@article{MAKHILLJEAS2019141318009,
    title = {Lossy Compression of Hyperspectral Images Using Real-Time Technique},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {14},
    number = {13},
    pages = {4430-4434},
    year = {2019},
    issn = {1816-949x},
    doi = {jeasci.2019.4430.4434},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2019.4430.4434},
    author = {Haitham S. and},
    keywords = {Fractal encoding,prediction,lossy compression,hyper-spectral image,exploit,technique},
    abstract = {Several proposed methods related to Hyper-Spectral (HS) image compression have been published
in the recent years. These methods have often effective compression accuracy but they are time-consuming.
This study introduces the development of a real-time practical scheme for use in lossy HS image compression.
This scheme includes two parts; hardware using the Field Programmable Gate Array (FPGA) system and
software utilizing the band prediction and fractal encoding techniques. The software technique starts by
partitioning the HS image into a number of Groups of Bands (GoBs). Then, the first band in each GoB is utilized
by the intra-band prediction to exploit the spatial correlation. And the other bands in each GoB are employed
by the inter-band fractal coding technique as well as a limited search algorithm to make a complete benefit from
the local matching between any two neighboring bands. This technique shows that the reconstructed image
has a better improvement in the classification accuracy than the primary uncompressed image but still
time-consuming. So, to overcome this problem, the technique is implemented by using the FPGA. This hardware
technique is extremely suitable for real-time purposes.}
    }