Tensor completion for on-board compression of hyperspectral images

Nan Li, Baoxin Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

41 Scopus citations

Abstract

We present a new image compression scheme for hyperspectral images based on the newly-emerged matrix/tensor completion theory. Unlike typical transform-coding based methods, the proposed approach does not require any transform to be performed by the imaging sensor when doing on-board compression. Only a small set of pixels on a sparse set of locations on the imaging sensor needs to be captured and transmitted for each image. The decoder side relies on matrix/tensor completion for reconstructing the original images. Hence the scheme can drastically reduce the computation and bandwidth requirements on the on-board imaging sensors. Experiments show that the proposed method is able to obtain compression performance close to JPEG2000 while enjoying the afore-mentioned unique benefits.

Original languageEnglish (US)
Title of host publication2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings
Pages517-520
Number of pages4
DOIs
StatePublished - Dec 1 2010
Event2010 17th IEEE International Conference on Image Processing, ICIP 2010 - Hong Kong, Hong Kong
Duration: Sep 26 2010Sep 29 2010

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Other

Other2010 17th IEEE International Conference on Image Processing, ICIP 2010
CountryHong Kong
CityHong Kong
Period9/26/109/29/10

Keywords

  • Hyperspectral image compression
  • Matrix completion
  • Tensor completion

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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  • Cite this

    Li, N., & Li, B. (2010). Tensor completion for on-board compression of hyperspectral images. In 2010 IEEE International Conference on Image Processing, ICIP 2010 - Proceedings (pp. 517-520). [5651225] (Proceedings - International Conference on Image Processing, ICIP). https://doi.org/10.1109/ICIP.2010.5651225