Hyperspectral image compression approaches: opportunities, challenges, and future directions: discussion

Area of Science:

• Remote Sensing
• Data Compression
• Image Processing

Background:

• Hyperspectral (HS) images possess unique multi-dimensional structures.
• HS image compression requires specialized coding techniques due to inherent data redundancy.
• Advances in general image compression highlight the need for tailored HS approaches.

Purpose of the Study:

• To review recent advancements in hyperspectral (HS) image compression.
• To summarize current literature on inter-band and intra-band HS compression methods.
• To discuss challenges, opportunities, and future research directions in HS image compression.

Main Methods:

• Literature review of hyperspectral image compression techniques.
• Analysis of inter-band and intra-band data redundancy.
• Summarization of existing coding standards and approaches.

Main Results:

• Identification of key challenges in HS image compression.
• Overview of current research trends and opportunities.
• Experimental validation of existing HS compression techniques.

Conclusions:

• Hyperspectral image compression is a critical research area with significant potential.
• Further research is needed to address the unique challenges of HS data.
• The review provides a foundation for future developments in HS image coding.