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  A Review on Techniques of Hyperspectral Image Compression  
  Authors : Varsha Ajith; Dilip. K. Budhwant
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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.


Published In : IJCSN Journal Volume 5, Issue 1

Date of Publication : February 2016

Pages : 205-209

Figures :01

Tables : 01

Publication Link : A Review on Techniques of Hyperspectral Image Compression




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


Wavelet Transform


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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