A Hyperspectral image is a sequence of image
generated by collecting contiguously spaced spectral bands of
data. It produces a huge amount of three-dimensional digital
data that can be used to recognize objects and to classify
materials on the surface of the earth. Hyperspectral image
compression had received considerable interest in recent
years due to enormous data volumes. In this paper, the
author perspective is to perform a comparative study on
different compression algorithms for hyperspectral imagery.
Remote sensing images are recorded in various wavelength
and spectrum, thus, transmitting them to ground with
efficient compression algorithm is perplexing.
Varsha Ajith : received her B.E (Information
Technology) degree from Guru Ghasidas
University, Bilaspur, Chhattisgarh; 2006.She
is currently working towards Masters in
Computer Science and Engineering from
Dr.B.A.M.University, Aurangabad.Her
research interest focuses on Geographic
Information System and Remote Sensing.
Dilip K.Budhant : is working as Asst.Professor
in Computer Science and Engineering
department. His research interest includes
Networking and Security, Mobile computing
and on Geographic Information System and
Remote Sensing.
Hyperspectral Images
Compression
Wavelet
Transform
PCA
Tucker Decomposition
This paper presents survey on hyperspectral image
compression techniques. It is worth observing that there
are numerous compression algorithms available for
compression of hyperspectral images. Which algorithm
can be considered as the best one for hyperspectral imagery? But based on case study, it is observed that some
are suitable for better compression. It could be
summarized that wavelet transform based provides better
compression and PSNR ratio, for lossy compression of
hyperspectral remote sense images.
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