The International Journals of Engineering & Sciences (IJENS)
Hyperspectral imaging has been widely studied in many applications; notably in climate changes, vegetation, and desert studies. However, such kind of imaging brings a huge amount of data, which requires transmission, processing, and storage resources for both airborne and space-borne imaging. Compression of hyperspectral data cubes is an effective solution for these problems. Lossless compression of the hyperspectral data usually results in low compression ratio, which may not meet the available resources; on the other hand, lossy compression may give the desired ratio, but with a significant degradation effect on object identification performance of the hyperspectral data.