperspectral images offer rich spectral information crucial for applications in military, marine, and agriculture.
• The large data volume of hyperspectral images presents significant compression challenges.
• Prediction-based compression methods are favored for their efficiency and high compression ratios.

Purpose:

• To analyze hyperspectral image features in detail.
• To improve the inter-spectral prediction algorithm for enhanced compression.
• To propose a novel compression scheme combining subspace partition and multi-inter-spectral prediction.

Summary:

• A new hyperspectral image compression method is introduced, integrating subspace partition with multi-inter-spectral prediction.
• This approach builds upon and refines existing prediction-based compression techniques.
• Experimental results demonstrate the effectiveness of the proposed algorithm in compressing hyperspectral data.

Impact:

• Enables more efficient storage and transmission of hyperspectral data.
• Facilitates wider adoption of hyperspectral imaging in various fields by addressing data size limitations.
• Contributes to advancements in lossless and near-lossless compression for high-dimensional imagery.